v wrfn dunhw ricky chia chee jiun academic advisor

General Election and Political Uncertainty in the Malaysia Stock Market:

Evidence from Stock Market Returns and their Volatility

Ricky Chia Chee Jiun

Academic Advisor:

Professor Kobayashi Masahito

This dissertation has been submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy in Economics

Graduate School of International Social Sciences

Yokohama National University

September 2018

ii

Copyright by

Ricky Chia Chee Jiun

2018

iii

ACKNOWLEDGEMENTS

With great pleasure, I would like to express my deep and sincere gratitude to my research

supervisor, Professor Kobayashi Masahito, who gave me his support, encouragement and

advice. Professor Kobayashi Masahito provided me great ideas through discussion, excellent

error checking, and added clear instructions to improve the quality of my research. The

research facilities and equipment provided by him and the faculty are acknowledged. It was a

great privilege and honor to study under Professor Kobayashi Masahito guidance.

Next, I would like to express my heartfelt gratitude to Graduate School of Social Sciences,

Yokohama National University, for giving me the opportunity to further my doctoral study

and providing all the facilities throughout my study. I also extend my special thanks to the

Japanese government, the Ministry of Education, Culture, Sports, Science and Technology

(MEXT) for offer me the Monbukagakusho scholarship throughout my study, which enable

me to focus more clearly and aggressively on my academics rather than my finances. Their

kindness and generosity are greatly appreciated. Besides, I would like to thanks my faculty,

Labuan Faculty of International Finance, Universiti Malaysia Sabah, for allowing my study

leave and supporting me to further my doctoral study in Yokohama National University.

Completion of this doctoral dissertation was not possible without the academic’s support of

the academic committee members (Professor Parsons Craig and Professor Suzuki Masataka),

lecturers, university officers, dissertation reviewers and friends. Their continuous

encouragement patience and forbearance have been valuable for the successful completion of

this research. I would like to express my sincere gratitude to all of them.

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Lastly, I would like to express my love and thanks to my family members, especially my

lovely wife and daughter for their understanding and encouragement in my study.

v

ABSTRACT

This thesis consists of three research papers that thoroughly examine the effect of the

Malaysian general elections on its stock market volatility from the year 1994 to 2015. The

link of the general election and stock market volatility is crucial for market participants. If the

market is subject to investor sentiment during the times of political election, investors who

got the right direction could make profitable trading around general election date. Malaysia

has a unique empirical setting in investigating the impact of political uncertainty on stock

market performance. In term of market behaviour, Malaysia is one of the Asia countries with

higher behavioural risk and higher returns compared to developed financial markets. In term

of political, Malaysia has been long enjoying stable political condition where the incumbent

won all the general elections with a two-thirds majority in the Parliament. However, during

the 12th and 13th Malaysian general elections, the incumbent faced challenges from increasing

pressure for electoral reform and lost the two-thirds majority in parliament which is never

happened in the political history of Malaysia. The presence of a political shock in the 12th and

13th Malaysia general election provides a great opportunity to investigate how investors react

to the market uncertainty. Chapter 1 presents an overview of Malaysian general election

which briefly describes the Parliamentary system in Malaysia and the condition of general

elections held in years 1995-2013.

This study contributes to the literature on a few grounds. First, the election window is

set in an extraordinary way which is in line with the Malaysian electoral process. In particular,

the pre-general election period is defined as the duration from the day of dissolution of the

parliament until the day before voting, while the post-general election period refers to the

duration from the day after voting until the first parliament assembly. Second, this study

examines the election effect in the Malaysian stock market level by level. The examination

vi

starts with the big picture by using seven benchmark indices, including the Shariah-compliant

indices, where each of the benchmark indices represents a different level of market

capitalization. Next, the examination is extended to the sectoral indices of the Malaysian

stock market. By breaking down into industry type, the findings illustrate the sensitivity of

each sector to the market condition during general elections. Lastly, the examination is

conducted at the firm level to complete the understanding of election effect on the stock

market. So far, the election effect at the industry level and firm level remain an unexplored

issue in the literature.

Third, in the asymmetric GARCH models, control variables of MSCI World Index

and MSCI Emerging Market Index are added into both the Exponential Generalized

Autoregressive Conditional Heteroskedasticity (EGARCH) and the Threshold Generalized

Autoregressive Conditional Heteroskedasticity (Threshold GARCH / GJR GARCH) model to

account for external effects. Moreover, this study also conducts an array of robustness checks

by considering the Chicago Board Options Exchange (CBOE) Volatility Index (VIX) as one

of the market uncertainty indicators for global risk, and controlling the US Federal Fund Rate

for interest rate differentials. Fourth, this study contributes to the literature by formally

investigating whether the high volatility is associated with high trading volume as suggested

by Admati and Pfleiderer (1988) or low trading volume as proposed by Foster and

Viswanathan (1990). The trading volume analysis is not included in previous studies in

Malaysia, nonetheless, it reveals an important trading pattern that investors could not miss.

Overall, the findings of this study indicate that the Malaysian stock market volatility

is associated with investors' behaviour during the period of the general elections. Chapter 2

shows the relevance of market capitalization to stock market volatility when there is political

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uncertainty surrounding elections. Companies with small capital experienced higher stock

volatility prior to general election. Conversely, the stock volatility is lower for larger

companies' stock. Furthermore, lower stock volatility is observed in Shariah-compliant stock

indices which suggest that Shariah-compliant companies have a lower risk during the pre-

general election periods. Further examination on sectoral indices is presented in Chapter 3.

The finding in Chapter 3 shows that volatility on the stock return is lower during the pre-

general election periods of 1994-2005, conversely, the stock volatility is higher in the pre-

general election periods of 2006-2015. Thus, the finding sheds light on the importance of

breaking down the full sample period into two sub-samples in order to address the difference

of political condition. Besides, the finding also indicates that the sectors of Construction,

Finance, Mining, and Property are more sensitive to the market condition with significant

result found in stock volatility, while Consumer Product is a defensive sector where the

estimated results are mostly insignificant.

Since evidence of election effect is found on the main stock indices and sectoral

indices, Chapter 4 further explores the reaction of stock returns and volatility in the firm level.

The finding in this chapter also shows that the pattern of the stock volatility in GLCs and

Non-GLCs is clearly different in the two sub-samples, and thus, lends support to the

observation in Chapter 3. As well, lower volatility of returns is found before the general

elections in years 1994-2005, for both the GLCs and Non-GLCs stock indices. In the general

election years of 2006-2015, most of the GLCs and Non-GLCs stock prices were highly

volatile before the general elections. Additionally, analysis on trading volume shows that the

high volatility found in the GLCs and Non-GLCs is associated with high trading volume.

Another interesting point found in the finance sector is that investors are still willing to

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actively trade the GLCs stock despite market uncertainties. Nonetheless, this trading pattern

appears only in the finance sector, but not in other sectors.

This study provides a complete understanding of election effects on the Malaysian

stock market. The market uncertainty induced by the political shock in the recent general

elections has changed the trading pattern in the market. Therefore, this study is of great

importance to market participants to understand the pattern of volatility in the Malaysian

stock market during general election years, and perhaps provide an insight for investors in

adjusting their portfolio around the next general election.

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TABLE OF CONTENTS

Page

Acknowledgements iii

Abstract v

Table of Contents ix

List of Tables xi

List of Figures xiv

List of Appendixes xv

Abbreviations xvi

1 General Introduction

1.1 Political Elections and Financial Markets 1

1.2 Overview of Malaysian General Election 5

1.3 Contributions of the Study 11

1.4 Contents and Organization 13

References 15

2 The Effect of Political Elections on Stock Market Volatility in Malaysia

2.1 Introduction 19

2.2 Literature Review 22

2.3 Data 27

2.4 Empirical Methodology 27

2.5 Empirical Results and Discussions 30

2.6 Conclusion 33

References 35

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3 Stock Market Volatility in Malaysia Sectoral Indices during the General Election

3.1 Introduction 42


3.3 Data and Empirical Methodology 52


3.5 Extensions and Robustness 61

3.6 Conclusion 67

References 71

4 General Election and Stock Market Volatility in Malaysia: Evidence from GLCs and Non-GLCs Stock Performance

4.1 Introduction 101


4.3 Data and Empirical Methodology 113


4.5 Conclusion 128

References 132

5 General Conclusion 164

References 168

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LIST OF TABLES

Pages

Table 1.0: Malaysia General Elections Result from 1995 – 2013 18

Table 2.1: Malaysia General Election 37

Table 2.2: Descriptive Statistics for the Malaysian Stock Indices 37

Table 2.3: Summary Statistics for the Returns on Pre- and Post-General Election 38

Table 2.4: Pre-General Election and Post-General Election: EGARCH Results Controlled by World Market Effect

39

Table 2.5: Pre-General Election and Post-General Election: EGARCH Results Controlled by Emerging Market Effect

40

Table 3.1: Malaysia General Election Information 75

Table 3.2: Descriptive Statistics for the Malaysian Sectoral Indices (1994 - 2015) 75

Table 3.3: Mean Returns on Pre-General Election and Post-General Election 76

Table 3.4(a): Threshold GARCH Results for Pre-General Election and Post-General Election (1994 - 2015) - Controlled by World Market Effect

77

Table 3.4(b): Threshold GARCH Results for Pre-General Election and Post-General Election (1994 - 2015) - Controlled by World Market Effect

78


79


80


81


82

Table 3.7(a): Threshold GARCH Results for Pre-General Election and Post-General Election (1994 - 2015) - Controlled by Emerging Market Effect

83

Table 3.7(b): Threshold GARCH Results for Pre-General Election and Post-General Election (1994 - 2015) - Controlled by Emerging Market Effect

84


85

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86


87


88

Table 4.1: Malaysia General Election 135

Table 4.2: Descriptive Statistics for GLCs Stock Return (1994 – 2015) 136

Table 4.3: Descriptive Statistics for Non-GLCs Stock Return (1994 – 2015) 136

Table 4.4: Sub-Sample Mean Return for GLCs 137

Table 4.5: Sub-Sample Mean Return for Non-GLCs 137

Table 4.6: Selected Sample of Malaysian Government-Link Companies (GLCs) 138

Table 4.7: Selected Sample of Malaysian Non-Government-Link Companies (Non-GLCs)

139

Table 4.8(a): Threshold GARCH Results for Pre-General Election and Post-General Election (1994 - 2015) – Government Link Companies (GLCs)

140

Table 4.8(b): Threshold GARCH Results for Pre-General Election and Post-General Election (1994 - 2015) – Government Link Companies (GLCs)

141

Table 4.9(a): Threshold GARCH Results for Pre-General Election and Post-General Election (1994 - 2015) – Non-Government Link Companies (Non-GLCs)

142

Table 4.9(b): Threshold GARCH Results for Pre-General Election and Post-General Election (1994 - 2015) – Non-Government Link Companies (Non-GLCs)

143


144


145


146


147


148

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149


150


151

Table 4.14(a): Threshold GARCH Results for Trading Volume during Pre- and Post-General Election (1994 - 2005) – Government Link Companies (GLCs)

152

Table 4.14(b): Threshold GARCH Results for Trading Volume during Pre- and Post-General Election (1994 - 2005) – Government Link Companies (GLCs)

153

Table 4.15(a): Threshold GARCH Results for Trading Volume during Pre- and Post-General Election (1994 - 2005) – Non-Government Link Companies (Non-GLCs)

154

Table 4.15(b): Threshold GARCH Results for Trading Volume during Pre- and Post-General Election (1994 - 2005) – Non-Government Link Companies (Non-GLCs)

155


156

Table 4.16(b): Threshold GARCH Results for Trading Volume during Pre- and Post-General Election (2006 - 2015) – Government Link Companies (GLCs)

157

Table 4.17(a): Threshold GARCH Results for Trading Volume during Pre- and Post-General Election (2006 - 2015) – Non-Government Link Companies (Non-GLCs)

158

Table 4.17(b): Threshold GARCH Results for Trading Volume during Pre- and Post-General Election (2006 - 2015) – Non-Government Link Companies (Non-GLCs)

159

xiv

LIST OF FIGURES

Pages

Figure 2.1: Volatility during the Pre-General Election for the Selected Stock Indices

41

Figure 4.1: Relationship between Trading Volume and Stock Price Volatility for GLCs and Non-GLCs from 1994 – 2005

160


161

Figure 4.3: Trading Volume and Stock Prices for the Highly Traded GLCs before the General Elections

162

Figure 4.4: Trading Volume and Stock Prices for the Highly Traded Non-GLCs before the General Elections

163

xv

LIST OF APPENDIXES

Pages

Appendix 2.1: Details of the Selected Indices in this Study 41

Appendix 3.1(a): Threshold GARCH Results for Pre-General Election and Post-General Election (1994 - 2015) - Controlled by Volatility Index (VIX)

89

Appendix 3.1(b): Threshold GARCH Results for Pre-General Election and Post-General Election (1994 - 2015) - Controlled by Volatility Index (VIX)

90


91


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93


94

Appendix 3.4(a): Threshold GARCH Results for Pre-General Election and Post-General Election (1994 - 2015) - Controlled by Federal Fund Rate

95

Appendix 3.4(b): Threshold GARCH Results for Pre-General Election and Post-General Election (1994 - 2015) - Controlled by Federal Fund Rate

96


97


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99


100

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ABBREVIATIONS

ASEAN Association of Southeast Asian Nations

BERSIH The Coalition for Clean and Fair Elections

BN Barisan National

CBOE Chicago Board Options Exchange

DAP Democratic Action Party

EGARCH Exponential Generalized Autoregressive Conditional Heteroskedasticity

GARCH Generalized Autoregressive Conditional Heteroskedasticity

Gerakan Gerakan Rakyat Malaysia

GLCs Government-Linked Companies

KLCI Kuala Lumpur Composite Index

MCA Malaysian Chinese Association

MIC Malaysian Indian Congress

MENA Middle East and North Africa

MSCI Morgan Stanley Capital International

NGOs Non-Governmental Organisations

Non-GLCs Non-Government-Linked Companies

OECD The Organisation for Economic Co-operation and Development

PAS Pan-Malaysian Islamic Party

PBB Parti Pesaka Bumiputera Bersatu

PKR People’s Justice Party

SUPP Sarawak United Peoples' Party

TGARCH Threshold Generalized Autoregressive Conditional Heteroskedasticity

UMNO United Malays National Organization

VIX Volatility Index

1

CHAPTER 1

GENERAL INTRODUCTION

1.1 Political Elections and Financial Markets The history of the stock market suggests that stock index is one of the most sensitive

indicators of business cycle. In the annals of cyclical analysis, the most popular cycle is the

four years Presidential Election Cycle (Wong and McAleer, 2009). The existence of the

Presidential Election Cycle in stock market is simply due to investors' sentiment. In

behavioral finance, investor sentiment is broadly defined as a belief about future cash flows

and investment risks that is not justified by the facts at hand (Baker and Wurgler, 2007).

Specifically, investor sentiment on political election could exhibit optimism or pessimism

which induce underreaction or overreaction in the market. Whenever investors are optimistic

about the future of the economy, they are more inclined to invest in stock market. On the

contrary, whenever investors feel unsecured with the future or policy of the country, they are

more likely to withdraw from the market (Bialkowski, Gottachalk, & Wisniewski, 2008). The

response of investor, thus, causes changes in trading volume, volatility and stock prices in the

market. (Tuyon et al., 2016).

In investment practice, investor sentiment plays an important role on the stock market

activity because stock prices are affected by both the fundamental and behavioural forces

(Akerlof and Shiller, 2009). During the times of political elections, negative news could

destabilize investor trading, thereby creating disarray and possible panic in the markets.

Specifically, in the pre-election periods, election campaign rhetoric may cause bounded

rationality in market players. On the other hand, post-election shock can be caused by several

factors such as a narrow margin of victory, lack of compulsory voting laws, change in the

2

political orientation of the government, or the failure to form a government with

parliamentary majority. Evident from empirical study found support that investors are

affected by sentiment in their investment decision making (Statman, 2008). Furthermore,

Tuyon et al. (2016) also highlighted that investor sentiment risks could influence stock prices

regardless of size and industry groups. Therefore, it is crucial for market participants to know

whether the market is subject to investor sentiment during the times of political election.

Empirical literature on the Presidential Election Cycle dates back to Allivine and

O’Neill (1980). The four years pattern started with a relatively weak performance in the stock

market during the first year of a presidency. Gradually, the stock market's performance

improves in the second and three years. Lastly, the stock market tends to prosper in the

Presidential election year. Nordhaus (1975) explained the causes of the Presidential Election

Cycle through the predictable pattern of the government policies during a term of office. In

order to gain voters support and win the election, the decision made by incumbent political

parties tends to stimulate the economy prior to elections. Hence, political factors are

conjectured to influence the economy through government policies, which affect the timing

and severity of the business conditions.

In developed countries, election effect in the stock market is shown by a number of

studies, among them are Allivine and O’Neill (1980) and Huang (1985), and Gemmill (1992).

Generally, evidence from the U.S. stock markets showed that the market made larger gains in

the third and fourth years of a presidential term. In Britain general election, Gemmill (1992)

also found an extremely close relationship between opinion polls and the FTSE 100 share

index during the 1987 election. Beside that, Pantzalis et al. (2000) conducted a study on an

international scale which covered stock market indices across 33 countries around political

3

election dates during the sample period 1974 - 1995. They found a positive abnormal return

in two weeks prior to the election week.

When the existence of abnormal return is confirmed during the election period,

researchers started to consider the magnitude of market volatility. The study of Białkowski et

al. (2008) aimed to test whether national elections induce higher stock market volatility in a

sample of 27 OECD countries. The finding from the GARCH model indicated that the

country-specific component of index return variance can easily double during the week

around an election. By using a single country testing case, Wang and Lin (2009) also found

that democratically presidential elections negatively impacted stock returns and induced

higher volatility in the Taiwanese stock market. Lean and Yeap (2017) also found similar

pattern in Malaysian stock market where the key index of FTSE Bursa Malaysia KLCI

experienced significant volatility during the general election years.

The evidence found in previous studies is mostly based on the examination of main

composite indices which provided a big picture about the stock market. Nonetheless, stock

market information from the top to the bottom is precious for investor. Sector-specific

information could be useful for investors to narrow down their investments option in the

financial market. Moreover, volatility of the sectoral stock may evolve differently from the

composite indices. The existence of election effect in firm level is equally important. Study

on listed company index could be useful for investors because the individual firm may react

differently to election effect due to the nature of the business industry. However, the aspect of

the influence of political events on the movement of sectoral indices and firm index has not

been thoroughly explored in the literature. Hence, a comprehensive analysis of stock market

4

performance by breaking down into smaller segments should be conducted in order to gain

insight into the sectoral market as well as individual firm index.

Among the emerging markets, Asian markets are interesting in examining the

influence of election on the stock market performance. Although Asia suffers from higher

risk of behavioural biases than other developed markets (Ritter, 2003; Schmeling, 2009), the

markets are still attractive to investors because of their relatively higher returns compared to

developed financial markets (Kearney, 2012). Malaysian stock market is quite a developed

capital market among Asian markets (Mohamad et al. 2007). The Bursa Malaysia has

steadily emerged as one of the top-performing markets in Asia. Its capitalisation has reached

USD 382 billion in December 2015 and the market ranked the second highest in ASEAN

markets after the Singapore Exchange. Moreover, Malaysia has a unique empirical setting in

investigating the impact of political uncertainty on stock market performance. Since the

independence of Malaysia, the country has been enjoying stable political condition where the

incumbent won all the general elections with a two-thirds majority in the Parliament.

However, during the 12th and 13th Malaysian general elections, the incumbent faced

challenges from increasing pressure for electoral reform. Also, in these two general elections,

the incumbent lost the two-thirds majority in parliament which is never happened in the

political history of Malaysia. Without the two-thirds majority in parliament, the incumbent

may face difficulty in amending the constitution in the new cabinet. The unexpected election

outcomes induced an unusual high spike in the stock market as investors were worried about

the danger of unrest and instability.

Hence, the Malaysian stock market is chosen as a single country testing case to see

the influence of political events on the movement of stock prices during the years 1994-2015.

5

The Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH)

model developed by Nelson (1991) and the Threshold Generalized Autoregressive

Conditional Heteroskedasticity (Threshold GARCH / GJR GARCH) model developed by

Glosten et al. (1993) suit the objective of this study in examining the time-varying of stock

volatility.

The remainder of the chapter is organized as follows. The next section presents an

overview of Malaysian general election which briefly describes the Parliamentary system in

Malaysia and the condition of general election held in years 1995-2013. The subsequent

section discusses the contribution of this study. The last section gives a brief description of

each chapter.

1.2 Overview of Malaysian General Election

Malaysia is a country with a Parliamentary system where the Parliament of Malaysia is the

national legislature of Malaysia. The Malaysian electoral system follows the British

parliamentary, as opposed to the US presidential system. The country is geographically

divided into constituencies. Each constituency is contested by candidates who stand either as

nominees of political coalitions or as independents. The elected candidate will become the

Member of Parliament of the constituency and sit in the Lower House of Parliament (the

Dewan Rakyat). As in all parliamentary systems, the leader of the political coalition with a

majority of seats in parliament or a state legislative assembly would be appointed as the

Prime Minister to form the government.

The legislature at the state level is called the State Legislative Assembly. Each of the

13 states has a State Legislative Assembly (Dewan Undangan Negeri), while the federal

6

parliament has two houses, namely the Senate (Dewan Negara) and the House of

Representatives (Dewan Rakyat). The Senate consists of 70 senators of whom 26 are elected

by the state legislative assemblies, with two senators for each state, while the rest are

appointed by the King (Yang di-Pertuan Agong). The Senate, which represents the interests

of the various states, reviews legislation that has been passed by the lower house. The

Parliament, or a State Legislature, has a term of five years, unless dissolved sooner. Thus,

elections are held for the Parliament and the state legislatures at intervals not exceeding five

years (Lim, 2002). When the Parliament is dissolved, a general election shall be held within

two months in West Malaysia and three months for East Malaysia (Sabah and Sarawak) from

the date of dissolution. Within this period, the Election Commission fixes the nomination

day, followed by a formal period of campaigning before the polling day.

Since the first election of the Federal Legislative Council of the Malaya in 1955, the

Alliance Party or Parti Perikatan coalition is the incumbent ruling coalition, and from 1973

onwards, its successor, the Barisan Nasional (BN) coalition. The coalition of BN consists of

more than ten component parties and the three main component parties are racially based

parties. They are the United Malays National Organization (UMNO) represents the Malay

which is the largest ethnic group in Malaysia, the Malaysian Chinese Association (MCA)

represents the Chinese, and the Malaysian Indian Congress (MIC) represents the Indians. The

regional parties from Sabah and Sarawak, namely Sabah Alliance Party, Sarawak United

Peoples' Party (SUPP), and Parti Pesaka Bumiputera Bersatu (PBB) also joined the BN to

form a grand coalition.

Malaysia has an interesting political background for the testing of election effect. It is

therefore essential to begin with a brief summary of the general elections in Malaysia during

7

the sample period from 1995 to 2015. This period covers five general elections, the 9th to the

13th Malaysian general election. Although BN coalition won all the five general elections, the

general elections were accompanied by a certain level of political uncertainty. Table 1.0

summarizes the percentage of votes and seats won by BN and the opposition for the five

general elections.

In early 1990s, Malaysia has a booming economy with nearly zero unemployment,

low inflation and superior organization and finance under the lead of the fourth Prime

Minister, Mahathir Mohamad. In conjunction with the strong economy, the United Malays

National Organisation (UMNO) has grown stronger and united under Mahathir's leadership.

Given the buoyant economy and stable political condition, Malaysia went to the 9th general

election on April 25, 1995. The election was gone through without a hitch and BN recorded

the greatest victory in the election history. The BN won 162 of the 192 parliamentary seats

and secured an the highest percentage of votes of 65.2%. The victory of BN coalition

demonstrated the strengths of Mahathir's administration and the economic boom. The BN

coalition successfully gained support not only from the rural Malay voters, but most

importantly from the Chinese in urban centres. The 9th general elections provided a clear

mandate for the BN coalition to form the government with multiethnic support from urban as

well as rural constituencies (Moten and Mokhtar, 1995).

Two years after the 1997 Asian financial crisis, the 10th general election held on

November 29, 1999, witnessed the tremendous change of voters' behaviour due to dramatic

political and economic changes in Malaysia. The two important events that influenced the

election was the dismissal of Anwar Ibrahim and the 1997 financial crisis. The issue of

Anwar Ibrahim dominated the election campaigns and manipulated to gain voters support.

8

The 1997 financial crisis also caused Malaysia in an unstable political climate. The opposing

views in handling the crisis had struggled both the leaders to continue leading the country

together. Mahathir bravely strategized the economy against the norm in order to protect

Malaysia. Its effect was clearly reflected in the 10th general election when the BN coalition

managed to secure a two-third majority in parliament but with fewer majority seats (Lin,

1999). This election was mostly a two-bloc antagonism. The ruling BN coalition obtained

56.5% of the votes with a drop of 8.7% from the previous general election. The opposition

gained 43.5% of the votes, mostly contributed by the Pan-Malaysian Islamic Party (PAS).

. In year 2004, the Prime Minister, Abdullah Badawi, led the BN coalition to contest in

the 11th general election. This election was the first election led by Abdullah Badawi.

Abdullah had come into office with a distinctly different political persona with the previous

prime minister. His Islamic credentials gained him much mileage among the Muslim voters. He

also gained public support by the promise of an anticorruption platform. The rural Malay

electorate also supported Abdullah administration as he put much closer attention to

agriculture and rural development. and a moderate, progressive version of Islam (Liow,

2004). Given the Abdullah factor, the 11th general election held on March 21, 2004 was the

greatest electoral victory for the ruling BN in the history of Malaysian electoral politics. The

BN coalition garnered a total of 199 of the 219 seats (91%) and limited the opposition to 20

seats. The opposition PAS leader, Abdul Hadi Awang, lost his parliamentary seat in his own

state of Terengganu. The failure of the PAS coalition was largely attributed to the party’s

inability to convince the moderate Muslim and non-Muslim electorate. The non-religious

Democratic Action Party (DAP) was the only winner among the opposition parties by

securing 12 seats in the Parliament.

9

The 12th general election in Malaysia was the second election held under the Prime

Minister, Abdullah Ahmad Badawi. The nomination day is set by the Election Commission

on February 24, 2008, and the general election is set on March 8, 2008. The campaign period

of 13 days was the longest in Malaysian electoral history since 1969. The opposition parties,

DAP, PAS and the People’s Justice Party (PKR), formed an alliance to contest against BN.

The election was conducted with a low expectation for a repeat of the fruitful result of the

11th general election. The environment was surrounded by worries over rising oil prices in the

global market, the increase in consumer price index, perceptions of ethnic inequality

especially among the Indian ethnic group, concerns over the independence of the judiciary,

and a revived opposition under the leadership of former Deputy Prime Minister, Anwar

Ibrahim. Besides, electoral reform has been demanded and officially started with the forming

of The Coalition for Clean and Fair Elections or BERSIH by non-governmental organisations

(NGOs) in November 2006. The first ever mass rally called by BERSIH happened before the

12th general election, and ended with detention of a group of more than two hundred people.

The 12th general election were held on March 8, 2008 and the outcome was a great

shock to BN coalition. It is quoted as ‘Political Tsunami’ because the result was the worst

performance ever for BN coalition. BN won 140 of the 222 seats in the federal parliament,

where 55 of the 57 seats in East Malaysia, and 85 of the 165 seats in the peninsula.

Obviously, the BN’s margin of victory was helped by its performance in East Malaysia.

Overall, BN won just 51.4% of the votes and 63% of parliamentary seats. It was the first time

the BN lost the two-thirds majority in parliament which might affect the new cabinet in

amending the constitution in future. The failure to secure a two-thirds majority was partly due

to the poor performance of BN’s non-Malay components, the Malaysian Indian Congress

(MIC), Malaysian Chinese Association (MCA), and Gerakan Rakyat Malaysia (Malaysian

10

People Movement, Gerakan), in securing the votes of the non-Malays in the peninsula. The

electoral outcomes also imply that BN has significantly lost support among the non-Malay

constituencies, due in large part of its failure to address the economic factors such as rising

fuel and consumer prices and the issue of ethnic inequality among the Chinese and Indian

ethnic minorities (Mokhtar, 2008)

The 13th general election in 2013 is quoted as the most tensely contested general

election in the political history of Malaysia. The complexity of the 13th general election

begun as soon as the 12th general election was over. The gains of the PR coalition in the 12th

general election raised worries to the BN coalition, about the strength of PR and the ability of

BN to retain power in the 13th general election, and the changes on the political system if the

election result were to be a very close between the two coalitions. Moreover, in the five years

period, the most spectacular event were the mass demonstrations in 2011 and 2012 called by

BERSIH 2.0 and BERSIH 3.0 (Coalition for Clean and Fair Elections). The demonstrations

were joined by small political coalitions, non-governmental organizations, and mainly

supported by the opposition coalitions. The demonstrations demanded for a free and fair

electoral process and also called for voters to show up in large numbers to negate illegal

voting by non-citizens.

On May 5, 2013, Malaysia went to the 13th general election with the highest record of

voter turnout in the history of Malaysia. The 13th general election was the first election held

under the Prime Minister, Najib Razak. Despite the uncertainty, BN won against the

opposition and formed the government at the federal level. BN won 133 of the 222 seats

while the opposition won 89 seats. This was the best performance shown by the opposition

coalition, but the worst for BN coalition with only 47.4% of the popular vote. The result of

11

the 13th general election altered the trends of earlier general elections. The success of the

opposition coalition contributed to the progress towards two-coalition system in Malaysia

(Khoo, 2013).

1.3 Contributions of the Study

The examination of asymmetric effect in the Malaysian stock market around elections

contributes to the literature on a few grounds. First, the election window is precisely set in

accord with the Malaysian electoral process in order to capture the full impact of the general

election. The selection of the election window in this study is different with previous studies

that focused on the event day (Wang and Lin, 2009) or fix event windows before and after the

election, for example, 1 week, 2 weeks and 1 month (Nippani and Arize, 2005; Chuang and

Wang, 2010; Lean and Yeap, 2017). In this study, the pre-general election period is defined

as the duration from the day of dissolution of the parliament until the day before voting,

while the post-general election period refers to the duration from the day after voting until the

first parliament assembly. Furthermore, to test whether the pattern of the stock volatility

changes according to the political condition, the full sample period is divided into early years

(1994-2005), and later years (2006-2015). The year 2006 has been chosen as the cut-off date

because the 12th general election (2008) and the 13th general election (2013) induced election

uncertainty with a strong expectation of political changeover and eventually the incumbent

lost its two-thirds majority in the Parliament. Thus, the first sub-sample period covers the

general election years of 1995, 1999 and 2005 where general ups and downs happened in the

market during general elections, while the second sub-sample covers the general election

years of 2008 and 2013 where the market experiences drastic ups and downs.

12

Second, this study examines the election effect in the Malaysian stock market by

using the top-down approach. The examination starts with the big picture by using seven

benchmark indices, including the Shariah-compliant indices. Each of the benchmark indices

represents a different level of market capitalization and the findings show the pattern of stock

movement during election periods in each level of market capitalization. The selection of data

provides a clearer picture than previous studies (Lean, 2010; Lean & Yeap, 2017) that only

focused on the FTSE KLCI index. Next, the examination breaks down into smaller segments

by investigating the election effect on ten sectoral indices of the Malaysian stock market. By

breaking down into industry type, the findings illustrate the sensitivity of each sector to the

market condition during general elections. Lastly, the examination is reduced to the base

elements by conducting analysis on the firm level to complete the understanding of election

effect on the stock market. So far, this remains an unexplored issue in the literature.

Therefore, this study attempts to uncover evidence of election effect in firm-level by selecting

GLC firms and non-GLC firms as the sample.

Third, in the asymmetric GARCH models, control variables are added into both the

Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) and the

Threshold Generalized Autoregressive Conditional Heteroskedasticity (Threshold GARCH /

GJR GARCH) model to account for external effects. Specifically, the MSCI World Index is

to capture the global market effect to Malaysian stock market, and the MSCI Emerging

Market Index are included to control for emerging market effect. Moreover, this study also

conducts an array of robustness checks by considering the Chicago Board Options Exchange

(CBOE) Volatility Index (VIX) as one of the market uncertainty indicator for global risk, and

controlling the US Federal Fund Rate for interest rate differentials.

13

High volatility in stock returns is always a concern for market participants. Fourth,

this study contributes to the literature by formally investigating whether the high volatility is

associated with high trading volume as suggested by Admati and Pfleiderer (1988) or low

trading volume as proposed by Foster and Viswanathan (1990). The trading volume analysis

is not included in previous studies in Malaysia, nonetheless, it reveals an important trading

pattern that investors could not miss.

Overall, this study may be of interest to investors as the results will reveal the

Malaysian stock market information, from the top level of benchmark indices to the middle

level of sectoral indices, and further to the base level of firm indices. This precious

information is useful for investors to construct an effective equity portfolio investment,

especially during the times of election.

1.4 Contents and Organization

The rest of the thesis is organized as follows:

Chapter 2 examines the election effect on FTSE Bursa Malaysia KLCI Index and

selected main stock indices in the Malaysian stock market that represent large, medium, and

small market capitalization, including the Shariah-compliant indices. The scope of the

examination only covers the 12th and 13th general election, which are the most two recent

general elections with turbulent political condition. This chapter shows the relevance of

market capitalization to stock market volatility when there is political uncertainty

surrounding elections.

Chapter 3 attempts to identify the influence of general elections on the movement of

ten selected sectoral indices in the Malaysian stock market. Further examination on sector-

14

specific information is useful for investors to narrow down their investments option. This

chapter sheds light on the importance of addressing the difference of political condition when

testing for asymmetry effect during election periods.

Since evidence of election effect is found on the main stock indices and sectoral

indices, Chapter 4 further explores the reaction of stock returns and volatility in the firm

level. Additionally, analysis on trading volume is performed to see whether the observed high

volatilities are associated with low or high trading volume. This chapter highlights that the

high volatility found in the GLCs and Non-GLCs is associated with high trading volume,

which lend support to the argument of Admati and Pfleiderer (1988).

Finally, Chapter 5 provides a general conclusion on the election effect found in the

Malaysian stock market.

15

References

Admati, A. and Pfleiderer, P. (1988). “A theory of intraday patterns: Volume and price variability”, Review of Financial Studies, Vol. 1, pp. 3 – 40. Akerlof, G. A. and Shiller, R. J. (2009). “Animal spirits: How human psychology drives the economy, and why it matters for global capitalism”, New Jersey: Princeton University Press. Allivine, F. D. and O’Neil, D. D. (1980). “Stock Market Returns and the Presidential Election Cycle”, Financial Analysts Journal, Vol. 36, No. 5, pp. 49 - 56. Baker, M. and Wurgler, J. (2007). “Investor Sentiment in the Stock Market”, Journal of Economic Perspectives, Vol. 21, No. 2, pp. 129 – 151. Bialkowski, J., Gottachalk, K. and Wisniewski, T. P. (2008). “Stock Market Volatility around National Elections”, Journal of Banking and Finance, Vol. 32, No. 9, pp. 1941 - 1953. Chuang, C. and Wang, Y. (2010). “Electoral Information in Develop Stock Market: Testing Conditional Heteroscedasticity in the Market Model”, Applied Economics, Vol. 42, No. 9, pp. 1125-1131. Foster, F. D. and Viswanathan, S. (1990). “A theory of the interday variations in volume, variances, and trading cost in securities market”, Review of Financial Studies, Vol. 3, pp. 593 – 624. Gemmill, G. (1992). “Political Risk and Market Efficiency: Tests Based in British Stock and Options Markets in the 1987 Election”, Journal of Banking and Finance, Vol. 16, No. 1, pp. 211 - 231. Glosten, L. R., Jagannathan, R. and Runkle, D. E. (1993). “Relationship between the expected value and the volatility of the nominal excess return on stocks”, The Journal of Finance, Vol. 48, No. 5, pp. 1779 - 1801. Huang, R. D. (1985). “Common Stock Returns and Presidential Elections”, Financial Analysts Journal, Vol. 41, No. 2, pp. 58 - 65. Kearney, C. (2012). “Emerging markets research: Trends, issues and future directions”, Emerging Markets Review, Vol. 13, pp. 159 - 183. Khoo, B. T. (2013). “13th General Election in Malaysia: Overview and Summary”, in Khoo, B. T. (Ed.), 13th General Election in Malaysia: Issues, Outcomes and Implications, Institute of Developing Economies (IDE-JETRO) Interim Report 2013, pp. 1 – 8. Retrieved from http://www.ide.go.jp/English/Publish/Download/Report/2013/2013_malaysia.html. Lean, H. H. (2010). “Political General Election and Stock Performance: The Malaysian Evidence”, in Ismail, M. T. and Mustafa, A. (Ed.), Research in Mathematics and Economics, Penang: Universiti Sains Malaysia, Malaysia, pp. 111 - 120. Lean, H. H. and Yeap, G. P. (2017). “Asymmetric Effect of Political Elections on Stock

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Returns and Volatility in Malaysia”, in Munir, Q. and Kok, S. C. (Ed.), Information Efficiency and Anomalies in Asian Equity Markets, Routledge, Taylor and Francis Group, pp. 228 - 245. Lim, H. H. (2002). “Electoral Politics in Malaysia: ‘Managing’ Elections in a Plural Society”, in Croissant, A. et al. (Ed.), Electoral Politics in Southeast and East Asia, Friedrich Ebert Stiftung, Singapore, pp. 101 – 148. Lin, J. Y. (2002). “A Structural Analysis of the 1999 Malaysian General Election: Changing Voting Preference of Ethnic Chinese and Malay Groups and Party”, Taipei: Tamkang Journal of International Affairs, Vol. 6, No. 3. Retrieved from https://www.brookings.edu/articles/a-structural-analysis-of-the-1999-malaysian-general-election-changing-voting-preference-of-ethnic-chinese-and-malay-groups-and-party/. Liow, J. C. Y. (2004). “A Brief Analysis of Malaysia’s Eleventh General Election”, UNISCI Discussion Papers. Retrieved from https://www.ucm.es/data/cont/media/www/pag-72535/Liow.pdf. Mohamad, S., Hassan T. and Ariff, M. (2007). “Research in an emerging Malaysian capital market: A guide to future direction”, International Journal of Economics and Management, Vol. 1, No. 2, pp. 173 – 202. Mokhtar, T. M. (2008). “The Twelfth General Elections in Malaysia”, Intellectual Discourse, Vol. 16, No. 1, pp. 89 – 100. Moten, A. R. and Mokhtar, T. M. (1995). “The 1995 Parliamentary Elections in Malaysia”, Intellectual Discourse, Vol. 3, No. 1, pp. 77 – 93. Nelson, D. (1991). “Conditional Heteroskedasticity in Asset Returns: A New Approach”, Econometrica, Vol. 59, pp. 347-370. Nippani, S. and Arize, A. C. (2005). “U.S. Presidential Election Impact on Canadian and Mexican Stock Markets”, Journal of Economics and Finance, Vol. 29, No. 2, pp. 271 - 279. Nordhaus, W. (1975). “The Political Business Cycle”, Review of Economic Studies, Vol. 42, No. 2, pp. 169 - 190. Pantzalis, C., Stangeland, D. A. and Turtle, H. J. (2000). “Political Elections and the Resolution of Uncertainty: The International Evidence”, Journal of Banking and Finance, Vol. 24, pp. 1575 – 1604. Ritter, J. R. (2003). “Behavioral finance”, Pacific-Basin Finance Journal, Vol. 11, pp. 429 - 437. Schmeling, M. (2009). “Investor sentiment and stock returns: Some international evidence”, Journal of Empirical Finance, Vol. 16, No. 3, pp. 394 - 408. Statman, M., Fisher, K. L. and Anginer, D. (2008). “Affect in a behavioural asset-pricing model”, Financial Analysts Journal, Vol. 64, No. 2, pp. 20 - 29.

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Tuyon, J., Ahamd, Z. and Matahir, H. (2016). “The role of investor sentiment in malaysian stock market”, Asian Academy of Management Journal of Accounting and Finance, Vol. 12 Suppl. 1, pp. 43 - 75. Wang, Y. H. and Lin, C. T. (2009). “The Political Uncertainty and Stock Market Behavior in Emerging Democracy: The Case of Taiwan”, Quality and Quantity, Vol. 43, No. 2, pp. 237 - 248. Wong, W. K. and McAleer, M. (2009). “Mapping the Presidential Election Cycle in U.S. Stock Market”, Mathematics and Computers in Simulation, Vol. 79, No. 11, pp. 3267 - 3277.

18

Table 1.0: Malaysia General Elections Result from 1995 – 2013

Election Year

Alliance / Barisan Nasional / National Front All Opposition Parties Total No. of Seats

Contested

No. of seat won % Total Vote

% Seats

No. of seat won % Total Vote

% Seats UMNO MCA MIC Others Total

Seat PAS PKR DAP Others Total Seat

1995 89 30 7 36 162 65.2 84 7 n.a. 9 14 30 34.8 16 192 1999 71 29 7 41 148 56.5 77 27 5 10 3 45 43.5 23 193 2004 109 31 9 49 198 63.8 91 7 1 12 1 21* 36.2 9 219 2008 79 15 3 43 140 51.4 63 23 31 28 0 82 48.6 37 222 2013 88 7 4 34 133 47.4 60 21 30 38 0 89 50.8 40 222

Note: *Figure includes one independent candidate. UMNO: United Malays National Organisation, MCA: Malaysian Chinese Organisation, MIC: Malaysian Indian Congress, PAS: Islamic Party of Malaysia, PKR: People’s Justice Party, and DAP: Democratic Action Party. Sources: Suruhanjaya Pilihan Raya, Election Report, various years.

19

CHAPTER 2 THE EFFECT OF POLITICAL ELECTIONS ON STOCK MARKET VOLATILITY

IN MALAYSIA 2.1 Introduction

History suggests that the stock market has a role as one of the most sensitive indicators of the

business cycle and the most popular cycle is the four years Presidential Election Cycle (Wong

and McAleer, 2009). The four years pattern started with a relatively weak performance in the

stock market during the first year of a presidency. Gradually, the stock market's performance

improves in the second and three years. Lastly, the stock market tends to prosper in the

Presidential election year. Nordhaus (1975) explained the causes of the Presidential Election

Cycle through the predictable pattern of the government policies during a term of office. In

order to gain voters support and win the election, the decision made by incumbent political

parties tends to stimulate the economy prior to elections. Hence, political factors are

conjectured to influence the economy through government policies, which affect the timing

and severity of the business conditions.

Moreover, investors' expectation also explains the political election effect on stock

market performance (Bialkowski et al., 2008). Whenever investors are optimistic about the

future of the economy, they are more inclined to invest in stock market. On the contrary,

whenever investors feel unsecured with the future or policy of the country, they are more

likely to withdraw from the market. Specifically, in the pre-election periods, general public

and investors may be affected by the election campaign rhetoric and the promises made by

the candidates, which can cause dramatic changes in stock prices. On the other hand, post-

election shock can be caused by several factors such as a narrow margin of victory, lack of

compulsory voting laws, change in the political orientation of the government, or the failure

20

to form a government with parliamentary majority significantly. Hence, investors’ sentiment

around election could induce under-reaction or over-reaction in the market and consequently

influence changes in trading volume, volatility, prices and accordingly determine stock

returns (Tuyon et al., 2016).

In the literature, there has been a constant stream of work analyzing the impact of the

political factor on stock market performance and the empirical evidence suggests that stock

market is significantly affected by the election. In developed countries, election effect in the

stock market is shown by number of studies, among them are Peel and Pope (1983), Gemmill

(1992), Lobo (1999), Nippani and Arize (2005), and Wong and McAleer (2009). However,

not many studies have been done in the emerging market, except for Wang and Lin (2009) on

Taiwanese stock market, Lean (2010) and Lean and Yeap (2017) on Malaysian stock market.

Actually, emerging market like Asian market is an interesting case study to investigate the

influence of election on the stock market performance. Asian are more socially collective in

decision-making (Kim and Nofsinger, 2008), collectively, political events such as national

election could cause investors to react irrationally.

Among the Asian countries, Malaysia has a unique empirical setting in investigating the

impact of political uncertainty on stock market performance. Since the independence of

Malaysia, the country has been enjoying stable political condition where the incumbent won

all the general elections with a two-thirds majority in the Parliament. However, during the

12th and 13th Malaysian general elections, the incumbent faced challenges from increasing

pressure for electoral reform. Also, in these two general elections, the incumbent lost the two-

thirds majority in parliament which is never happened in the political history of Malaysia.

Without the two-thirds majority in parliament, the incumbent may face difficulty in amending

21

the constitution in the new cabinet. The unexpected election outcomes induced an unusual

high spike in the stock market as investors were worried about the danger of unrest and

instability. Nonetheless, the two exogenous events provide a great opportunity to investigate

the impact of general elections on the stock market in Malaysia.

Hence, the Malaysian stock market is chosen as a single country testing case to see the

influence of political events on the movement of stock prices. From the perspective of

statistical analysis, selection of a single country is preferred to eliminate the heterogeneous

effect of multiple country characteristics such as differences in economics, political,

institutional, demographics and culture (Bekaert and Harvey, 2002; Statman et al., 2008).

Moreover, when performing standard time series analysis, breaking the series into similar

condition is recommended to avoid erroneous inferences. Therefore, this study only focuses

on the drastic shock periods during the 12th and 13th general elections because the market

condition during these two general elections is clearly different with earlier general elections.

The aim of this study is to examine the asymmetric effect of Malaysian general elections held

in the year 2008 and 2013 on the performance of Malaysian stock market. The Exponential

Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model developed by

Nelson (1991) suits the objective of this study in examining the time-varying of stock

volatility. Furthermore, the data is segregated into pre-general election period and post-

general election period. The pre-general election period starts from the day of dissolution of

the Parliament until the day before voting, while the post-general election period starts from

the day after voting until the first Parliament assembly. The selected time frames include all

important events before and after the general election which possibly affect the investors'

confidence in making their investment decision.

22

The examination of stock volatility around elections in this paper contributes to the

literature on a few grounds. First, the election window in this study is designed in accord with

the Malaysian electoral process, which is different with previous studies that focused on the

event day (Wang and Lin, 2009) or fix event windows (Nippani and Arize, 2005; Chuang and

Wang, 2010; Lean and Yeap, 2017). The pre-general election and post-general election

sample period are set according to the important date of the election in order to capture the

full impact of the general election. Second, this study examines seven benchmark indices in

Malaysia, including Shariah-compliant indices, to determine the impact of the election on

stock indices in different market capitalization. This approach enables us to relate the stock

volatility with market capitalization during the general election. Third, the MSCI World

Index and MSCI Emerging Market Index are included to control for global and emerging

market effect. This study has significant implication for investors as the findings can be of

interest to adjust their portfolio during the general election.

This paper is organized as follows. The next section presents the review of the impact

of political events on stock markets. Section 2.2 presents the data and Section 2.3 presents the

methodology and preliminary analysis. Next, Section 2.4 reports the estimated results and

Section 2.5 provides a brief conclusion.

2.2 Literature Review

The pattern in stock market prices related to the four years of Presidential term has been the

focus of study for some time. The empirical study of stock market behaviour and the US

elections was initiated by Niederhoffer et al. (1970). Further studies by Allivine and O’Neill

(1980) and Huang (1985) also supported the theory of presidential election cycle, in which

23

the US stock market has a weak performance in the first year of a presidential term and then

turn to a larger gain in the third and fourth years. On the other hand, the study by Jones and

Banning (2009) did not support the theory. They found little relationship between stock

market performance and US elections and the market returns do not appear to vary based on

partisan control of the government from 1896 through 2000. Nevertheless, recent study by

Wong and McAleer (2009) applying spectral analysis reconfirmed that US stock prices

closely followed the four-year presidential election cycle and the cyclical trend existed for the

last ten administrations from 1965 through 2003, particularly when the incumbent is a

Republican.

A notable study by Leblang and Mukherjee (2005) provided a deep understanding on

how political information affects market participants. By examining the US and British

equity markets between 1930 and 2000, they found statistical supports for their model which

predict that rational expectations of higher (lower) inflation under left-wing (right-wing)

administrations leads to lower (higher) trading volume of stocks, in turn, leads to a decrease

(increase) in the mean and volatility of stock prices. The finding also showed that the model

not only applicable on government partisanship, but also traders’ expectations of electoral

victory. Thus, their study explained the sensitivity of stock prices to elections and partisan

politics.

Beside the Presidential election cycle in the US, other studies have been done in other

countries to show abnormal return around election dates. The study of Pantzalis et al. (2000)

covered stock indices across 33 countries including major OECD countries of the US, UK,

and emerging countries. Their analysis showed that politics does matter in emerging markets

by comparing the mean and median around election with a comparison period mean.

24

Abnormal returns were detected during the two-week period prior to the election week.

Furthermore, there is also evidence that one-time occurrence of political event impacted

financial markets. Studies conducted by Nippani and Medlin (2002) and Nippani and Arize

(2005) examined the impact of the delay in the 2000 presidential election results on stock

market performance. Conventional heteroskedastic t-tests and binary variable regression were

applied to show that the delay had negatively impacted stock prices. The negative impact not

only appeared in the US stock market, but also the Mexican and Canadian stock markets. The

findings also implied that elections are wide watched events by local and foreign market

participants and the regional contagion risk of election shock to international markets.

Earlier studies mentioned above showed that national elections have significant

impact on stock return by comparing mean returns. Without considering the magnitude of

market volatility, the evidences are quite limited and arguable. In the study of Mei and Guo

(2004), increased market volatility was discovered during political election and transition

periods across 22 countries. Furthermore, over the sample period, financial crises mostly

happened during the periods of political election and transition and led to the conclusion that

political uncertainty could be a major contributing factor to financial crisis. After that, the

linkage between national elections and stock market volatility was formally investigated by

Białkowski et al. (2008). The study covered a sample of 27 OECD countries where majority

of the countries operate are under the parliamentary systems and aimed to test whether

national elections induce higher stock market volatility. The finding from the GARCH model

indicated that the country-specific component of index return variance can easily double

during the week around an election.

25

After the study of Białkowski et al. (2008), academic studies started to center in

testing the relationship between political events and stock market volatility by employing the

GARCH model (Wang and Lin, 2009; Chau et al., 2014; among others). Wang and Lin

(2009) found that democratically presidential elections negatively impacted stock returns and

induced higher volatility in the Taiwanese stock market. Unlike others, Chau et al. (2014)

studied the impact of political uncertainty caused by Arab Spring which started in December

2010 on conventional and Islamic stock market indices in the MENA region. Despite

heterogeneous reaction of the conventional and Islamic stock indices to that political turmoil,

only significant increase in the volatility of Islamic indices is detected during the period of

June 2009 to June 2012, whereas there is no significant effect on the volatility in

conventional markets.

Malaysia is an ideal test case because it is a country in which political situation and

market development have attracted recent theoretical interest. The Malaysian stock market

has steadily emerged as one of the top-performing markets in Asia. Its capitalisation has

reached USD 382 billion in December 2015 and it ranked the second highest in ASEAN

market after the Singapore exchange. In term of political situation, Malaysia is well-know as

a politically stable country. However, in term of electoral process, Malaysia has been quoted

with a long history of vote buying, vote stealing, and campaign media blitzes (Pepinsky,

2007). Electoral reform has been demanded and officially started with the forming of The

Coalition for Clean and Fair Elections or BERSIH by non-governmental organisations

(NGOs) in November 2006. This issue marked the start of Malaysian political tense. Due to

the significance of the Malaysian stock market, political stability will be an important factor

for local and foreign market participant in adjusting portfolio distribution.

26

From the literature of Malaysia stock market, only few studies had been done in investigating

the impact of general election on stock market volatility. A related study by Ali et al. (2010)

revealed that Malaysian stock market was affected by political events. They found significant

over-reaction behaviour existed in the Malaysian market upon announcement of the removal

of the deputy prime minister and announcement of the resignation of the prime minister. In

contrast, evidence of under-reaction was detected upon announcement of the national

election. Furthermore, Lean (2010) showed that general election in Malaysia significantly

affected the stock market performance. The stock returns reacted positively before election

and negatively after election. However, both studies did not focus on the possible impact of

general election on stock volatility. The study of Lean and Yeap (2017) circumvented the

limitation of previous studies and examined stock volatility during election periods. They

found significant election effect in stock volatility but not in the stock returns. Lean and Yeap

(2017) covered six general elections in their study, where the sample period is from the 8th to

13th general election. However, the market condition during the 12th and 13th general election

are clearly different with previous general election. The significant break point in the 12th

general election may have an effect on the inference of the analysis.

From the perspective of policy makers or government, this study might provide some

explanation about the reaction of the market in return and volatility. Policy makers and

government can use the information from this study to analyze the determinants that cause

the volatility of the stock market performance. Various policies either in fiscal or monetary

can be implemented to improve and stabilize the stock market in Malaysia. For investors or

investment institutions, they can use the information to get a better understanding on the

effect of general election. This will be increasing their awareness toward the government

action on policies and be able for them to adjust portfolio accordingly. For fund managers,

27

they can use this result to analyze the relationship of government policy on the stock return in

either a positive effect or negative way. With this, they can manage fund in a more effective

and efficient way.

2.3 Data

This study uses daily closing values of seven selected indices in Bursa Malaysia, namely

FTSE Bursa Malaysia Hijrah Shariah Index, FTSE Bursa Malaysia KLCI Index, FTSE Bursa

Malaysia Top 100 Index, FTSE Bursa Malaysia EMAS Shariah Index, FTSE Bursa Malaysia

EMAS Index, FTSE Bursa Malaysia Mid 70 Index and FTSE Bursa Malaysia Small Cap

Index1. The sample period covers the 12th and 13th Malaysian general election (21 May 2007

to 31 December 2015), with a total of 2,248 observations. All data are collected from Bursa

Malaysia (http://www.bursamalaysia.com). Table 2.1 shows the date of dissolution of

Parliament, election date or voting date and the date of 1st Parliament assembly after the

election for the 12th and 13th Malaysian general election.

[Insert Table 2.1: Malaysia General Election]

2.4 Empirical Methodology

Daily returns are calculated as the first difference in the natural logarithms of the stock

market index, )/ln(100 1 ttt IIR where tI and 1tI are the values for each index for

1 See Appendix 2.1: Details of the selected indices in this study.

28

periods t and 1t , respectively. In the case of a day following a non-trading day, the return

is calculated using the closing price of the latest trading day.

For an overview of the sample period, Table 2.2 presents the descriptive statistics of

daily stock returns for the selected stock indices. All the stock indices have a positive average

return over the sample period. The standard deviation and kurtosis are all positive, while the

skewness for all the series is negative. The null hypothesis of normally distributed daily

returns is rejected by the highly significant Jarque-Bera normality test. This finding is in line

with most of the previous findings which found that stock return series is non-normally

distributed. In addition, Table 2.3 shows the summary statistics of daily stock returns on the

pre-general election and post-general election periods. Interestingly, the mean returns for the

indices are all negative prior general election, while positive mean returns are recorded after

the general election.

[Insert Table 2.2: Descriptive Statistics for the Malaysian Stock Indices]

[Insert Table 2.3: Summary Statistics for the Returns on Pre- and Post-General

Election]

The Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH)

(p, q)2 model with dummy variables is applied to examine the general election effect and its

volatility. The mean equation and variance equation of the Exponential GARCH model are

expressed as:

2 According to Bolerslev et al. (1992), in testing the GARCH models, p = q = 1 is sufficient for most financial series.

29

tttttt RWMRPtGEPGER 1113210 (1)

t

q

it

it

iti

it

itijt

p

jjt PtGEPGE 2

11

2

10

2 2loglog

(2)

tttttt REMRPtGEPGER 1113210 (3)

t

q

it

it

iti

it

itijt

p

jjt PtGEPGE 2

11

2

10

2 2loglog

(4)

where tR is the logarithmic return of the market index at day t ; tPGE and tPtGE are dummy

variables; t is the error term. tPGE takes a value of one if the corresponding return for the

day t is pre-general election period, while tPtGE takes a value of one if the corresponding

return for the day t is post-general election period, and 0 otherwise. Meanwhile, in the mean

equations of Equation (1) and Equation (3), the 30 ,..., are parameters to be estimated.

Among them, 0 measures the mean return (in percentage) on other trading days; whereas

1 and 2 capture the average return of the stock index for the pre-general election period

and post-general election period. The null hypothesis of this test is 0: 210 H , which

implies that average daily returns (volatility) for the period of pre-general election and post-

general election have no different. If the null hypothesis does not hold, then it can be

concluded that the market index is characterized by statistically different on average returns

(volatility) for the period of pre-general election and post-general election. In another word,

this would imply that general election effect is indeed present in the market.

30

For the Equation (2) and (4), the left-hand side of the equations is the logarithm of the

conditional variance. This implies that the leverage effect is exponential, rather than

quadratic, and that forecasts of the conditional variance are guaranteed to be non-negative. In

this case, the presence of leverage effects can be tested by the hypothesis that i > 0, whereas

the impact is asymmetric if 0i . Furthermore, a lagged value of the return variable was

introduced in the equations to avoid serial correlation error terms in the model, which may

yield misleading inferences.

Besides, the return variables for MSCI World Index ( 1tRWM ) and MSCI Emerging

Market Index ( 1tREM ) are introduced into the mean equation. For the Equation (1), return

variable of MSCI World Index ( 1tRWM ) is added to examine whether the returns during the

election are associated with the MSCI World Index returns. While return variable of MSCI

Emerging Market Index ( 1tREM ) is added to Equation (3) as the control variables for

emerging market effect. Both the MSCI World Index and MSCI Emerging Market Index are

obtained from S&P Capital IQ. If the parameter of 1 is insignificant, then it can be

concluded that the returns during general election are not influenced by the MSCI World

Index ( 1tRWM ) and MSCI Emerging Market Index ( 1tREM ) returns.

2.5 Empirical Results and Discussions

Firstly, we examine the presence of pre-general election and post-general election effect in

the series of FTSE Bursa Malaysia Index by controlling the global effect. Table 2.4 reports

the estimation results of the mean equation and variance equation of the EGARCH (1, 1)

model based on Equation (1) and (2). Under the mean equation, the dummy coefficients of

the pre-general election are positive only for BMT100, BMKLCI and Shariah-compliant

31

stock indices (BMES and BMHS). Conversely, the pre-general election dummy coefficients

for the other stock indices are all negative. On the other hand, for post-general election, the

dummy coefficients are all positive. Nevertheless, the high p-value of dummy coefficient

indicates insignificant stock returns among all the series of indices, for both the pre-general

election and post-general election periods. Thus, there is no significant impact of general

elections on average stock market returns in Malaysia. This finding with an insignificant

abnormal return around election period is consistent with the studies of Lean (2010) and Lean

& Yeap (2017).

The estimation of the variance equation is presented in the second part of Table 2.4.

The results show that the Malaysian stock market encountered significant higher volatility in

pre-general election periods during the 12th and 13th General Election. This finding is evident

by the positive and highly significant pre-general election dummy coefficients for all the

stock indices model estimation. Meanwhile, the post-general election dummy coefficients in

the variance equation are all negative. Particularly, only the FTSE Bursa Malaysia Small Cap

Index showed insignificant negative volatility during post-general election.

By controlling the emerging market effect, the results of the mean equation and

variance equation of the EGARCH (1, 1) model based on Equation (3) and (4) are presented

in Table 2.5. The estimated results are similar to the first model which controlled for global

market effect. In term of control variables, the dummy coefficients of the MSCI World Index

and MSCI Emerging Index for the mean equations, as shown in Table 2.4 and Table 2.5, are

positive and significant at 1% for all the series of FTSE Bursa Malaysia Index. The positive

sign indicates that the Malaysian stock market returns are positively affected by an increase

in the return in global market and emerging market.

32

The asymmetric effect of the general election is reported in Table 2.4 and Table 2.5.

The significant asymmetry coefficient ( i ) strongly supports the asymmetric effect in most

of the indices. Moreover, the negative sign of the asymmetry coefficient means that volatility

decreases more when returns shocks are positive. Besides, the validity of the model is

supported by the diagnostic test with no remaining ARCH effect and serial correlation in all

of the estimated models.

[Insert Table 2.4: Pre-General Election and Post-General Election: EGARCH Results

Controlled by World Market Effect]

[Insert Table 2.5: Pre-General Election and Post-General Election: EGARCH Results

Controlled by Emerging Market Effect]

Overall, the examination of Malaysian stock market performance by large, medium and small

companies’ capitalization enable us to observe the impact of general election more precisely.

Moreover, we also examine the general election effect on Shariah-compliant stocks. As

shown in Figure 2.1, the index of the FTSE Bursa Malaysia Hijrah Shariah has the lowest

volatility for pre-general election, followed by the FTSE Bursa Malaysia KLCI Index and the

FTSE Bursa Malaysia Top 100 Index. Meanwhile, the FTSE Bursa Malaysia Small

Capitalization Index has the highest volatility during the pre-general election periods. The

result indicates that, in term of volatility, companies stock with larger market capitalization

and fulfilling the Shariah-compliant requirement are less affected by the election shock prior

general election.

33

[Insert Figure 2.1: Volatility during the Pre-General Election for the Selected Stock

Indices]

2.6 Conclusion

This study investigates the effect of the Malaysian general elections on its stock market

volatility from the year 2007 to 2015. Using the EGARCH model, we find significant

election effect in stock volatility but not in stock returns. Specifically, the stock volatility for

all selected stock indices is significantly higher during pre-general election periods but only

six stock indices recorded lower stock volatility in the post-general election periods. Notably,

political uncertainty due to the close fight between major parties during the 2008 and 2013

general election had a significant role in influencing the stock volatility prior to the election.

Furthermore, this study also finds that Shariah-compliant indices have lower stock volatility

compare to other indices.

The value added of this paper is we provide a detailed examination of Malaysian stock

market performance around general election by dividing into large, mid, small cap, and

Shariah-compliant indices. The findings show the relevance of market capitalization to stock

market volatility. Companies with small capital experienced higher stock volatility prior to

general election. The stock market volatility is indeed lower for larger companies stock. We

also observe relatively lower stock volatility in Shariah-compliant indices which suggest that

Shariah-compliant companies stock have a lower risk during pre-general election periods.

This study contributed to the evidence of general election influences stock market volatility

and made an effort to investigate the general election effect on stock indices with different

size of market capitalization. The implications of this study for investors are important. Risk-

34

averse investors could mitigate the political risk by diversifying their portfolio in large

companies stock and Shariah-compliant companies stock. Furthermore, an investor should be

vigilant during pre-general election periods as their profits are underlying high volatility and

compensation for abnormal high returns is negligible.

35

References Ali, N., Nassir, A. M., Hassan, T. and Abidin, S. Z. (2010). “Short Run Stock Overreaction: Evidence from Bursa Malaysia”, International Journal of Economics and Management, Vol. 4, pp. 319 – 333. Allivine, F. D. and O’Neil, D. D. (1980). “Stock Market Returns and the Presidential Election Cycle”, Financial Analysts Journal, Vol. 36, pp. 49 - 56. Bekaert, G. and Harvey, C. R. (2002). “Research in emerging markets finance: Looking to the future”, Emerging Markets Review, Vol. 3, pp. 429 - 448. Bialkowski, J., Gottachalk, K. and Wisniewski, T. (2008). “Stock Market Volatility around National Elections”, Journal of Banking and Finance, Vol. 32, pp. 1941-1953. Bolerslev, T., Chou, R. and Kroner, K. (1992). “ARCH Modelling in Finance: A Selective Review of the Theory and Empirical Evidence”, Journal of Econometrics, Vol. 52, pp. 5-59. Chau, F., Deesomsak, R. and Wang, J. (2014). “Political Uncertainty and Stock Market Volatility in the Middle East and North African (MENA) Countries”, Journal of International Financial Markets, Institutions & Money, Vol. 28, pp. 1 – 19. Chuang, C. and Wang, Y. (2010). “Electoral Information in Develop Stock Market: Testing Conditional Heteroscedasticity in the Market Model”, Applied Economics, Vol. 42, No. 9, pp. 1125-1131. Gemmill, G. (1992). “Political Risk and Market Efficiency: Tests based in British Stock and Options Markets in the 1987 Election”, Journal of Banking and Finance, Vol. 16, pp. 211-231. Huang, R. D. (1985). “Common Stock Returns and Presidential Elections”, Financial Analysts Journal, Vol. 41, pp. 58 - 65. Jones, S. T. and Banning, K. (2009). “US Elections and Monthly Stock Market Returns”, Journal of Economics and Finance, Vol. 33, pp. 273 - 287. Kim, K. A. and Nofsinger, J. R. (2008). “Behavioral finance in Asia”, Pacific-Basin Finance Journal, Vol. 16, pp. 1 - 7. Leblang, D. and Mukherjee, B. (2005). “Government Partisanship, Elections and the Stock Market: Examining Americana and British Stock Returns 1930-2000”, American Journal of Political Science, Vol. 49, pp. 780 – 802. Lean, H. H. (2010). “PoliticalGeneral Election and Stock Perfomance: The Malaysia Evidence”, In M. Ismail, & A. Mustafa, Research in Mathematics and Economics (pp.111 -120). Penang: Universiti Sains Malaysia.

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Lean, H. and Yeap, G. (2017). “Asymmetric Effect of Political Elections on Stock Returns and Volatility in Malaysia”, In Q. Munir, & S. C. Kok, Information Efficiency and Anomalies in Asian Equity Markets (pp. 228-245). UK: Routledge, Taylor and Francis Group. Lobo, B. (1999). “Jump Risk in the US Stock Market: Evidence using Political Information”, Review of Financial Economics, Vol. 8, pp. 149-163. Mei, J. P. and Guo, L. M. (2004). “Political Uncertainty, Financial Crisis and Market Volatility”, European Financial Management, Vol. 10, pp. 639 – 657. Nelson, D. (1991). “Conditional Heteroskedasticity in Asset Returns: A New Approach”, Econometrica, Vol. 59, pp. 347-370. Niederhoffer, V., Gibbs, S. and Bullock, J. (1970). “Presidential Elections and the Stock Market”, Financial Analysts Journal, Vol. 26, pp. 111 - 113. Nippani, S. and Arize, A. (2005). “US Presidential Election Impact on Cadian and Mexican Stock Markets”, Journal of Economics and Finance, Vol. 29, pp. 271-279. Nippani, S. and Medlin, W. B. (2002). “The 2000 Presidential Election and the Stock Market”, Journal of Economics and Finance, Vol. 26 No. 2, pp. 162 - 169. Nordhaus, W. (1975). “The Political Business Cycle”, Review of Economic Studies, Vol. 42, pp. 169-190. Pantzalis, C., Stangeland, D. A. and Turtle, H. J. (2000). “Political Elections and the Resolution of Uncertainty: The International Evidence”, Journal of Banking and Finance, Vol. 24, pp. 1575 - 1604. Peel, D. and Pope, P. (1983). “General Election in the UK in the post-1950 period and the Behavior of the Stock Market”, Investment Analysis, Vol. 67, pp. 4-10. Pepinsky, T. (2007). “Autocracy, Elections and Fiscal Policy: Evidence from Malaysia”, Studies in Comparative International Development, Vol. 42, No. 1, pp. 136 - 163. Statman, M., Fisher, K. L. and Anginer, D. (2008). “Affect in a behavioural asset-pricing model”, Financial Analysts Journal, Vol. 64, No. 2, pp. 20 - 29. Tuyon, J., Ahamd, Z. and Matahir, H. (2016). “The role of investor sentiment in Malaysian stock market”, Asian Academy of Management Journal of Accounting and Finance, Vol. 12, Suppl. 1, pp. 43 - 75. Wang, Y. H. and Lin, C. T. (2009). “The Political Uncertainty and Stock Market Behavior in Emerging Democracy: The Case of Taiwan”, Quality and Quantity, Vol. 43, pp. 237-248. Wong, W. and McAleer, M. (2009). “Mapping the Presidential Election Cycle in US Stock Market”, Mathematics and Computers in Simulation, Vol. 79, pp. 3267-3277.

37

Table 2.1: Malaysia General Election

Dissolution of

Parliament Election Day 1st Parliament Assembly after Election

12th General Election

13 February 2008 (Wednesday)

8 March 2008 (Saturday)

28 April 2008 (Monday)


3 April 2013 (Wednesday)

5 May 2013 (Sunday)

24 June 2013 (Monday)

Source: Authors' compilation based on information from Election Commission of Malaysia and Parliament of Malaysia websites.

Table 2.2: Descriptive Statistics for the Malaysian Stock Indices

BMEMAS BMT100 BMM70 BMKLCI BMSC BMES BMHS Mean 0.0119 0.0115 0.0135 0.0102 0.0188 0.0142 0.0191 Max 4.4184 4.1961 5.2661 4.2587 6.7322 4.0747 4.5368 Min -9.9494 -10.0817 -9.9045 -9.9785 -9.0170 -11.3205 -11.0873 Std. Dev. 0.7769 0.7746 0.8634 0.7622 1.0333 0.8061 0.8245 Skewness -1.1855 -1.1571 -1.1950 -1.1581 -0.8067 -1.5094 -1.1982 Kurtosis 18.8855 19.4337 15.7521 19.2995 11.2360 23.8308 21.7728 Jarque- Bera 24163.18 25797.76 15766.73 25387.37 6597.39 41497.55 33547.62

Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Note: BMEMAS: FTSE Bursa Malaysia EMAS Index, BMT100: FTSE Bursa Malaysia Top 100 Index, BMM70: FTSE Bursa Malaysia Mid 70 Index, BMKLCI: FTSE Bursa Malaysia KLCI, BMSC: FTSE Bursa Malaysia Small Cap Index, BMES: FTSE Bursa Malaysia EMAS Shariah Index, and BMHS: FTSE Bursa Malaysia Hijrah Shariah Index.

38

Table 2.3: Summary Statistics for the Returns on Pre- and Post-General Election

BMEMAS BMT100 BMM70 BMKLCI BMSC BMES BMHS PreGE- Mean -0.2062 -0.2021 -0.2687 -0.2039 -0.2928 -0.1982 -0.1892

PostGE- Mean 0.0456 0.0344 0.1007 0.0340 0.2369 0.0355 0.0533

PreGE- Max 1.3967 1.5471 1.3502 1.5086 1.1529 1.1519 1.4903

PostGE- Max 3.5972 3.5318 4.3072 3.3222 5.1983 3.2472 3.3339

PreGE- Min -2.4440 -2.5106 -2.4713 -2.6051 -1.9755 -2.7384 -3.2789

PostGE- Min -9.9494 -10.0817 -9.9045 -9.9785 -9.0170 -11.3205 -11.0873

Note: Pre-General Election: Dissolution of Parliament to the day before General Election; and Post-General Election: Day after the General Election to the first day of the Parliament Assembly.

39

Table 2.4: Pre-General Election and Post-General Election: EGARCH Results Controlled by World Market Effect

Variables BMEMAS BMT100 BMM70 BMKLCI BMSC BMES BMHS

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (2, 1) (1, 1) Mean Equation

0 0.0104 (0.3474)

0.0107 (0.3315)

0.0047 (0.6942)

0.0119 (0.2907)

0.0315 (0.0300)**

0.0200 (0.0898)*

0.0268 (0.0291)**

PGE -0.0018 (0.9948)

0.0051 (0.9849)

-0.1081 (0.6830)

0.0111 (0.9674)

-0.2385 (0.4578)

0.0129 (0.9575)

0.0099 (0.9598)

PtGE 0.0692 (0.4752)

0.0595 (0.5396)

0.1264 (0.2394)

0.0261 (0.7846)

0.1553 (0.2624)

0.0465 (0.6349)

0.0648 (0.4950)

1tR 0.0450 (0.0183)**

0.0397 (0.0406)**

0.0602 (0.0013)***

0.0348 (0.0868)*

0.0996 (0.0000)***

0.0264 (0.2178)

0.0310 (0.0942)*

1tRWM 0.2162 (0.0000)***

0.2220 (0.0000)***

0.2382 (0.0000)***

0.2194 (0.0000)***

0.1791 (0.0000)***

0.2043 (0.0000)***

0.1980 (0.0000)***

Variance Equation

0 -0.1485 (0.0000)***

-0.1395 (0.0000)***

-0.1520 (0.0000)***

-0.1275 (0.0000)***

-0.2412 (0.0000)***

-0.1400 (0.0000)***

-0.1191 (0.0000)***

1 0.1698 (0.0000)***

0.1616 (0.0000)***

0.1806 (0.0000)***

0.1449 (0.0000)***

0.2952 (0.0000)***

0.2643 (0.0000)***

0.1483 (0.0000)***

2 -- --

-- --

-- --

-- --

-- --

-0.0973 (0.0000)***

-- --

i -0.0791 (0.0000)***

-0.0730 (0.0000)***

-0.0666 (0.0000)***

-0.0688 (0.0000)***

-0.0561 (0.0000)***

-0.0641 (0.0000)***

-0.0641 (0.0000)***

1 0.9797 (0.0000)***

0.9826 (0.0000)***

0.9785 (0.0000)***

0.9825 (0.0000)***

0.9409 (0.0000)***

0.9827 (0.0000)***

0.9894 (0.0000)***

PGE 0.1514 (0.0000)***

0.1411 (0.0000)***

0.1688 (0.0000)***

0.1314 (0.0000)***

0.2798 (0.0000)***

0.1438 (0.0000)***

0.1094 (0.0000)***

PtGE -0.0517 (0.0009)***

-0.0511 (0.0003)***

-0.0553 (0.0059)***

-0.0455 (0.0000)***

-0.0201 (0.5818)

-0.0443 (0.0034)***

-0.0467 (0.0010)***

(Diagnostic Checking) ARCH – LM Statistic (p-value)

5 lags 0.5321 0.5960 0.3584 0.3131 0.6421 0.6534 0.7076 10 lags 0.3713 0.3218 0.5014 0.1941 0.3161 0.3387 0.4458

Ljung-Box Q2 Statistic (p-value) 5 lags 0.5190 0.5870 0.3560 0.2960 0.6490 0.6510 0.7150

10 lags 0.3800 0.3300 0.5090 0.1970 0.3070 0.3330 0.4290 Return Equation: Wald Test (p-value)

F-stat 0.7745 0.8256 0.4699 0.9628 0.4192 0.8905 0.7872 Chi-Square 0.7745 0.8256 0.4698 0.9628 0.4190 0.8905 0.7871

Variance Equation: Wald Test (p-value) F-stat 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Chi-Square 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Note: BMEMAS: FTSE Bursa Malaysia EMAS Index, BMT100: FTSE Bursa Malaysia Top 100 Index, BMM70: FTSE Bursa Malaysia Mid 70 Index, BMKLCI: FTSE Bursa Malaysia KLCI, BMSC: FTSE Bursa Malaysia Small Cap Index, BMES: FTSE Bursa Malaysia EMAS Shariah Index, and BMHS: FTSE Bursa Malaysia Hijrah Shariah Index. ***, ** and * denote significance at 1, 5 and 10% levels respectively. Numbers in parentheses depict p-value. The null hypothesis of the Wald Test is 0: 210 H (same average return / volatility for pre- and post-General Election).

40

Table 2.5: Pre-General Election and Post-General Election: EGARCH Results Controlled by Emerging Market Effect

Variables BMEMAS BMT100 BMM70 BMKLCI BMSC BMES BMHS

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0255 (0.0235)**

0.0256 (0.0211)**

0.0162 (0.2279)

0.0211 (0.0721)*

0.0329 (0.0380)**

0.0247 (0.0355)**

0.0252 (0.0413)**

PGE 0.0429 (0.8610)

0.0486 (0.8390)

-0.0329 (0.9066)

0.0551 (0.8244)

-0.0794 (0.8253)

0.0138 (0.9505)

0.0555 (0.7576)

PtGE 0.0952 (0.3211)

0.0905 (0.3450)

0.1534 (0.1365)

0.1002 (0.3046)

0.1696 (0.2094)

0.0975 (0.3165)

0.0987 (0.2831)

1tR 0.0069 (0.7820)

-0.0137 (0.5704)

0.0392 (0.1039)

-0.0195 (0.4224)

0.1066 (0.0000)***

-0.0219 (0.3341)

-0.0217 (0.3037)

1tREM 0.1394 (0.0000)***

0.1491 (0.0000)***

0.1333 (0.0000)***

0.1496 (0.0000)***

0.0718 (0.0000)***

0.1420 (0.0000)***

0.1530 (0.0000)***

Variance Equation

0 -0.1487 (0.0000)***

-0.1358 (0.0000)***

-0.1573 (0.0000)***

-0.1220 (0.0000)***

-0.2405 (0.0000)***

-0.1592 (0.0000)***

-0.1183 (0.0000)***

1 0.1687 (0.0000)***

0.1567 (0.0000)***

0.1843 (0.0000)***

0.1389 (0.0000)***

0.2975 (0.0000)***

0.1892 (0.0000)***

0.1486 (0.0000)***

i -0.0774 (0.0000)***

-0.0693 (0.0000)***

-0.0662 (0.0000)***

-0.0655 (0.0000)***

-0.0589 (0.0000)***

-0.0627 (0.0000)***

-0.0596 (0.0000)***

1 0.9791 (0.0000)***

0.9830 (0.0000)***

0.9748 (0.0000)***

0.9831 (0.0000)***

0.9408 (0.0000)***

0.9792 (0.0000)***

0.9897 (0.0000)***

PGE 0.1427 (0.0000)***

0.1317 (0.0000)***

0.1707 (0.0000)***

0.1220 (0.0000)***

0.2611 (0.0000)***

0.1490 (0.0000)***

0.1048 (0.0000)***

PtGE -0.0507 (0.0011)***

-0.0506 (0.0002)***

-0.0489 (0.0214)**

-0.0478 (0.0000)***

-0.0188 (0.6071)

-0.0430 (0.0120)**

-0.0469 (0.0002)***


5 lags 0.2413 0.2413 0.4836 0.0702 0.6652 0.3359 0.5219 10 lags 0.1065 0.0840 0.5644 0.0276 0.2773 0.0970 0.1887

Ljung-Box Q2 Statistic (p-value) 5 lags 0.2410 0.2410 0.4700 0.0650 0.6610 0.3270 0.5100

10 lags 0.1110 0.0870 0.5690 0.0280 0.2640 0.1030 0.1760 Return Equation: Wald Test (p-value)

F-stat 0.5845 0.6066 0.3298 0.5664 0.4402 0.5956 0.5104 Chi-Square 0.5845 0.6065 0.3296 0.5663 0.4400 0.5955 0.5103

Variance Equation: Wald Test (p-value) F-stat 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Chi-Square 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Note: BMEMAS: FTSE Bursa Malaysia EMAS Index, BMT100: FTSE Bursa Malaysia Top 100 Index, BMM70: FTSE Bursa Malaysia Mid 70 Index, BMKLCI: FTSE Bursa Malaysia KLCI, BMSC: FTSE Bursa Malaysia Small Cap Index, BMES: FTSE Bursa Malaysia EMAS Shariah Index, and BMHS: FTSE Bursa Malaysia Hijrah Shariah Index. ***, ** and * denote significance at 1, 5 and 10% levels respectively. Numbers in parentheses depict p-value. The null hypothesis of the Wald Test is 0: 210 H (same average return / volatility for pre- and post-General Election).

41

Figure 2.1: Volatility during the Pre-General Election for the Selected Stock Indices

Appendix 2.1: Details of the Selected Indices in this Study

Selected Indices in this Study Details of the Indices BMEMAS FTSE Bursa Malaysia

EMAS Index Constituents of the FTSE Bursa Malaysia Top 100 Index and FTSE Bursa Malaysia Small Cap Index

BMT100 FTSE Bursa Malaysia Top 100 Index

Sum of constituents in FTSE Bursa Malaysia Mid 70 Index and FTSE Bursa Malaysia KLCI

BMM70 FTSE Bursa Malaysia Mid 70 Index

Constituents of the next 70 companies in FBMEMAS

BMKLCI FTSE Bursa Malaysia KLCI

Constituents of 30 largest companies in FBMEMAS by full market capitalization

BMSC FTSE Bursa Malaysia Small Cap Index

Constituents of top 98% of the Bursa Malaysia Main Market excluding FTSE Bursa Malaysia Top 100 Index constituents

BMES FTSE Bursa Malaysia EMAS Shariah Index

Shariah-compliant constituents of the FBMEMAS that meet the screening requirement of the SAC

BMHS FTSE Bursa Malaysia Hijrah Shariah Index

Constituents of 30 largest Shariah-compliant companies in FBMEMAS screened by Yasaar Ltd and the Securities Commission's Shariah Advisory Council (SAC)

Source: Authors' compilation based on information from Bursa Malaysia’s website. For further information, please visit: http://www.bursamalaysia.com.

http://www.bursamalaysia.com/

42

CHAPTER 3

STOCK MARKET VOLATILITY IN MALAYSIA SECTORAL INDICES DURING THE GENERAL ELECTION

3.1 Introduction Political factor that exerts influence on investors' decision-making is one of the possible

causes of market sentiment in the stock market. Specifically, investor sentiment or

expectations on major political events could exhibit optimism or pessimism. The stage of

sentiment will induce underreaction or overreaction in the market which influence changes in

trading volume, volatility, prices and accordingly determine stock returns (Tuyon et al.,

2016). Hence, sentiment risk could be deemed as a systematic behavioural risk. In investment

practice, the role of investor sentiment on the stock market activity is important because the

stock prices are affected by both the fundamental and behavioural forces (Akerlof and Shiller,

2009). During major political events, the combination of fundamental and behavioural forces

in decision-making cause bounded rationality in market players which could induce

uncertainty in the stock market.

In the literature, it is evident from several studies that the occurrences of major

political events induced higher market volatility. The recent empirical evidence is found on

the national election (Mei and Guo, 2004; Białkowski et al., 2008; Jones and Banning, 2009;

Lean and Yeap, 2017; among others), delay in election results (Nippani and Arize, 2005),

change of ruling party (Lin and Wang, 2007), as well as the political scandal (Lobo, 1999).

Previous studies on the relationship between political events and stock market performance

are largely centered on elections. Earlier studies of Niederhoffer et al. (1970), Peel and Pope

(1983) and Gemmill (1992) have examined the stock price behaviours during national

43

elections in developed countries. These studies found that changes in government

administration after elections tend to affect financial policies or legislation, thereby stock

prices were significantly impacted. However, the study on election effect in emerging market

only started in recent years, for instance, Wang and Lin (2009) and Hung (2011) on

Taiwanese stock market, Lean (2010) and Lean and Yeap (2017) on Malaysian stock market.

The evidence found in previous studies is mostly based on the examination of main

composite indices, such as the Toronto 300 Composite and the I.P.C. All-Share in Nippani

and Arize (2005), the Taiwan Stock Exchange Value Weighted Index (TAIEX) in Wang and

Lin (2009), and the FTSE Bursa Malaysia KLCI Index in Lean and Yeap (2017). Besides

information from the composite index, sector-specific information could be useful for

investors to narrow down their investments option in the financial market. Nevertheless, the

stock return volatility due to changes in political may evolve differently in sectoral indices.

Therefore, the evidence found based on composite indices need not be applicable to the

individual sectors.

In addition, there are recent studies on sector-specific analysis of the stock market in

the Asian region (Cao et al., 2013; Lakshmi, 2013). The main focus of their studies is to

investigate the sensitivity of the sectoral indices to market fluctuation and the performance of

the sectoral indices. Nevertheless, the aspect of the influence of political events on the

movement of sectoral indices has not been thoroughly discussed. Moreover, recent research

provided evidence that firms in different sectors are reported to have different sentiment

effect (Kaplanski and Levy, 2010; Chou et al. 2012; Chen et al., 2013; Dash and Mahakud,

2013). Hence, a comprehensive analysis of stock market performance based on sectoral

44

indices should be addressed in order to have a better understanding of political changes in

relation to fluctuation in sectoral indices.

In behavioural finance, Asia suffers from higher risk of behavioural biases than other

developed markets (Ritter, 2003; Schmeling, 2009). Nevertheless, emerging financial

markets are still attractive to investors because of their relatively higher returns compared to

developed financial markets (Kearney, 2012). Among the emerging markets, the Malaysian

stock market is quite a developed capital market (Mohamad et al. 2007). The Bursa Malaysia

has steadily emerged as one of the top-performing markets in Asia. Its capitalisation has

reached USD 382 billion in December 2015 and the market ranked the second highest in

ASEAN markets after the Singapore Exchange. In terms of behavioural risk, empirical

studies of Statman (2008) and Tuyon et al. (2016) found that Malaysian investors are affected

by sentiment in their investment decision making. The finding of Tuyon et al. (2016) further

highlighted that investor sentiment risks influence stock prices regardless of size and industry

groups.

From the perspective of statistical analysis, single country data analysis is preferred to

mitigate the heterogeneous effect of multiple country characteristics such as differences in

economics, political, institutional, demographics and culture (Bekaert and Harvey, 2002;

Statman, 2008). Hence, taken all these facts, the Malaysian stock market is chosen as a single

country testing case to see the influence of political events on the movement of stock prices

and this study could be of interest to international investors. Evidently, as a proxy of the

Malaysian stock market, the key index of FTSE Bursa Malaysia KLCI experienced

significant volatility during the general election years (Lean and Yeap, 2017). Prior to the

year 2005, the 9th, 10th, and 11th Malaysian general election have not resulted in unexpected

45

outcomes as the coalition Barisan Nasional (BN) won and continued ruling with a stable two-

thirds majority. Hence, general ups and downs in the stock market are well-anticipated by

investors. On the other hand, the coalition BN experienced close fight in the 12th and 13th

general election and consecutively lost the two-thirds majority in parliament, which is never

happened in political history since Malaysia independence. Besides, the total percentage vote

for BN experienced significant drop from 63.8% in year 2004, to 51.4% in year 2008 and

47.4% in year 2013. Due to political uncertainty, a sharp decline in the key indices of FTSE

Bursa Malaysia was recorded prior to the general election and investors' confidence was

badly shaken due to the potential shift of ruling party.

Therefore, in order to examine the election effect, the focus of this study is on the

Malaysian sectoral indices for the past general election years of 1995, 1999, 2004, 2008 and

2013. The sectoral index provides a value for the aggregate performance of a number of

companies of a particular sector and it serves as an indirect measure of the performance of the

economy. There are ten main indices based on sectors or industries at the Bursa Malaysia,

each represents the sector of Construction, Consumer Product, Finance, Industrial, Industrial

Product, Mining, Plantation, Property, Trading and Services, Technology. A benchmark

index of FBMKLCI also included in the analysis for comparison purpose.

In general, using a long history of aggregate stock returns that incorporates a sharp

decline may produce erroneous inferences due to model misspecification. However, previous

studies on the Malaysia election effect did not address this issue. For example, Lean and

Yeap (2017) covered six general elections (the 8th to 13th general elections) under the same

sample period. In fact, the market condition during the general election years of 2008 and

2013 (the 12th and 13th general elections) is clearly different with previous general elections.

46

In concern of the different effect of the general election on market volatility, this study

divides the general election periods into two stages. One stage represents the general ups and

downs periods from 1994 to 2005, and the other represents drastic shock periods from 2006

to 2015.

In brief, the contributions of this study are, first, the Threshold Generalized

Autoregressive Conditional Heteroskedasticity (Threshold GARCH / GJR GARCH) model

developed by Glosten et al. (1993) is applied to investigate the pre-general election and post-

general election effect on sectoral indices of the Malaysian stock market. Previous studies

examined the impact of the election on the composite index, while this study attempts to see

the election effect on the ten sectoral indices. Second, the selection of the event window in

this study is in line with the Malaysian general election process. Relevant studies normally

used trading day windows before and after the election, for example, 1 week, 2 weeks and 1

month, to see the different effect of election. This study precisely defines the pre-general

election period as the trading days from the day of dissolution of the parliament until the day

before voting, while the post-general election period covers the trading days from the day

after voting until the day of first parliament assembly.

Third, this study enhances the knowledge in the case of Malaysia by investigating the

election effect in two different stages which represent the general up and down and the

drastic rise and fall period. Fourth, the MSCI World Index is included as a control variable in

the model to account for the global market effect. Moreover, this study also conducts an array

of robustness checks, including analyzing the model with the MSCI Emerging Market Index

to control for emerging market effect, considering Chicago Board Options Exchange (CBOE)

Volatility Index (VIX) as one of the market uncertainty indicator for global risk, and

47

controlling the US Federal Fund Rate for interest rate differentials. This study may be of

interest to investors as the results will contribute the information that most investors require

particularly in constructing an effective equity portfolio investment during the times of

election.

The rest of the paper is organized as follows. Section 3.1 summarizes the literature of

related studies. Section 3.2 describes the nature of the data sets and the methodology adopted

in this study. Section 3.3 reports the estimation results. Next, Section 3.4 reports the

Extensions and Robustness results in this study. Lastly, Section 3.5 concludes by highlighting

some implications of the findings.


In modern finance, investors are assumed to be rational in making decisions on portfolio

investment (Lawrence et al., 2007). Nevertheless, behavioural finance research has confirmed

bounded rationality of investors that drives to market inefficiency. Irrational behaviours in

decision-making including considered gossip, rumors and tips as an information (Bauman,

1989). Moreover, this behavioural risk is critical for Asia emerging financial markets because

Asian are more socially collective in decision-making (Kim and Nofsinger, 2008).

Collectively, political events such as national election could be possibly one of the drivers of

bounded rationality that influence the performance of a stock market. Specifically, during the

election period, the rumors circulated by the media and campaign strategists may induce

irrational behaviours in investors decision-making. Investors who confronted with political

risk are likely to have an extreme response and their reaction will differ substantially from the

optimal forecast. Thus, if the outcome of the election does not allow investors to immediately

assess the effect on the country's future, then this will induce surprises in the markets.

48

In the literature, the mounting evidence of political election effect lends support to

bounded rational of investors' behaviour. The pioneering study by Nordhaus (1975) discussed

the relationship between economic performance and political business cycle. Other studies

such as Allivine and O’Neill (1980) and Huang (1985) concentrated on the U.S. presidential

election cycle, in which the U.S. stock markets make larger gains in the third and fourth years

of a presidential term. Recent evidence in the area includes the study by Wong and McAleer

(2009) indicating the impact of U.S. presidential elections on its stock market. They found

that the U.S. stock prices closely followed the four-year presidential election cycle and the

cyclical trend existed for the last ten administrations from the year 1965 through 2003,

particularly when the incumbent is Republican.

Besides the U.S. presidential election cycle, additional studies including Foerster and Schmitz

(1997), Leblang and Mukherjee (2005), Wang and Lin (2009) and Hung (2013) investigated

the impact of presidential election results on international stock markets. Finding of these

studies indicated that stock markets are affected by the presidential election. Moreover, there

are several studies in the empirical literature that examined the impact of one-time occurrence

of the political event on financial markets. Lobo (1999) found that there was a great deal of

insecurity amongst investors in the U.S. stock market after a political scandal had been

revealed. Other related studies of Nippani and Medlin (2002) and Nippani and Arize (2005)

examined the impact of the delay in the 2000 presidential election results to stock market

performance. Using the conventional heteroskedastic t-tests and binary variable regression,

they discovered that the delay had negatively impacted stock prices. The negative impact not

only appeared in U.S. stock market, but also Mexican and Canadian stock market. It is

49

evident that election results are a widely watched event by local and foreign market

participants.

In earlier literature, the conventional t-test is used to test the hypothesis of presidential

election effect in the stock market, for example, Allivine and O’Neill (1980), Nippani and

Medlin (2002) and Hung (2011). The limitations are that these studies do not consider the

effect of time dependence and conditional heteroskedasticity or the GARCH effect in stock

returns. Campbell and Hentschel (1992) presented the volatility feedback hypothesis, where

any innovations to volatility (especially positive ones) lead to a decrease in returns. Hence,

volatility is a fundamental issue in testing the impact of the political election on stock market

performance. There is another strand of interesting research on political elections and stock

market volatility (Białkowski et al., 2008; Wang and Lin, 2009; Chau et al., 2014; Smales,

2016; among others). In particular, Białkowski et al. (2008) revealed that the index return

variance can easily double during the week around an election in a sample of 27 OECD

countries. Wang and Lin (2009) also found that presidential elections negatively impacted

stock returns and induced higher volatility in the Taiwanese stock market. In the case of Arab

Spring which started in December 2010, Chau et al. (2014) studied the impact of political

uncertainty on conventional and Islamic stock market indices in the MENA region. Despite

the heterogeneous reaction of the conventional stock indices and Islamic stock indices to that

political turmoil, volatility of the Islamic indices significantly increased during the period of

June 2009 to June 2012, whereas there is no significant effect on the volatility of

conventional markets.

On top of that, previous studies as mentioned above mostly focused on the relationship

between political election and the stock market in a Presidential system country. However,

50

less attention is given in country with a Parliamentary system which held elections for the

national parliament. The major difference between these two systems is that in a Presidential

system, the President is the executive leader directly voted upon by the people, while in a

Parliamentary system, the Prime Minister is the executive leader elected from the legislative

branch directly. For that reason, the sensitiveness of stock market to the election may vary

between Presidential system country and Parliamentary system country.

Malaysia is a country with a Parliamentary system where the Parliament of Malaysia is the

national legislature of Malaysia. Moreover, Malaysia has an interesting political background

for the testing of election effect. The recent five general elections held during 1994 - 2015

were accompanied by a political uncertainty due to fierce challenge between the opposition

and the incumbent. Moreover, there have been increasing concerns about the change of

government in the recent two general elections in the year 2008 and 2013. The key index of

FTSE Bursa Malaysia KLCI has undergone significant volatility during the general election

years. Notably, the KLCI shows an unusually high spike before the election during the

general election years of 2008 and 2013 (Lean and Yeap, 2017). As documented by Smales

(2016), the implied volatility of financial markets increases in line with uncertainty about the

election outcome. Therefore, uncertainty arising from political elections in Malaysia can

cause a drastic effect on the performance of the stock market.

In the Malaysian stock market, only a few studies had been done in relation to the general

election. The study by Ali et al. (2010) found significant over-reaction behaviour existed

upon the announcement of the removal of the deputy prime minister and announcement of the

resignation of the prime minister. In contrast, evidence of under-reaction was detected upon

the announcement of the national election. Ali et al. (2010) explained that investors are well

51

predicted with the election outcome, hence the finding of under-reaction is in line with the

political condition at that times of election. Furthermore, Lean (2010) showed that general

election in Malaysia significantly affected the stock market performance, where stock returns

react positively before the election and negatively after the election. However, both studies

did not focus on the possible impact of the general election on stock volatility. The study of

Lean and Yeap (2017) circumvented the limitation of previous studies and examined stock

volatility during election periods. They found significant election effect in stock volatility but

not in the stock returns.

The study of Lean and Yeap (2017) used a long history of stock returns that covered six

general elections from the 8th to 13th general election. However, the Malaysian stock market

experienced a sharp decline prior to the 12th and 13th general election due to the close fight

between major coalitions. The market condition during the 12th and 13th general election are

clearly different from the previous general election. It is noteworthy that there is a significant

research showed that stock volatility varies according to market condition. The study of Cao

et al. (2013) examined the stock market performance in China during the bull and bear

market happened in the year 2007 and 2008. The study is specially designed to capture the

movement of stock during the stage of drastic shock periods in 2007 and 2008, and the

general ups and downs periods. The study concluded that the movement of stock indices are

different in the two stages. When the market experiences drastic ups and downs, the sectoral

indices exhibit very close correlations between each other. However, much smaller

correlations appear in the general ups and downs period. Hence, taken together with all these

considerations, this present study divides the sample period into two sub-samples to avoid

erroneous inferences, and to reflect the real market volatility under different political tense. In

52

brief, statistical results of this study are able to fill in the research gap by showing a

significant difference of market volatility in the two sub-sample periods.

Despite different industry characteristic, previous studies conclude the findings on election

effect by observing the stock market aggregately. In fact, firms in a different industry are

expected to have a different characteristic. Based on the industry type definition used by

(Becher et al., 2008; Held, 2009; Nagy and Ruban, 2011), there are two industry groups in

which the defensive industry is expected to be less sensitive to macroeconomic and market

fluctuations, and the cyclical industry is more sensitive to the macroeconomic and market

developments. Moreover, the study of Lakshmi (2013) found volatility patterns are not the

same across the eleven selected sectoral indices in Indian Stock Market. They found out that

the realty sector has witnessed higher volatility than any other sector during the period of the

global meltdown in the year 2008 to 2013. Thus, there are possibilities that the impact of the

general election on the industry may vary among each other, and this present study attempts

to fill in the research gap by examining the sectoral stock returns and volatility in the recent

five Malaysian general elections.

3.3 Data and Empirical Methodology

This study uses daily closing values of the FTSE Bursa Malaysia KLCI Index and ten

selected main sectors indices (Construction, Consumer Product, Finance, Industrial,

Industrial Product, Mining, Plantation, Property, Trade and Services, and Technology). The

full sample period covers from 4 January 1994 to 31 December 2015, with a total of 5,738

observations, which covers the recent five Malaysia general elections. All data are collected

from the Bursa Malaysia (http://www.bursamalaysia.com). For control variable, the MSCI

World Index and MSCI Emerging Index, obtained from the S&P Capital IQ, are used to

53

control for world market and emerging market effect, respectively. Besides, the Chicago

Board Options Exchange (CBOE) Volatility Index (VIX) is used as an indicator of global

risk, and the US Federal Fund Rate is used for interest rate differentials. The important dates

of general elections are summarized in Table 3.1, which are the date of dissolution of

parliament, election date or voting date and the 1st parliament assembly after election. The

pre-general election period refers to the duration from the day of dissolution of the parliament

until the day before voting, while the post-general election period refers to the duration from

the day after voting until the day of first parliament assembly.

[Insert Table 3.1: Malaysia General Election Information]

Table 3.2 presents the descriptive statistics for daily returns series for the full sample period.

Daily returns are calculated as the first difference in the natural logarithms of the stock

market index, )/ln(100 1 ttt IIR where tI and 1tI are the values of each index for

periods t and 1t , respectively. In the case of a trading day following a non-trading day, the

return is calculated using the closing price of the last trading day. From the descriptive

statistics, the null hypothesis of normally distributed daily returns is rejected by the Jarque-

Bera normality test. This finding is in line with most of the previous findings, saying that

daily stock returns are not normally distributed.

[Insert Table 3.2: Descriptive Statistics for the Malaysian Stock Indices]

Furthermore, mean returns for the periods of pre-general election and post-general

election are presented in Table 3.3. It is observed that the mean returns prior to general

election are mostly positive for the sub-sample period of 1994-2005. However, for the sub-

54

sample period of 2006-2015, the mean returns are all negative prior to general election. On

the other hand, for the period of post-general election, the mean returns for the indices are all

negative for the sub-sample period of 1994-2005, except for the sectoral indices of Consumer

Product and Industrial. For the period of 2006-2015, all the mean returns are positive after

general election. From the descriptive statistics and mean returns for the two sub-sample

periods, it is notable that there could be different election effects on the stock market for the

general elections in year 1994 to 2005 and 2006 to 2015. The preliminary statistics justify the

aim of this study in dividing the full sample period into two sub-samples in order to study the

election effects under different political condition.

[Insert Table 3.3: Mean Returns on Pre-General Election and Post-General Election]

In this study, the test for market volatility during general elections is carried out by

using the Threshold Generalized Autoregressive Conditional Heteroskedasticity (Threshold

GARCH / GJR GARCH) model developed by Glosten et al. (1993), Threshold GARCH3

model with dummy variables:

tttttt RWMRPtGEPGER 1113210 (1)

ttttttt PtGEPGEN 212

1112

12

1102

(2)


variables which take on value 1 if the corresponding return for day t is the period of the pre-

3 According to Bollerslev et al. (1992), in testing the GARCH models, p = q = 1 is sufficient for most financial series. Hence, the sufficient order of p and q considered in this study for the Threshold GARCH model, is (1, 1).

55

general election, and the period of the post-general election respectively, and 0 otherwise; t

is the error term. Meanwhile, 30 ,..., are the parameters to be estimated. Among them, 0

measures the mean return (in percentage) on other trading days; whereas 1 and 2 capture

the average return of the stock index for the period of pre-general election and post-general

election. At the later part of the estimation, a lagged value return variable for the MSCI

World Index ( 1tRWM ) is introduced into the mean equation and variance equation as control

variables to examine whether the returns of the general election years are associated with the

MSCI World Index lagged return.

The null hypothesis of the test is 0: 210 H , which implies that average daily

returns (volatility) for the period of pre-general election and post-general election are

significantly different from zero. If the null hypothesis does not hold, then it can be

concluded that the market index is characterized by statistically different on average returns

(volatility) for the period of pre-general election and post-general election. In another word,

this would imply that general election effect is indeed present in the market. Besides, if the

parameter of 3 is insignificant, then it can be concluded that the stock returns of the general

election years are not influenced by the MSCI World Index lagged return.

In the Equation (2), tN takes on value 1 when the stock quote falls in a period and 0

for increments of the stock quotation. Besides, the parameter is used to capture the

asymmetrical effect of bad news (decrease in stock indices, hence negative tR ) and good

news (increase stock indices, hence positive tR ). If 0 by the t-test of significance, then it

can be concluded that the impact of news is asymmetric. If the parameter is positive, then

56

good news has an impact of i on volatility while bad news has an impact of ( i ) on

volatility. Thus, the positive value of indicates the existence of a leverage effect in that bad

news increases volatility. The additional parameters, t , which makes this specification

different from the original Threshold GARCH model, are employed to capture the daily

effect. Furthermore, a lagged value of the return variable is introduced in the equations to

avoid serial correlation error terms in the model, which may yield misleading inferences.


Firstly, the results of the pre-general election and post-general election effect on the sectoral

indices for the full-sample period of 1994-2015 are presented in Table 3.4(a) and Table

3.4(b). Table 3.4(a) reports the results of the mean equation and variance equation of the

Threshold GARCH (1, 1) model for the FTSE Bursa Malaysia KLCI index and the sectoral

indices of Construction, Consumer Product, Finance, and Industrial. Meanwhile, Table 3.4(b)

reports the estimation results for the sectoral indices of Industrial Product, Mining,

Plantation, Property, Trade and Services, and Technology. The diagnostic test result is

included in the lower part of the tables to support the validity of the models.

Under the mean equation, the dummy coefficients are all insignificant. The high p-

value of dummy coefficient indicates insignificant stock returns for both the pre-general

election and post-general election periods. The finding of insignificant abnormal return

around election period is consistent with the studies of Lean (2010) and Lean and Yeap

(2017). In term of control variables, the dummy coefficients of the MSCI World Index for the

mean equation are all positive and significant at 1%. The results indicate that the Malaysian

stock market returns are strongly affected by global market environment.

57

The estimation results of the variance equations are also presented in Table 3.4(a) and

Table 3.4(b). For the variance equation, the pre-general election dummy coefficients for eight

out of ten sectoral indices are positive and highly significant. These eight sectoral indices of

Construction, Consumer Product, Finance, Industrial, Industrial Product, Property, Trade and

Services, and Technology experienced significant high volatility in pre-general election

periods. Besides, significant low volatility is found in the sectoral index of Mining during the

pre-general election periods. The plantation is the only sector with the insignificant result.

Thus, the results of Threshold GARCH estimation on the pre-general election period show

the existence of significant pre-general election effect in stock volatility in eight out of ten

sectoral indices in the Malaysian stock market. Meanwhile, for the period of the post-general

election, the dummy coefficients of the variance equations are positive and significant for the

Construction, Plantation, and Technology sectoral indices.

The leverage effect term, , in the variance equation is positive and statistically

different from zero for all the sectoral indices. The positive value of indicates that the

leverage effect in bad news increases the volatility. In particular, the bad news has an impact

of ( i ), while good news has an impact of ( i ) only. For example, refer to Table 3.4(a),

bad news in the Construction sectoral index has an impact of 0.9682 (0.8926 + 0.0756), while

good news has an impact of 0.8926 only. Hence, the results indicate the existence of the

asymmetric effect on stock volatility in all ten sectoral stock indices of Malaysian stock

market.

[Insert Table 3.4(a): Threshold GARCH Results for Pre-General Election and Post-

General Election (1994 - 2015) - Controlled by World Market Effect]

58

[Insert Table 3.4(b): Threshold GARCH Results for Pre-General Election and Post-


Next, this study examines the presence of pre-general election effect and post-general

election effect in the sectoral indices for the first sub-sample period of 1994-2005 and the

results are presented in Table 3.5(a) and Table 3.5(b). For the stock return, the dummy

coefficients for the mean equations of the pre-general election are significantly positive for

two out of ten sectoral indices, which are Construction and Industrial Product. These two

sectoral indices recorded significant positive return before the general election. On the other

hand, for post-general election, the dummy coefficients are significantly negative for

Technology sectoral index. The general election has negatively impacted this sector. Overall,

the results indicate that the election effect in stock return only exists in certain sectors in the

Malaysian stock market. From the dummy coefficients of the control variables, it is evident

by the positive and significant coefficients that the Malaysian stock market returns are

positively impacted by the MSCI stock return.

Furthermore, the estimation results of the variance equations with control variables

are also presented in Table 3.5(a) and Table 3.5(b). For the sub-sample period of 1994-2005,

the results are consistent among the sectoral indices, compare to the results of the full-sample

period. Among the ten sectoral indices, eight of them experienced significant volatility

change before and after the general election. In particular, the sectoral indices of

Construction, Finance, Industrial Product, Mining, Plantation, Property, and Trade and

Services experienced significant low volatility before the general election. However, after the

announcement of the election result, the stock volatility increased significantly in these seven

sectoral indices. For the sector of Construction, this sector recorded significant low volatility

59

after the general election. Thus, it is evident that most of the sectoral indices in the Malaysian

stock market experienced significant volatility change due to the general election.

Meanwhile, no significant result is found for the sectoral indices of Consumer Product.

The results of variance equations also confirm that there is an asymmetric effect of

political elections on stock volatility for the sub-sample period of 1994-2005. The positive

value of the leverage effect term is statistically significant, and this indicates the existence of

asymmetrical effect in the Malaysian stock market. This finding implies that negative shocks

or bad news from the election have larger impact on stock volatility than good news in the

sub-sample period of 1994-2005. Lastly, the validity of the model is checked by the

diagnostic tests. No remaining ARCH effect and serial correlation are found in most of the

estimated models.





For the second sub-sample period of 2006-2015, Table 3.6(a) present the results of the

pre-general election and post-general election effect for the FTSE Bursa Malaysia KLCI

index and the sectoral indices of Construction, Consumer Product, Finance, and Industrial,

while Table 3.6(b) reports the estimation results for the sectoral indices of Industrial Product,

Mining, Plantation, Property, Trade and Services, and Technology. From the estimations of

mean equations, the sectoral index of Consumer Product and Mining are the only two indices

60

that show the significant result for the period of pre-general election and post-general

election. The Mining index has a negative and significant return during the period of the pre-

general election, while the Consumer Product sectoral index has a positive and significant

return during the period of post-general election. The finding indicates that the general

election result brought a negative impact to the Mining sector and a positive impact on the

Consumer Product sector. Besides, the dummy coefficients of the MSCI World Index for the

mean equations are positive and significant at 1% for all the sectoral indices. The positive

sign of the dummy coefficient indicates that the global index has a positive impact on the

Malaysian sectoral indices.

As explained earlier, the political condition in the 12th and 13th Malaysia general

elections were different with previous general elections due to the close fight between the two

major coalition. Prior to the general election, the market condition experienced significant

volatility change as supported by the empirical results of this study. From the estimation

results of the Threshold GARCH variance equations, six out of ten of the sectoral indices

encountered significant high volatility in pre-general election periods. The Mining sectoral

index is the only one which recorded significant low volatility during the period. On the other

hand, this study also finds evidence on post-general election effect in stock volatility. The

results of post-general election show insignificant low volatility in the sectoral indices of

Construction, Consumer Product, Industrial, Mining, Plantation, Property, and Trade and

Services. Meanwhile, the Technology sectoral index is the only sector with significant high

volatility in the post-general election period. The result on the second sub-sample period of

2006-2015 is clearly different between the first sub-sample period which covers the 9th, 10th

and 11th Malaysia general elections, where most of the sectoral indices recorded significant

low volatility before general elections and significant high volatility after general elections.

61

The asymmetric effect of the general election is also reported in Table 3.6(a) and

Table 3.6(b). The significant asymmetry coefficient ( ) strongly supports the asymmetric

effect in most of the indices. The leverage effect term, , is statistically different from zero

for all the indices, indicating the existence of the asymmetrical stock returns in the

Malaysian. Besides, the validity of the model is supported by the diagnostic test with no

remaining ARCH effect and serial correlation in all of the estimated models.





3.5 Extensions and Robustness

In order to test the robustness of the model, this study extends the analysis by using the

lagged value of MSCI Emerging Market Index ( 1tREM ) return as an alternative control

variable to test the impact of emerging market on Malaysian stock market returns for all the

three sample periods. The full-sample period results from the model with control variable

( 1tREM ) are presented in Table 3.7(a) and Table 3.7(b). The results for the first sub-sample

period of 1994-2005 and the second sub-sample period of 2006-2015 are shown in Table

3.8(a) and Table 3.8(b), and Table 3.9(a) and Table 3.9(b), respectively. Similar to the

previous models with World Market Index as the control variable, the dummy coefficients of

the MSCI Emerging Market Index in the mean equation are all positive and significant at 1%

for all the three sample periods. The results indicate that the Malaysian stock market returns

62

are strongly affected by emerging market environment as well. It also implies the robustness

of the models after control for external factor, either for world market effect or emerging

market effect. Nevertheless, in term of stock market volatility, there are a few differences in

the estimation result by comparing the models with control variable of World Market Index

to the models with MSCI Emerging Market Index. The differences mainly occur in the first

sample period of 1994-2005.

In particular, for the full sample period, the pre-general election dummy coefficient

for the sector of Consumer Product becomes insignificant after controlling for the emerging

market effect. Previously, this dummy coefficient is significant at 10% for the model

controlled by World Market Index as presented in Table 3.4(a). On the other hand, the stock

index of the Property sector showed a significant higher volatility in post-general election

periods. Similar to the result presented in Table 3.4(b), the Property stock index also has a

positive and significant dummy coefficient in the pre-general election periods. This implies

that the Property sector is sensitive to the influence of general election after controlled for

external factor. The rest of the sectors have similar results for the mean equation and variance

equation.

Overall, as presented in Table 3.7(a) and 3.7(b), the dummy coefficients for the mean

equation are all insignificant for both the pre-general election and post-general election

periods from the year 1994-2015. For the variance equation, the pre-general election dummy

coefficients for seven out of ten sectoral indices are positive and highly significant. This

implies that the seven sectoral indices of Construction, Finance, Industrial, Industrial Product,

Property, Trade and Services, and Technology showed significant high volatility in pre-

general election periods. Besides, significant low volatility is found in the sectoral index of

63

Mining during the pre-general election periods. The Plantation is the only sector with an

insignificant result. Thus, the results of Threshold GARCH estimation on the pre-general

election period show the existence of significant pre-general election effect in stock volatility

in seven out of ten sectoral indices in the Malaysian stock market. Meanwhile, for the period

of the post-general election, the dummy coefficients of the variance equations are positive

and significant for the Construction, Plantation, Property, and Technology sectoral indices.

The leverage effect term, , in the variance equation is positive and statistically different

from zero for all the sectoral indices. The positive value of indicates that the leverage

effect in bad news increases the volatility. In particular, the bad news has an impact of

( i ), while good news has an impact of ( i ) only. Hence, the results indicate the

existence of the asymmetric effect on stock volatility in all ten sectoral stock indices of



General Election (1994 - 2015) - Controlled by Emerging Market Effect]



Turning to the first sub-sample period of 1994-2005, changes happened in the sector

of Property, Construction, and Industrial Product after controlling for emerging market effect.

As shown in Table 3.8(b), a significant result is found in the mean equation where the

Property sectoral stock index has a significant positive return before the general election and

significant negative return after the general election. While controlling for world market

effect before this, there is no significant result found in the Property sector mean return. This

64

result further implies that the stock index of Property sector is sensitive to the influence of

general election after controlled for emerging market effect. On the other hand, the pre-

general election dummy coefficient for the sector of Construction and Industrial Production

becomes insignificant after controlling for the emerging market effect. Previously, with the

control variable of World Market Index, the estimated model has a significant dummy

coefficient on the pre-general election for the sector of Construction (significant level at 5%)

and Industrial Product (significant level at 10%) as presented in Table 3.5(a) & 3.5(b). Also,

in the variance equation of Industrial Product, the dummy coefficient for the pre-general

election remains significant at 10%, but the significant high volatility in the post-general

election no longer exist in the model controlled by emerging market effect.

The full result for the first sub-sample period of 1994-2005, Table 3.8(a) and Table

3.8(b) are described as follow. For the stock return, significant results are only found in the

sectoral indices of Property and Technology. The Property sectoral index has a significant

positive dummy coefficient for the pre-general election period and a significant negative

dummy coefficient for the post-general election period. The Technology sectoral index also

has a significant negative dummy coefficient for the post-general election period. Hence, it is

evident that the outcome of the general election has negatively impacted these two sectors.

Furthermore, the estimation results of the variance equations are consistent among the

sectoral indices, compared to the results of the full-sample period. Among the ten sectoral

indices, six of them experienced significant volatility change before and after the general

election. In particular, before the general election, the sectoral indices of Construction,

Finance, Mining, Plantation, Property, and Trade and Services experienced significant low

volatility. However, after the announcement of the election result, the stock volatility

increased significantly in the six sectoral indices. The sector of the Industrial product also has

65

a significant negative dummy coefficient in the pre-general election period but no significant

result is found in the post-general election period. Moreover, the outcome of the general

election also led to a high volatility in the Technology sector. The Industrial sector is the only

sector that recorded significant high volatility before the general election. Meanwhile, no

significant result is found for the sectoral indices of Consumer Product. The results of the

variance equations also confirm that there is an asymmetric effect of political elections on

stock volatility. The positive value of the leverage effect term is statistically significant and

this indicates the existence of asymmetrical effect in the Malaysian stock market. This

finding implies that negative shocks or bad news from the election have larger impact on

stock volatility than good news. Lastly, the validity of the model is checked by the diagnostic

tests. No remaining ARCH effect and serial correlation are found in most of the estimated

models.





For the second sub-sample period of 2006-2015, the only change in the result comes

from the sector of Consumer Product in the variance equation. The post-general election

dummy coefficient for the sector becomes significant after controlled for the emerging

market effect as presented in Table 3.9(a). This implies that the Consumer Product sector has

a significant low volatility after controlling for external factor. The results for the rest of the

66

sectors are consistent with the main analysis, for both the mean equation and variance

equation.

Table 3.9(a) and 3.9(b) present the full results for the second sub-sample period of

2006-2015. From the estimation of mean equations, the sectoral index of Consumer Product

and Mining are the only two indices that show a significant result. The Mining index has a

negative and significant return in the period of the pre-general election, while the Consumer

Product sectoral index has a positive and significant return in the period of post-general

election. As explained earlier, the political condition in the 12th and 13th Malaysia general

elections were different with previous general elections due to the fierce challenge between

the opposition and the incumbent. Prior to the general election, the market condition

experienced significant volatility change as supported by the empirical results of this study.

From the estimation results of the Threshold GARCH variance equations, six out of ten of the

sectoral indices encountered significant high volatility in pre-general election periods. The

Mining sectoral index is the only one which recorded significant low volatility during the

period. On the other hand, this study also finds evidence on post-general election effect in

stock volatility. The results of post-general election show insignificant low volatility in the

sectoral indices of Construction, Industrial, Mining, Property, and Trade and Services.

Meanwhile, the Technology sectoral index is the only sector with significant high volatility in

the post-general election period. On the other hand, Consumer Product recorded significant

low volatility during the post-general election period. The asymmetric effect of the general

election is also reported in Table 3.9(a) and Table 3.9(b). The significant asymmetry

coefficient ( ) strongly supports the asymmetric effect in most of the indices. The leverage

effect term, , is statistically different from zero for all the indices, indicating the existence

of the asymmetrical stock returns in the Malaysian. Besides, the validity of the model is

67

supported by the diagnostic test with no remaining ARCH effect and serial correlation in all

of the estimated models.





Next, to consider the possibility of other effects, this study extends the analysis by

using the control variables of VIX ( 1 tVIX ) to measure the market uncertainty and U.S.

Federal Fund Rate ( 1 tFFR ) for interest rate differentials. Higher volatility in the U.S. stocks

could affect the expectations about the future monetary policy stances of major central banks,

resulting in shifts of capital out from the U.S. and into Malaysia stock market. Furthermore,

international investors might take the interest rate differentials opportunity, to borrow in

currencies with low-interest rates and invest in a potential growth market, such as Malaysia,

to gain some better returns. From the findings, the VIX exhibits some degree of predictability

in the sense that the lagged variable of VIX is statistically significant in the empirical

analyses. However, both the control variables do not qualitatively change the main results.4

3.6 Conclusion

This study empirically examines the behavior of the Malaysian stock return and volatility

using the Threshold GARCH model for the period of 4 January 1994 to 31 December 2015.

4 Results of the additional control variables (VIX and US Federal Fund Rate) are not included for brevity. However, all results pertaining to this section are available at the Appendix 3.1(a) and (b) to 3.6(a) and (b).

68

From the perspective of behavioral finance, it is worthwhile to analyze the investor's behavior

before and after the general election in a socially collective market. Beside the full sample

period, this study divides the five general election periods into two stages. The first sub-

sample covers the 9th, 10th and 11th Malaysia general election from 1994 to 2005. This period

represents the general ups and downs periods where the existing parties continued to win 2/3

majority seats. The second sub-sample period represents drastic shock periods during the 12th

and 13th Malaysia general election, from 2006 to 2015. Interestingly, the finding of the first

sub-sample period is obviously different from the second sub-sample period.

For the first sub-sample period of 1994 to 2005, there is an asymmetric effect of

political elections on stock volatility. Moreover, there is a significant pre-general election

effect in the sectoral indices of Construction and Industrial Product. These two sectoral

indices had a significant positive return associated with low volatility before the general

election. Another five sectoral indices also recorded significant low stock volatility prior

general election, but no significant election effect in term of stock returns. The low volatility

in the market before the election is a good sign to indicate that there is no uncertainty due to

the general election. After the general election, there are seven sectoral indices encountered

significant high volatility. Even though there were no unexpected outcomes as the coalition

of Barisan Nasional won in the general elections, the stock volatility increased significantly

during the period of post-general election. Looking at the stable political condition at that

election year, the high volatility is not induced by the uncertainty of the general elections.

Nevertheless, it is possibly due to active trading activity in the market right after the election.

Investors were highly confident with the stable political condition in the country and they

started to trade actively after the market reopened after election dates.

69

For the period of 2006 to 2015, the results of the second sub-sample confirm the

asymmetric effect of pre-general election and post-general election periods on stock

volatility. Prior to the general election, most of the sectoral indices were highly volatile,

except for the Mining sectoral index with low volatility. The pre-general election results are

consistent with Lean and Yeap (2017), who found that volatility of the FTSE KLCI index

reacts positively before the election. According to the political condition during that period,

the high volatility in the market was due to uncertainties associated with the general election.

However, after the election, most of the sectoral indices results are insignificant. The sectoral

index of Technology is the only one that influences by the political uncertainties and shows

significant high volatility in the post-general election periods.

The examination of the Malaysian stock market performance by sector illustrates the impact

of general elections more precisely. Generally, the results of the selected sectoral indices are

in line with the sensitivity of industry type as mentioned in Tuyon and Ahmad (2016). The

cyclical sector of Construction, Finance, Mining, and Property are more sensitive to the

market condition with significant result found in stock volatility. While Consumer Product is

a defensive sector where the estimated results are mostly insignificant. Moreover, the results

also show that the volatility of the Malaysian stock market during the 12th and 13th general

election are different from the previous general election. Notably, while volatility on the

stock market return is low during the pre-general election periods of 1994-2005, it did show

its negative and significant influence in the 2008 and 2013 election years. The results of this

study clearly show that the election effect is different in the two sub-sample periods.

Therefore, future studies in this area should be caution in grouping the general election

periods. Furthermore, the results of the extension by using the emerging market index as an

alternative control variable, however, are very similar to the results of the main analysis.

70

Hence, the findings imply that the Threshold GARCH model used in this study is completely

robust after the model taking into consideration for few external factors.

Overall, the analysis results indicate that the Malaysian stock market volatility is

associated with the investors' behaviour during the periods of the general election. The

possible rationale is that whenever the political condition is stable in a country and investors

feel optimistic about the future of the economy under the ruling politic party, willingness to

trade in the stock market is higher. On the contrary, whenever there is political uncertainty,

interest to trade is much lower in the market. Therefore, this study is of great importance to

risk managers, portfolio managers, policymakers, and market participants to understand the

volatility in the Malaysian stock market during general election years. Thus, the results of this

study perhaps provide an insight for investors in adjusting their portfolio around the next

general election. Future work in this area can proceed in several directions. First, microdata

on investors' personal investment choices can be used to study their influence on stock

market performance during the general election. Second, future study can be conducted to

compare the market performance of different stocks characteristics to evaluate the volatility

during the general election.

71

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Table 3.1: Malaysia General Election Information

Dissolution of Parliament Election Date and Day 1st Parliament Assembly

after Election 9th General

Election 6 April 1995 (Thursday)

25 April 1995 (Tuesday)

7 June 1995 (Wednesday)


11 November 1999 (Wednesday)

29 November 1999 (Monday)

20 December 1999 (Monday)


4 March 2004 (Thursday)

21 March 2004 (Sunday)

17 May 2004 (Monday)







5 May 2013 (Sunday)


Sources: Suruhanjaya Pilihan Raya, Election Report, various years.

Table 3.2: Descriptive Statistics for the Malaysian Sectoral Indices (1994 - 2015)

KLCI CONST CONPR FIN IND INDPRO MNG PLANT PROP TRAD TECH

Mean 0.0047 -0.0075 0.0167 0.0084 0.0085 -0.0062 -0.0096 0.0120 -0.0185 -0.0003 -0.0446

Max 20.8174 23.9197 16.1281 22.6276 17.2483 18.9714 52.0143 16.9362 20.9022 22.3703 11.3668

Min -24.1534 -22.7828 -16.4773 -20.5651 -22.6965 -24.7880 -42.0379 -16.6592 -18.9174 -21.0987 -13.3861

Std. Dev. 1.3097 1.7787 1.0439 1.4683 1.2145 1.3035 2.9459 1.3692 1.5963 1.3945 1.5378

Skewness 0.4731 0.6526 0.1895 1.2226 -0.1577 -0.7173 0.7910 -0.2772 0.5177 0.8819 -0.0574

Kurtosis 58.5326 33.3929 51.6275 39.4080 54.3015 49.8949 46.6704 29.3345 24.8775 43.0376 11.2884

Jarque-Bera 737515.40 221254.80 565378.40 318344.50 629254.30 526268.20 456555.10 165878.50 114687.10 383997.20 11678.11

Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Note: KLCI: FTSE Bursa Malaysia KLCI Index, CONST: Construction, CONPR: Consumer Product, FIN: Finance, IND: Industrial, INDPRO: Industrial Product, MNG: Mining, PLANT: Plantation, PROP: Property, TRAD: Trade and Services, TECH: Technology (TECH data only available since May 15, 2000).

76

Table 3.3: Mean Returns on Pre-General Election and Post-General Election

1994 - 2005 KLCI CONST CONPR FIN IND INDPRO MNG PLANT PROP TRAD TECH

PreGE-Mean 0.0762 0.0167 0.0157 0.0311 -0.0297 0.0261 0.1972 0.0663 -0.0425 0.0586 0.1484

Observations 39.0000 39.0000 39.0000 39.0000 39.0000 39.0000 39.0000 39.0000 39.0000 39.0000 12.0000

PostGE-

Mean -0.0123 -0.1253 0.0617 -0.0205 0.0363 -0.0250 -0.0380 -0.0899 -0.1742 -0.0055 -0.5105

Observations 87.0000 87.0000 87.0000 87.0000 87.0000 87.0000 87.0000 87.0000 87.0000 87.0000 41.0000

2006 - 2015 KLCI CONST CONPR FIN IND INDPRO MNG PLANT PROP TRADSER TECH

PreGE-Mean -0.2039 -0.4886 -0.1524 -0.1920 -0.2428 -0.1513 -0.5291 -0.1223 -0.3323 -0.2182 -0.1881

Observations 41.0000 41.0000 41.0000 41.0000 41.0000 41.0000 41.0000 41.0000 41.0000 41.0000 41.0000

PostGE-

Mean 0.0340 0.0688 0.1195 0.0787 0.0311 0.1137 0.1909 0.0700 0.0765 0.0406 0.2037

Observations 72.0000 72.0000 72.0000 72.0000 72.0000 72.0000 72.0000 72.0000 72.0000 72.0000 72.0000

Notes: KLCI: FTSE Bursa Malaysia KLCI Index, CONST: Construction, CONPR: Consumer Product, FIN: Finance, IND: Industrial, INDPRO: Industrial Product, MNG: Mining, PLANT: Plantation, PROP: Property, TRAD: Trade and Services, TECH: Technology. Pre-General Election: start from Dissolution of Parliament to the day before General Election, and Post-General Election: start from Day after the General Election to the first day of the Parliament Assembly.

77


Variables KLCI Construction Consumer

Product Finance Industrial

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0081 (0.3549)

-0.0027 (0.8461)

0.0205 (0.0080)***

0.0153 (0.1446)

0.0141 (0.1412)

PGE 0.1226 (0.2740)

0.1401 (0.5256)

-0.0157 (0.8686)

0.0571 (0.7352)

-0.0269 (0.7994)

PtGE -0.0783 (0.2195)

-0.2170 (0.1263)

0.0083 (0.8624)

-0.0704 (0.4133)

-0.0658 (0.3164)

1tR 0.0925 (0.0000)***

0.0721 (0.0000)***

0.0645 (0.0000)***

0.1139 (0.0000)***

0.0354 (0.0079)***

1tRWM 0.2244 (0.0000)***

0.2774 (0.0000)***

0.1603 (0.0000)***

0.2396 (0.0000)***

0.1942 (0.0000)***

Variance Equation

0 0.0057 (0.0000)***

0.0218 (0.0000)***

0.0040 (0.0000)***

0.0068 (0.0000)***

0.0069 (0.0000)***

1 0.0538 (0.0000)***

0.0663 (0.0000)***

0.0468 (0.0000)***

0.0624 (0.0000)***

0.0446 (0.0000)***

i 0.0665 (0.0000)***

0.0756 (0.0000)***

0.0377 (0.0000)***

0.0458 (0.0000)***

0.0512 (0.0000)***

1 0.9106 (0.0000)***

0.8926 (0.0000)***

0.9299 (0.0000)***

0.9132 (0.0000)***

0.9240 (0.0000)***

PGE 0.0479 (0.0041)***

0.2568 (0.0000)***

0.0090 (0.0801)*

0.0746 (0.0000)***

0.0529 (0.0001)***

PtGE 0.0006 (0.9093)

0.0224 (0.0615)*

0.0005 (0.8230)

0.0011 (0.8850)

-0.0006 (0.9013)


5 lags 0.2865 0.5556 0.2209 0.0023 0.9160 10 lags 0.3826 0.4533 0.3500 0.0057 0.9617

Ljung-Box Q2 Statistic (p-value) 5 lags 0.2840 0.5500 0.2070 0.0020 0.9170

10 lags 0.3370 0.4010 0.3030 0.0030 0.9610 Return Equation: Wald Test (p-value)

F-stat 0.1405 0.1426 0.9704 0.6149 0.6031 Chi-Square 0.1404 0.1425 0.9704 0.6149 0.6030

Variance Equation: Wald Test (p-value) F-stat 0.0108 0.0000 0.1791 0.0001 0.0003

Chi-Square 0.0108 0.0000 0.1790 0.0001 0.0002 Note: ***, ** and * denote significance at 1, 5 and 10% levels respectively. Numbers in parentheses depict p-value. The null hypothesis of the Wald Test is 0: 210 H (average daily returns (volatility) for the period of pre-general election and post-general election are significant different from zero).

78


Variables Industrial

Product Mining Plantation Property Trade and

Services Technology

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0039 (0.6898)

0.0378 (0.2308)

0.0142 (0.2255)

-0.0172 (0.1422)

0.0044 (0.6346)

-0.0380 (0.0350)**

PGE 0.0798 (0.5014)

-0.2898 (0.2649)

0.0701 (0.5091)

0.1208 (0.5663)

0.1329 (0.2989)

0.0072 (0.9684)

PtGE -0.0117 (0.8482)

-0.1277 (0.4857)

-0.0055 (0.9471)

-0.1226 (0.2179)

-0.0915 (0.2112)

-0.0982 (0.4705)

1tR 0.0696 (0.0000)***

-0.0612 (0.0000)***

0.1047 (0.0000)***

0.1242 (0.0000)***

0.0572 (0.0000)***

0.1189 (0.0000)***

1tRWM 0.2068 (0.0000)***

0.3195 (0.0000)***

0.1948 (0.0000)***

0.2213 (0.0000)***

0.2173 (0.0000)***

0.2330 (0.0000)***

Variance Equation

0 0.0139 (0.0000)***

0.4406 (0.0000)***

0.0208 (0.0000)***

0.0157 (0.0000)***

0.0048 (0.0000)***

0.0343 (0.0000)***

1 0.0798 (0.0000)***

0.1244 (0.0000)***

0.0894 (0.0000)***

0.1172 (0.0000)***

0.0483 (0.0000)***

0.0794 (0.0000)***

i 0.0790 (0.0000)***

0.1302 (0.0000)***

0.0450 (0.0000)***

0.0285 (0.0000)***

0.0758 (0.0000)***

0.0206 (0.0046)***

1 0.8742 (0.0000)***

0.7855 (0.0000)***

0.8765 (0.0000)***

0.8698 (0.0000)***

0.9144 (0.0000)***

0.8964 (0.0000)***

PGE 0.0486 (0.0143)**

-0.2188 (0.0012)***

0.0002 (0.9893)

0.2440 (0.0000)***

0.0645 (0.0020)***

0.0631 (0.0071)***

PtGE 0.0135 (0.1264)

0.0462 (0.6804)

0.0312 (0.0143)**

0.0170 (0.1021)

-0.0034 (0.5671)

0.0698 (0.0000)***


5 lags 0.5444 0.9999 0.0154 0.4935 0.4044 0.0120 10 lags 0.6203 0.9996 0.0252 0.6618 0.4799 0.0565

Ljung-Box Q2 Statistic (p-value) 5 lags 0.5430 1.0000 0.0160 0.4810 0.4140 0.0120 10 lags 0.5900 1.0000 0.0190 0.6290 0.4540 0.0480

Return Equation: Wald Test (p-value) F-stat 0.7502 0.4159 0.8025 0.2827 0.1349 0.7707

Chi-Square 0.7502 0.4158 0.8025 0.2826 0.1348 0.7707 Variance Equation: Wald Test (p-value)

F-stat 0.0037 0.0053 0.0433 0.0000 0.0083 0.0000 Chi-Square 0.0037 0.0053 0.0432 0.0000 0.0083 0.0000

Note: ***, ** and * denote significance at 1, 5 and 10% levels respectively. Numbers in parentheses depict p-value. The null hypothesis of the Wald Test is 0: 210 H (average daily returns (volatility) for the period of pre-general election and post-general election are significant different from zero).

79




(p, q) (1, 1) (1, 0) (1, 1) (1, 1) (0, 1) Mean Equation

0 -0.0089 (0.5590)

-0.0849 (0.0118)**

0.0075 (0.5510)

0.0032 (0.8662)

0.0149 (0.3229)

PGE 0.1812 (0.3122)

0.2991 (0.0267)**

0.0973 (0.5496)

0.1125 (0.5832)

-0.0169 (0.9482)

PtGE -0.1225 (0.2268)

-0.0545 (0.7923)

-0.0566 (0.3920)

-0.1914 (0.1898)

-0.1189 (0.2530)

1tR 0.1274 (0.0000)***

0.0093 (0.3114)

0.1221 (0.0000)***

0.1534 (0.0000)***

0.0585 (0.0001)***

1tRWM 0.2353 (0.0000)***

0.4677 (0.0000)***

0.1448 (0.0000)***

0.2879 (0.0000)***

0.1723 (0.0000)***

Variance Equation

0 0.0088 (0.0000)***

2.4997 (0.0000)***

0.0042 (0.0000)***

0.0156 (0.0000)***

0.0057 (0.0000)***

1 0.0464 (0.0000)***

0.3265 (0.0000)***

0.0306 (0.0000)***

0.0584 (0.0000)***

-- --

i 0.0752 (0.0000)***

0.1947 (0.0000)***

0.0438 (0.0000)***

0.0559 (0.0000)***

0.0745 (0.0000)***

1 0.9152 (0.0000)***

-- --

0.9448 (0.0000)***

0.9104 (0.0000)***

0.9583 (0.0000)***

PGE -0.0585 (0.0503)*

-2.0862 (0.0000)***

-0.0035 (0.7338)

-0.0785 (0.0751)*

0.0885 (0.0034)***

PtGE 0.0437 (0.0075)***

-0.6282 (0.0000)***

0.0030 (0.6081)

0.0900 (0.0000)***

-0.0134 (0.1883)


5 lags 0.1877 0.0000 0.2209 0.0206 0.0000 10 lags 0.4197 0.0000 0.5794 0.0355 0.0000



F-stat 0.3209 0.0806 0.6099 0.4005 0.5157 Chi-Square 0.3208 0.0805 0.6099 0.4004 0.5156



80




Services Technology

(p, q) (1, 1) (1, 1) (0, 1) (1, 0) (1, 1) (1, 1) Mean Equation

0 -0.0333 (0.0275)**

-0.0161 (0.6811)

0.0095 (0.6016)

-0.0716 (0.0039)***

-0.0179 (0.3017)

-0.1027 (0.0015)***

PGE 0.2455 (0.0063)***

0.2758 (0.3284)

0.1677 (0.2937)

0.3339 (0.1204)

0.2351 (0.1899)

0.2444 (0.1025)

PtGE -0.0793 (0.4388)

-0.3505 (0.1631)

-0.0831 (0.3978)

-0.0752 (0.7756)

-0.1272 (0.1648)

-0.3136 (0.0075)***

1tR 0.0922 (0.0000)***

0.0362 (0.0476)**

0.1282 (0.0000)***

0.2193 (0.0000)***

0.0921 (0.0000)***

0.1384 (0.0000)***

1tRWM 0.2050 (0.0000)***

0.2401 (0.0000)***

0.1575 (0.0000)***

0.3476 (0.0000)***

0.2465 (0.0000)***

0.3884 (0.0000)***

Variance Equation

0 0.0136 (0.0000)***

0.1604 (0.0000)***

0.0199 (0.0000)***

1.6640 (0.0000)***

0.0071 (0.0000)***

-0.0003 (0.7331)

1 0.0788 (0.0000)***

0.0953 (0.0000)***

-- --

0.5432 (0.0000)***

0.0428 (0.0000)***

0.0045 (0.0826)*

i 0.1046 (0.0000)***

0.0845 (0.0000)***

0.1075 (0.0000)***

0.2151 (0.0002)***

0.0797 (0.0000)***

0.0233 (0.0000)***

1 0.8742 (0.0000)***

0.8548 (0.0000)***

0.9320 (0.0000)***

-- --

0.9204 (0.0000)***

0.9833 (0.0000)***

PGE -0.0630 (0.0000)***

-0.3728 (0.0001)***

-0.0409 (0.0556)*

-0.9755 (0.0000)***

-0.0896 (0.0002)***

-0.0296 (0.1211)

PtGE 0.0646 (0.0021)***

0.2623 (0.0214)**

0.0297 (0.0171)**

0.9104 (0.0000)***

0.0332 (0.0152)**

0.0174 (0.0017)***


5 lags 0.5556 0.3317 0.0000 0.0000 0.0869 0.0000 10 lags 0.8104 0.5875 0.0000 0.0000 0.2191 0.0000




F-stat 0.0000 0.0005 0.0352 0.0000 0.0004 0.0061 Chi-Square 0.0000 0.0005 0.0350 0.0000 0.0004 0.0060


81




(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0216 (0.0429)**

0.0227 (0.1886)

0.0364 (0.0004)***

0.0282 (0.0232)**

0.0145 (0.2494)

PGE 0.1135 (0.5588)

-0.1558 (0.8012)

-0.0750 (0.4723)

0.0221 (0.9340)

-0.0107 (0.9455)

PtGE -0.0542 (0.6658)

-0.1594 (0.3982)

0.1355 (0.0356)**

-0.0131 (0.9162)

-0.0477 (0.7123)

1tR 0.0567 (0.0052)***

0.0406 (0.0584)*

-0.0016 (0.9402)

0.0683 (0.0011)***

0.0244 (0.2295)

1tRWM 0.2230 (0.0000)***

0.2583 (0.0000)***

0.1620 (0.0000)***

0.2280 (0.0000)***

0.2046 (0.0000)***

Variance Equation

0 0.0120 (0.0000)***

0.0324 (0.0000)***

0.0209 (0.0000)***

0.0205 (0.0000)***

0.0141 (0.0000)***

1 0.0723 (0.0000)***

0.0977 (0.0000)***

0.0966 (0.0000)***

0.1086 (0.0000)***

0.0349 (0.0001)***

i 0.0748 (0.0000)***

0.0697 (0.0000)***

0.0657 (0.0000)***

0.0652 (0.0001)***

0.0745 (0.0000)***

1 0.8627 (0.0000)***

0.8454 (0.0000)***

0.8077 (0.0000)***

0.8233 (0.0000)***

0.9015 (0.0000)***

PGE 0.1139 (0.0092)***

0.9667 (0.0000)***

0.0179 (0.1869)

0.1745 (0.0004)***

0.0898 (0.0015)***

PtGE 0.0008 (0.9040)

-0.0365 (0.2144)

-0.0073 (0.1172)

0.0048 (0.6069)

-0.0026 (0.6623)


5 lags 0.9859 0.9655 0.7149 0.7217 0.9640 10 lags 0.5712 0.9110 0.4356 0.7667 0.9958



F-stat 0.6819 0.6947 0.0813 0.9870 0.9342 Chi-Square 0.6818 0.6947 0.0811 0.9870 0.9342



82




Services Technology

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0355 (0.0071)***

0.0867 (0.0719)*

0.0199 (0.2303)

0.0225 (0.1431)

0.0170 (0.1217)

0.0055 (0.7993)

PGE -0.1005 (0.6673)

-0.8343 (0.0067)***

0.0062 (0.9701)

-0.3028 (0.4974)

0.0897 (0.7309)

-0.2408 (0.3007)

PtGE 0.0651 (0.5884)

0.0319 (0.9111)

0.0137 (0.9108)

0.0947 (0.6387)

-0.0687 (0.5838)

0.2014 (0.2995)

1tR 0.0395 (0.0607)*

-0.1900 (0.0000)***

0.0906 (0.0000)***

0.0922 (0.0000)***

0.0197 (0.2973)

0.0869 (0.0000)***

1tRWM 0.2099 (0.0000)***

0.3426 (0.0000)***

0.2357 (0.0000)***

0.2151 (0.0000)***

0.2120 (0.0000)***

0.1726 (0.0000)***

Variance Equation

0 0.0230 (0.0000)***

0.8847 (0.0000)***

0.0155 (0.0000)***

0.0215 (0.0000)***

0.0085 (0.0000)***

0.1596 (0.0000)***

1 0.0890 (0.0000)***

0.1666 (0.0000)***

0.0624 (0.0000)***

0.1260 (0.0000)***

0.0551 (0.0000)***

0.1349 (0.0000)***

i 0.0488 (0.0000)***

0.2124 (0.0000)***

0.0301 (0.0001)***

-0.0073 (0.5003)

0.0726 (0.0000)***

0.0341 (0.0402)*

1 0.8459 (0.0000)***

0.6787 (0.0000)***

0.9084 (0.0000)***

0.8561 (0.0000)***

0.8907 (0.0000)***

0.7536 (0.0000)***

PGE 0.1322 (0.0000)***

-0.3065 (0.0561)*

0.0413 (0.1107)

0.5704 (0.0000)***

0.1571 (0.0020)***

0.0502 (0.4033)

PtGE 0.0064 (0.4738)

-0.1674 (0.4616)

-0.0036 (0.8040)

-0.0255 (0.3699)

-0.0105 (0.1309)

0.4001 (0.0000)***


5 lags 0.8076 0.9999 0.1851 0.7353 0.9263 0.8261 10 lags 0.7050 1.0000 0.1459 0.8317 0.6053 0.9279




F-stat 0.0000 0.1144 0.2801 0.0000 0.0054 0.0000 Chi-Square 0.0000 0.1142 0.2799 0.0000 0.0053 0.0000


83




(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0097 (0.2845)

0.0004 (0.9750)

0.0226 (0.0046)***

0.0196 (0.0707)*

0.0126 (0.1965)

PGE 0.1435 (0.1704)

0.1480 (0.4842)

-0.0035 (0.9712)

0.0856 (0.5882)

-0.0150 (0.8794)

PtGE -0.0524 (0.4159)

-0.1957 (0.1763)

0.0229 (0.6326)

-0.0519 (0.5517)

-0.0417 (0.5188)

1tR 0.0663 (0.0000)***

0.0609 (0.0000)***

0.0354 (0.0099)***

0.1011 (0.0000)***

0.0056 (0.6947)

1tEWM 0.1295 (0.0000)***

0.1451 (0.0000)***

0.1048 (0.0000)***

0.1222 (0.0000)***

0.1345 (0.0000)***

Variance Equation

0 0.0064 (0.0000)***

0.0246 (0.0000)***

0.0045 (0.0000)***

0.0075 (0.0000)***

0.0072 (0.0000)***

1 0.0543 (0.0000)***

0.0650 (0.0000)***

0.0492 (0.0000)***

0.0617 (0.0000)***

0.0459 (0.0000)***

i 0.0692 (0.0000)***

0.0778 (0.0000)***

0.0402 (0.0000)***

0.0510 (0.0000)***

0.0534 (0.0000)***

1 0.9082 (0.0000)***

0.8911 (0.0000)***

0.9259 (0.0000)***

0.9112 (0.0000)***

0.9217 (0.0000)***

PGE 0.0400 (0.0083)***

0.2480 (0.0000)***

0.0096 (0.1007)

0.0690 (0.0000)***

0.0495 (0.0002)***

PtGE 0.0047 (0.4197)

0.0286 (0.0210)**

-0.0001 (0.9741)

0.0046 (0.5675)

-0.0007 (0.8982)


5 lags 0.3082 0.4561 0.4210 0.0019 0.9449 10 lags 0.4629 0.4345 0.4863 0.0047 0.9621



F-stat 0.1769 0.1787 0.8904 0.6510 0.8104 Chi-Square 0.1768 0.1786 0.8904 0.6510 0.8104



84

Table 3.7(b): Threshold GARCH Results for Pre-General Election and Post-General

Election (1994 - 2015) - Controlled by Emerging Market Effect

Variables Industrial Product

Mining Plantation Property Trade and Services

Technology

(p, q) (1, 2) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0038 (0.7017)

0.0300 (0.3351)

0.0146 (0.2053)

-0.0129 (0.2822)

0.0059 (0.5325)

-0.0355 (0.0487)**

PGE 0.1135 (0.3057)

-0.2391 (0.3551)

0.1110 (0.2847)

0.1315 (0.5327)

0.1537 (0.1848)

0.0131 (0.9483)

PtGE -0.0054 (0.9282)

-0.1113 (0.5441)

-0.0018 (0.9823)

-0.1177 (0.2554)

-0.0621 (0.4017)

-0.0830 (0.5359)

1tR 0.0427 (0.0061)***

-0.0722 (0.0000)***

0.0726 (0.0000)***

0.1186 (0.0000)***

0.0378 (0.0089)***

0.1133 (0.0000)***

1tEWM 0.1304 (0.0000)***

0.2293 (0.0000)***

0.1561 (0.0000)***

0.1048 (0.0000)***

0.1204 (0.0000)***

0.1001 (0.0000)***

Variance Equation

0 0.0172 (0.0000)***

0.4381 (0.0000)***

0.0219 (0.0000)***

0.0170 (0.0000)***

0.0055 (0.0000)***

0.0376 (0.0000)***

1 0.0925 (0.0000)***

0.1213 (0.0000)***

0.0968 (0.0000)***

0.1155 (0.0000)***

0.0516 (0.0000)***

0.0865 (0.0000)***

i 0.0940 (0.0000)***

0.1238 (0.0000)***

0.0501 (0.0000)***

0.0289 (0.0000)***

0.0779 (0.0000)***

0.0226 (0.0032)***

1 0.6506 (0.0000)***

0.7902 (0.0000)***

0.8673 (0.0000)***

0.8699 (0.0000)***

0.9097 (0.0000)***

0.8881 (0.0000)***

2 0.2012 (0.0320)**

-- --

-- --

-- --

-- --

-- --

PGE 0.0453 (0.0660)*

-0.2445 (0.0003)***

-0.0041 (0.8246)

0.2456 (0.0000)***

0.0567 (0.0033)***

0.0736 (0.0050)***

PtGE 0.0165 (0.1247)

0.0496 (0.6514)

0.0379 (0.0078)***

0.0203 (0.0630)*

0.0002 (0.9715)

0.0693 (0.0000)***


5 lags 0.9847 0.9999 0.0177 0.5028 0.4586 0.0146 10 lags 0.9584 0.9996 0.0271 0.5687 0.5757 0.0763




F-stat 0.0280 0.0014 0.0286 0.0000 0.0122 0.0000 Chi-Square 0.0280 0.0014 0.0286 0.0000 0.0121 0.0000


85




(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (0, 1) Mean Equation

0 -0.0107 (0.5032)

-0.0383 (0.1127)

0.0065 (0.6157)

0.0042 (0.8303)

0.0118 (0.4422)

PGE 0.1759 (0.3341)

0.1348 (0.3223)

0.1019 (0.5300)

0.1192 (0.5280)

-0.0107 (0.9648)

PtGE -0.1300 (0.2418)

-0.2250 (0.1747)

-0.0489 (0.4619)

-0.2013 (0.1824)

-0.0950 (0.3466)

1tR 0.1127 (0.0000)***

0.0957 (0.0000)***

0.1017 (0.0000)***

0.1386 (0.0000)***

0.0404 (0.0110)**

1tEWM 0.1119 (0.0000)***

0.1349 (0.0000)***

0.0979 (0.0000)***

0.1353 (0.0000)***

0.1086 (0.0000)***

Variance Equation

0 0.0101 (0.0000)***

0.0328 (0.0000)***

0.0043 (0.0000)***

0.0178 (0.0000)***

0.0061 (0.0000)***

1 0.0443 (0.0000)***

0.0439 (0.0000)***

0.0305 (0.0000)***

0.0568 (0.0000)***

-- --

i 0.0812 (0.0000)***

0.0803 (0.0000)***

0.0452 (0.0000)***

0.0616 (0.0000)***

0.0798 (0.0000)***

1 0.9135 (0.0000)***

0.9100 (0.0000)***

0.9443 (0.0000)***

0.9085 (0.0000)***

0.9559 (0.0000)***

PGE -0.0571 (0.0631)*

-0.1004 (0.0000)***

-0.0033 (0.7836)

-0.0866 (0.0414)**

0.0639 (0.0341)**

PtGE 0.0509 (0.0049)***

0.1132 (0.0000)***

0.0038 (0.5524)

0.0992 (0.0000)***

-0.0072 (0.5604)


5 lags 0.2852 0.0904 0.3208 0.0156 0.0000 10 lags 0.6075 0.2865 0.6987 0.0323 0.0000



F-stat 0.3558 0.2483 0.6480 0.3705 0.6372 Chi-Square 0.3556 0.2482 0.6480 0.3703 0.6371



86




Services Technology

(p, q) (1, 0) (2, 1) (0, 1) (2, 1) (1, 1) (1, 1) Mean Equation

0 -0.0629 (0.0003)***

-0.0272 (0.4882)

0.0072 (0.6929)

-0.0726 (0.0003)***

-0.0196 (0.2708)

-0.1035 (0.0014)***

PGE 0.1215 (0.3925)

0.3327 (0.2292)

0.1816 (0.2190)

0.3627 (0.0139)**

0.2239 (0.2199)

0.2348 (0.1676)

PtGE 0.0308 (0.8414)

-0.3968 (0.1418)

-0.0811 (0.3829)

-0.1582 (0.0826)*

-0.1283 (0.1853)

-0.3085 (0.0206)**

1tR 0.0933 (0.0000)***

0.0240 (0.2422)

0.1188 (0.0000)***

0.1288 (0.0000)***

0.0779 (0.0000)***

0.1335 (0.0000)***

1tEWM 0.2724 (0.0000)***

0.1459 (0.0000)***

0.0989 (0.0000)***

0.1247 (0.0000)***

0.1209 (0.0000)***

0.1380 (0.0000)***

Variance Equation

0 1.1561 (0.0000)***

0.1163 (0.0000)***

0.0209 (0.0000)***

0.0153 (0.0000)***

0.0082 (0.0000)***

-0.0004 (0.6803)

1 0.3568 (0.0000)***

0.1743 (0.0000)***

-- --

0.1117 (0.0000)***

0.0441 (0.0000)***

0.0050 (0.0587)*

2 -- --

-0.1024 (0.0000)***

-- --

0.0498 (0.0000)***

-- --

-- --

i 0.3589 (0.0000)***

0.0742 (0.0000)***

0.1135 (0.0000)***

0.8747 (0.0000)***

0.0842 (0.0000)***

0.0245 (0.0000)***

1 -- --

0.8858 (0.0000)***

0.9289 (0.0000)***

0.8747 (0.0000)***

0.9167 (0.0000)***

0.9822 (0.0000)***

PGE -0.7475 (0.0000)***

-0.3286 (0.0000)***

-0.0529 (0.0050)***

-0.1001 (0.0001)***

-0.0949 (0.0014)***

-0.0191 (0.3395)

PtGE 0.2915 (0.1050)

0.3250 (0.0008)***

0.0378 (0.0027)***

0.0332 (0.0203)**

0.0445 (0.0072)***

0.0164 (0.0077)***


5 lags 0.0000 0.8807 0.0000 0.0976 0.1348 0.0000 10 lags 0.0000 0.9518 0.0000 0.2458 0.3195 0.0001




F-stat 0.0000 0.0000 0.0018 0.0002 0.0022 0.0186 Chi-Square 0.0000 0.0000 0.0018 0.0002 0.0022 0.0184


87




(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0257 (0.0201)**

0.0297 (0.0889)*

0.0390 (0.0002)***

0.0319 (0.0146)**

0.0138 (0.2867)

PGE 0.1500 (0.3667)

-0.1126 (0.8526)

-0.0533 (0.6090)

0.0674 (0.7731)

-0.0030 (0.9836)

PtGE -0.0263 (0.8270)

-0.1134 (0.5282)

0.1518 (0.0182)**

-0.0093 (0.9424)

-0.0099 (0.9363)

1tR 0.0073 (0.7565)

0.0214 (0.3695)

-0.0468 (0.0404)**

0.0477 (0.0422)**

-0.0235 (0.2804)

1tEWM 0.1466 (0.0000)***

0.1545 (0.0000)***

0.1129 (0.0000)***

0.1283 (0.0000)***

0.1509 (0.0000)***

Variance Equation

0 0.0119 (0.0000)***

0.0347 (0.0000)***

0.0237 (0.0000)***

0.0175 (0.0000)***

0.0133 (0.0000)***

1 0.0726 (0.0000)***

0.1040 (0.0000)***

0.0922 (0.0000)***

0.0919 (0.0000)***

0.0376 (0.0000)***

i 0.0682 (0.0000)***

0.0661 (0.0000)***

0.0752 (0.0000)***

0.0640 (0.0000)***

0.0687 (0.0000)***

1 0.8674 (0.0000)***

0.8404 (0.0000)***

0.8007 (0.0000)***

0.8474 (0.0000)***

0.9039 (0.0000)***

PGE 0.0875 (0.0163)**

0.9875 (0.0000)***

0.0165 (0.2918)

0.1397 (0.0008)***

0.0825 (0.0011)***

PtGE 0.0019 (0.7944)

-0.0431 (0.1994)

-0.0102 (0.0699)*

0.0034 (0.7248)

-0.0038 (0.5531)


5 lags 0.8866 0.9446 0.7148 0.3830 0.9516 10 lags 0.4240 0.7896 0.1422 0.4259 0.9745



F-stat 0.5654 0.8167 0.0522 0.9405 0.9968 Chi-Square 0.5653 0.8167 0.0520 0.9405 0.9968



88

Table 3.9(b): Threshold GARCH Results for Pre-General Election and Post-General

Election (2006 - 2015) - Controlled by Emerging Market Effect



Technology

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0387 (0.0040)***

0.0766 (0.1107)

0.0248 (0.1230)

0.0273 (0.0847)*

0.0205 (0.0690)*

0.0072 (0.7401)

PGE -0.0490 (0.8271)

-0.7956 (0.0160)**

0.0522 (0.7557)

-0.2578 (0.5716)

0.1367 (0.5332)

-0.2316 (0.3433)

PtGE 0.0830 (0.4813)

0.1037 (0.7090)

0.0690 (0.5866)

0.1000 (0.6126)

-0.0289 (0.8137)

0.2073 (0.2732)

1tR 0.0102 (0.6667)

-0.1988 (0.0000)***

0.0342 (0.0815)*

0.0865 (0.0003)***

-0.0164 (0.4638)

0.0778 (0.0004)***

1tEWM 0.1282 (0.0000)***

0.2417 (0.0000)***

0.1975 (0.0000)***

0.1047 (0.0000)***

0.1306 (0.0000)***

0.0911 (0.0000)***

Variance Equation

0 0.0251 (0.0000)***

0.8586 (0.0000)***

0.0174 (0.0000)***

0.0240 (0.0000)***

0.0093 (0.0000)***

0.1587 (0.0000)***

1 0.0860 (0.0000)***

0.1603 (0.0000)***

0.0774 (0.0000)***

0.1228 (0.0000)***

0.0602 (0.0000)***

0.1419 (0.0000)***

i 0.0519 (0.0000)***

0.1862 (0.0000)***

0.0415 (0.0000)***

-0.0005 (0.9646)

0.0716 (0.0000)***

0.0365 (0.0347)**

1 0.8449 (0.0000)***

0.6948 (0.0000)***

0.8887 (0.0000)***

0.8533 (0.0000)***

0.8859 (0.0000)***

0.7490 (0.0000)***

PGE 0.1203 (0.0002)***

-0.3375 (0.0227)**

0.0418 (0.1933)

0.5797 (0.0000)***

0.1366 (0.0044)***

0.0538 (0.3727)

PtGE 0.0074 (0.4373)

-0.1946 (0.3538)

0.0047 (0.8124)

-0.0220 (0.4888)

-0.0097 (0.2209)

0.4124 (0.0000)***


5 lags 0.8945 0.9999 0.2149 0.5430 0.9688 0.7742 10 lags 0.6578 1.0000 0.1351 0.5663 0.5163 0.8774




F-stat 0.0008 0.0436 0.4050 0.0000 0.0131 0.0000 Chi-Square 0.0008 0.0434 0.4049 0.0000 0.0130 0.0000


89




(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0138 (0.1233)

0.0039 (0.7791)

0.0224 (0.0042)***

0.0221 (0.0363)**

0.0177 (0.0657)*

PGE 0.1287 (0.2590)

0.1503 (0.4875)

-0.0014 (0.9882)

0.0786 (0.6451)

-0.0138 (0.8965)

PtGE -0.0821 (0.2254)

-0.2285 (0.1231)

-0.0002 (0.9962)

-0.0771 (0.3936)

-0.0824 (0.2394)

1tR 0.1337 (0.0000)***

0.0972 (0.0000)***

0.0916 (0.0000)***

0.1500 (0.0000)***

0.0629 (0.0000)***

1 tVIX -0.1158 (0.0000)***

-0.1454 (0.0000)***

-0.0869 (0.0000)***

-0.1266 (0.0000)***

-0.1000 (0.0000)***

Variance Equation

0 0.0062 (0.0000)***

0.0242 (0.0000)***

0.0042 (0.0000)***

0.0067 (0.0000)***

0.0072 (0.0000)***

1 0.0541 (0.0000)***

0.0682 (0.0000)***

0.0474 (0.0000)***

0.0635 (0.0000)***

0.0437 (0.0000)***

i 0.0684 (0.0000)***

0.0741 (0.0000)***

0.0368 (0.0000)***

0.0467 (0.0000)***

0.0542 (0.0000)***

1 0.9090 (0.0000)***

0.8898 (0.0000)***

0.9294 (0.0000)***

0.9122 (0.0000)***

0.9232 (0.0000)***

PGE 0.0455 (0.0056)***

0.2468 (0.0000)***

0.0092 (0.0858)*

0.0711 (0.0000)***

0.0523 (0.0002)***

PtGE 0.0031 (0.6106)

0.0289 (0.0205)**

0.0012 (0.6139)

0.0034 (0.6734)

0.0012 (0.8131)


5 lags 0.3783 0.4573 0.2411 0.0040 0.9491 10 lags 0.4933 0.3520 0.4488 0.0055 0.9794



F-stat 0.1345 0.1267 0.9999 0.5398 0.4992 Chi-Square 0.1344 0.1266 0.9999 0.5398 0.4991



90




Services Technology

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0097 (0.3295)

0.0367 (0.2476)

0.0195 (0.1018)

-0.0100 (0.3987)

0.0101 (0.2797)

-0.0308 (0.0889)*

PGE 0.0904 (0.4159)

-0.2323 (0.4009)

0.1022 (0.3370)

0.1351 (0.5271)

0.1399 (0.2712)

0.0190 (0.9219)

PtGE -0.0175 (0.7849)

-0.1029 (0.5820)

-0.0140 (0.8700)

-0.1321 (0.2214)

-0.0939 (0.2062)

-0.1125 (0.4030)

1tR 0.0988 (0.0000)***

-0.0515 (0.0003)***

0.1262 (0.0000)***

0.1488 (0.0000)***

0.0924 (0.0000)***

0.1377 (0.0000)***

1 tVIX -0.1158 (0.0000)***

-0.1734 (0.0000)***

-0.0991 (0.0000)***

-0.1262 (0.0000)***

-0.1152 (0.0000)***

-0.1253 (0.0000)***

Variance Equation

0 0.0150 (0.0000)***

0.4571 (0.0000)***

0.0215 (0.0000)***

0.0164 (0.0000)***

0.0053 (0.0000)***

0.0344 (0.0000)***

1 0.0834 (0.0000)***

0.1280 (0.0000)***

0.0928 (0.0000)***

0.1194 (0.0000)***

0.0498 (0.0000)***

0.0841 (0.0000)***

i 0.0825 (0.0000)***

0.1199 (0.0000)***

0.0422 (0.0000)***

0.0277 (0.0001)***

0.0761 (0.0000)***

0.0227 (0.0021)***

1 0.8688 (0.0000)***

0.7837 (0.0000)***

0.8741 (0.0000)***

0.8678 (0.0000)***

0.9122 (0.0000)***

0.8918 (0.0000)***

PGE 0.0434 (0.0283)**

-0.2043 (0.0005)***

-0.0025 (0.8814)

0.2471 (0.0000)***

0.0634 (0.0020)***

0.0714 (0.0038)***

PtGE 0.0170 (0.0863)*

0.0294 (0.7963)

0.0383 (0.0050)***

0.0218 (0.0440)**

-0.0019 (0.7783)

0.0684 (0.0000)***


5 lags 0.7659 0.9998 0.0138 0.4778 0.6242 0.0081 10 lags 0.7291 0.9994 0.0261 0.5591 0.5878 0.0347




F-stat 0.0044 0.0023 0.0183 0.0000 0.0081 0.0000 Chi-Square 0.0044 0.0023 0.0182 0.0000 0.0080 0.0000


91




(p, q) (1, 1) (1, 0) (1, 1) (1, 1) (1, 0) Mean Equation

0 -0.0034 (0.8301)

-0.0606 (0.0725)*

0.0096 (0.4549)

0.0155 (0.5558)

-0.0191 (0.3648)

PGE 0.2101 (0.2041)

0.3738 (0.0053)***

0.1116 (0.5125)

0.1345 (0.4779)

0.0653 (0.5484)

PtGE -0.1493 (0.1659)

-0.0627 (0.7635)

-0.0673 (0.3148)

-0.2184 (0.1297)

0.0631 (0.6818)

1tR 0.1476 (0.0000)***

0.0120 (0.1983)

0.1346 (0.0000)***

0.1731 (0.0000)***

0.0929 (0.0000)***

1 tVIX -0.1382 (0.0000)***

-0.2683 (0.0000)***

-0.0906 (0.0000)***

-0.1669 (0.0000)***

-0.2060 (0.0000)***

Variance Equation

0 0.0100 (0.0000)***

2.5180 (0.0000)***

0.0044 (0.0000)***

0.0176 (0.0000)***

1.1623 (0.0000)***

1 0.0477 (0.0000)***

0.3155 (0.0000)***

0.0309 (0.0000)***

0.0588 (0.0000)***

0.3014 (0.0000)***

i 0.0769 (0.0000)***

0.2315 (0.0000)***

0.0426 (0.0000)***

0.0574 (0.0000)***

0.2521 (0.0000)***

1 0.9121 (0.0000)***

-- --

0.9449 (0.0000)***

0.9082 (0.0000)***

-- --

PGE -0.0680 (0.0043)***

-2.1606 (0.0000)***

-0.0046 (0.6325)

-0.0873 (0.0104)**

-0.8753 (0.0000)***

PtGE 0.0602 (0.0002)***

-0.5709 (0.0001)***

0.0038 (0.5245)

0.1071 (0.0000)***

-0.0614 (0.6494)


5 lags 0.3171 0.0000 0.1997 0.0210 0.0002 10 lags 0.6205 0.0000 0.5731 0.0303 0.0000



F-stat 0.1827 0.0191 0.5178 0.2792 0.7724 Chi-Square 0.1825 0.0190 0.5177 0.2790 0.7724



92

Appendix 3.2(b): Threshold GARCH Results for Pre-General Election and Post-

General Election (1994 - 2005) - Controlled by Volatility Index (VIX)



Technology

(p, q) (1, 1) (1, 0) (1, 0) (1, 0) (1, 1) (1, 1) Mean Equation

0 -0.0263 (0.0908)*

-0.0207 (0.6309)

-0.0069 (0.7371)

-0.0588 (0.0186)**

-0.0104 (0.5520)

-0.0899 (0.0056)***

PGE 0.2240 (0.0446)**

0.3746 (0.1651)

0.1085 (0.4666)

0.3718 (0.0631)*

0.2583 (0.1356)

0.2403 (0.1348)

PtGE -0.1054 (0.3367)

-0.3056 (0.2312)

-0.0776 (0.6252)

-0.0927 (0.7312)

-0.1359 (0.1520)

-0.3544 (0.0046)***

1tR 0.1041 (0.0000)***

0.0350 (0.0023)***

0.1319 (0.0000)***

0.2255 (0.0000)***

0.1091 (0.0000)***

0.1592 (0.0000)***

1 tVIX -0.1154 (0.0000)***

-0.0970 (0.0004)***

-0.1467 (0.0000)***

-0.2164 (0.0000)***

-0.1445 (0.0000)***

-0.2178 (0.0000)***

Variance Equation

0 0.0166 (0.0000)***

0.1485 (0.0000)***

1.0964 (0.0000)***

1.7002 (0.0000)***

0.0078 (0.0000)***

-0.0002 (0.8518)

1 0.0940 (0.0000)***

0.1359 (0.0000)***

0.4689 (0.0000)***

0.5290 (0.0000)***

0.0459 (0.0000)***

0.0060 (0.0374)**

i 0.1181 (0.0000)***

0.9201 (0.0000)***

0.0284 (0.5487)

0.2025 (0.0002)***

0.0783 (0.0000)***

0.0262 (0.0000)***

1 0.6186 (0.0000)***

-- --

-- --

-- --

0.9175 (0.0000)***

0.9804 (0.0000)***

2 0.2326 (0.0565)*

-- --

-- --

-- --

-- --

-- --

PGE -0.0687 (0.0042)***

-0.3921 (0.0000)***

-0.5374 (0.0000)***

-1.0678 (0.0000)***

-0.0981 (0.0000)***

-0.0234 (0.2644)

PtGE 0.0814 (0.0029)***

0.4124 (0.0000)***

0.5489 (0.0005)***

0.9331 (0.0000)***

0.0470 (0.0019)***

0.0169 (0.0069)***


5 lags 0.8461 0.0000 0.0000 0.0000 0.1751 0.0000 10 lags 0.9673 0.0000 0.0000 0.0000 0.3666 0.0000




F-stat 0.0034 0.0000 0.0000 0.0000 0.0000 0.0203 Chi-Square 0.0034 0.0000 0.0000 0.0000 0.0000 0.0201


93




(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0257 (0.0182)**

0.0290 (0.0966)*

0.0370 (0.0004)***

0.0320 (0.0101)**

0.0172 (0.1764)

PGE 0.1267 (0.4908)

-0.1299 (0.8380)

-0.0638 (0.5592)

0.0511 (0.8428)

0.0073 (0.9604)

PtGE -0.0625 (0.6041)

-0.1444 (0.4379)

0.1396 (0.0238)**

-0.0235 (0.8493)

-0.0621 (0.6382)

1tR 0.1229 (0.0000)***

0.0786 (0.0003)***

0.0417 (0.0523)*

0.1253 (0.0000)***

0.0684 (0.0005)***

1 tVIX -0.1110 (0.0000)***

-0.1372 (0.0000)***

-0.0835 (0.0000)***

-0.1198 (0.0000)***

-0.0969 (0.0000)***

Variance Equation

0 0.0122 (0.0000)***

0.0370 (0.0000)***

0.0220 (0.0000)***

0.0183 (0.0000)***

0.0140 (0.0000)***

1 0.0690 (0.0000)***

0.1065 (0.0000)***

0.0953 (0.0000)***

0.1014 (0.0000)***

0.0278 (0.0004)***

i 0.0824 (0.0000)***

0.0667 (0.0000)***

0.0721 (0.0000)***

0.0675 (0.0000)***

0.0871 (0.0000)***

1 0.8627 (0.0000)***

0.8339 (0.0000)***

0.8038 (0.0000)***

0.8341 (0.0000)***

0.9036 (0.0000)***

PGE 0.1038 (0.0120)**

1.0508 (0.0000)***

0.0234 (0.1387)

0.1587 (0.0006)***

0.0847 (0.0023)***

PtGE 0.0002 (0.9826)

-0.0306 (0.2938)

-0.0102 (0.0597)*

0.0035 (0.7197)

-0.0011 (0.8521)


5 lags 0.9782 0.9699 0.4830 0.6130 0.9670 10 lags 0.5677 0.7271 0.3056 0.6123 0.9973



F-stat 0.5779 0.7374 0.0629 0.9447 0.8868 Chi-Square 0.5779 0.7374 0.0627 0.9447 0.8868



94




Services Technology

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0386 (0.0037)***

0.0809 (0.0929)*

0.0278 (0.1042)

0.0267 (0.0810)*

0.0215 (0.0535)*

0.0086 (0.6908)

PGE -0.0689 (0.7629)

-0.7665 (0.0238)**

0.0226 (0.8953)

-0.2625 (0.5723)

0.1127 (0.6466)

-0.2123 (0.3963)

PtGE 0.0628 (0.5908)

0.0576 (0.8379)

0.0078 (0.9528)

0.0888 (0.6640)

-0.0725 (0.5510)

0.2047 (0.2842)

1tR 0.0896 (0.0000)***

-0.1725 (0.0000)***

0.1266 (0.0000)***

0.1279 (0.0000)***

0.0763 (0.0001)***

0.1039 (0.0000)***

1 tVIX -0.1173 (0.0000)***

-0.1810 (0.0000)***

-0.1041 (0.0000)***

-0.1218 (0.0000)***

-0.1097 (0.0000)***

-0.1035 (0.0000)***

Variance Equation

0 0.0277 (0.0000)***

0.9811 (0.0000)***

0.0166 (0.0000)***

0.0224 (0.0000)***

0.0093 (0.0000)***

0.1465 (0.0000)***

1 0.0958 (0.0000)***

0.1730 (0.0000)***

0.0700 (0.0000)***

0.1302 (0.0000)***

0.0532 (0.0000)***

0.1384 (0.0000)***

i 0.0617 (0.0000)***

0.2002 (0.0000)***

0.0261 (0.0010)***

-0.0039 (0.7253)

0.0807 (0.0000)***

0.0348 (0.0309)**

1 0.8259 (0.0000)***

0.6622 (0.0000)***

0.9022 (0.0000)***

0.8497 (0.0000)***

0.8874 (0.0000)***

0.7599 (0.0000)***

PGE 0.1408 (0.0000)***

-0.2748 (0.0875)*

0.0480 (0.1050)

0.5952 (0.0000)***

0.1519 (0.0027)***

0.0901 (0.1373)

PtGE 0.0080 (0.4581)

-0.2439 (0.3020)

0.0028 (0.8465)

-0.0160 (0.6114)

-0.0112 (0.1678)

0.3531 (0.0000)***


5 lags 0.8575 0.9999 0.1740 0.6677 0.9938 0.7437 10 lags 0.5366 0.9999 0.1450 0.6734 0.6766 0.8887




F-stat 0.0001 0.1284 0.2537 0.0000 0.0073 0.0000 Chi-Square 0.0001 0.1281 0.2535 0.0000 0.0073 0.0000


95




(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0136 (0.1470)

0.0038 (0.7858)

0.0242 (0.0036)***

0.0225 (0.0404)**

0.0161 (0.1033)

PGE 0.1220 (0.2840)

0.1358 (0.5287)

-0.0099 (0.9226)

0.0703 (0.6729)

-0.0160 (0.8803)

PtGE -0.0815 (0.2493)

-0.2224 (0.1392)

0.0004 (0.9936)

-0.0763 (0.4112)

-0.0758 (0.2788)

1tR 0.1489 (0.0000)***

0.1166 (0.0000)***

0.1018 (0.0000)***

0.1622 (0.0000)***

0.0711 (0.0000)***

1 tFFR 0.0604 (0.2092)

0.1003 (0.1329)

-0.0038 (0.9479)

0.0368 (0.5780)

0.0468 (0.4175)

Variance Equation

0 0.0065 (0.0000)***

0.0253 (0.0000)***

0.0052 (0.0000)***

0.0067 (0.0000)***

0.0075 (0.0000)***

1 0.0526 (0.0000)***

0.0644 (0.0000)***

0.0474 (0.0000)***

0.0592 (0.0000)***

0.0434 (0.0000)***

i 0.0726 (0.0000)***

0.0809 (0.0000)***

0.0435 (0.0000)***

0.0512 (0.0000)***

0.0593 (0.0000)***

1 0.9084 (0.0000)***

0.8899 (0.0000)***

0.9251 (0.0000)***

0.9143 (0.0000)***

0.9212 (0.0000)***

PGE 0.0466 (0.0036)***

0.2566 (0.0000)***

0.0116 (0.0577)*

0.0726 (0.0000)***

0.0473 (0.0010)***

PtGE 0.0039 (0.5330)

0.0289 (0.0277)**

-0.0011 (0.7435)

0.0036 (0.6607)

0.0005 (0.9375)


5 lags 0.2811 0.4417 0.2896 0.0029 0.9410 10 lags 0.4024 0.3932 0.4820 0.0052 0.9720



F-stat 0.1644 0.1509 0.9952 0.5711 0.5561 Chi-Square 0.1643 0.1508 0.9952 0.5711 0.5560



96


General Election (1994 - 2015) - Controlled by Federal Fund Rate



Technology

(p, q) (1, 2) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0079 (0.4410)

0.0304 (0.3025)

0.0183 (0.1237)

-0.0071 (0.5619)

0.0090 (0.3499)

-0.0297 (0.1026)

PGE 0.1036 (0.3757)

-0.2079 (0.4482)

0.1005 (0.3463)

0.1218 (0.5769)

0.1316 (0.2873)

0.0105 (0.9589)

PtGE -0.0202 (0.7579)

-0.0811 (0.6647)

-0.0149 (0.8646)

-0.1280 (0.2447)

-0.0899 (0.2449)

-0.0958 (0.4618)

1tR 0.1156 (0.0000)***

-0.0516 (0.0002)***

0.1325 (0.0000)***

0.1652 (0.0000)***

0.1107 (0.0000)***

0.1462 (0.0000)***

1 tFFR 0.0875 (0.1303)

-0.1760 (0.1916)

0.0771 (0.2736)

0.0599 (0.3897)

-0.0324 (0.6087)

0.2015 (0.4135)

Variance Equation

0 0.0167 (0.0000)***

0.4570 (0.0000)***

0.0206 (0.0000)***

0.0165 (0.0000)***

0.0057 (0.0000)***

0.0367 (0.0000)***

1 0.0827 (0.0000)***

0.1217 (0.0000)***

0.0913 (0.0000)***

0.1112 (0.0000)***

0.0508 (0.0000)***

0.0842 (0.0000)***

i 0.0890 (0.0000)***

0.1168 (0.0000)***

0.0448 (0.0000)***

0.0298 (0.0000)***

0.0807 (0.0000)***

0.0234 (0.0016)***

1 0.7389 (0.0000)***

0.7897 (0.0000)***

0.8754 (0.0000)***

0.8735 (0.0000)***

0.9092 (0.0000)***

0.8904 (0.0000)***

2 0.1247 (0.2055)

-- --

-- --

-- --

-- --

-- --

PGE 0.0486 (0.0336)**

-0.2139 (0.0008)***

-0.0019 (0.9198)

0.2446 (0.0000)***

0.0626 (0.0012)***

0.0771 (0.0026)***

PtGE 0.0163 (0.1382)

0.0320 (0.7742)

0.0411 (0.0033)***

0.0189 (0.0688)*

-0.0003 (0.9674)

0.0603 (0.0000)***


5 lags 0.9705 0.9998 0.0034 0.5873 0.5782 0.0065 10 lags 0.9229 0.9994 0.0074 0.6673 0.6070 0.0365




F-stat 0.0122 0.0038 0.0120 0.0000 0.0048 0.0000 Chi-Square 0.0121 0.0038 0.0119 0.0000 0.0047 0.0000


97




(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 -0.0021 (0.8975)

-0.0298 (0.2146)

0.0117 (0.3795)

0.0127 (0.5278)

0.0172 (0.2660)

PGE 0.1512 (0.4201)

0.1344 (0.2884)

0.0795 (0.6450)

0.0988 (0.6147)

-0.0206 (0.9397)

PtGE -0.1408 (0.2299)

-0.2411 (0.1401)

-0.0679 (0.3123)

-0.2049 (0.1899)

-0.1333 (0.2224)

1tR 0.1522 (0.0000)***

0.1245 (0.0000)***

0.1358 (0.0000)***

0.1754 (0.0000)***

0.0745 (0.0000)***

1 tFFR 0.0581 (0.3513)

0.1098 (0.2016)

0.0381 (0.5597)

0.0268 (0.7521)

0.0589 (0.3677)

Variance Equation

0 0.0102 (0.0000)***

0.0322 (0.0000)***

0.0047 (0.0000)***

0.0168 (0.0000)***

0.0060 (0.0000)***

1 0.0438 (0.0000)***

0.0422 (0.0000)***

0.0305 (0.0000)***

0.0521 (0.0000)***

0.0775 (0.0000)***

i 0.0800 (0.0000)***

0.0793 (0.0000)***

0.0447 (0.0000)***

0.0585 (0.0000)***

0.9567 (0.0000)***

1 0.9142 (0.0000)***

0.9119 (0.0000)***

0.9439 (0.0000)***

0.9143 (0.0000)***

-- --

PGE -0.0504 (0.1387)

-0.1010 (0.0000)***

0.0003 (0.9809)

-0.0709 (0.0764)*

0.0868 (0.0076)***

PtGE 0.0520 (0.0067)***

0.1183 (0.0000)***

0.0023 (0.7304)

0.0937 (0.0000)***

-0.0114 (0.3143)


5 lags 0.3148 0.0851 0.2493 0.0250 0.0000 10 lags 0.6526 0.2729 0.6480 0.0473 0.0000



F-stat 0.3859 0.1987 0.5706 0.3968 0.4705 Chi-Square 0.3857 0.1985 0.5705 0.3967 0.4704



98


General Election (1994 - 2005) - Controlled by Federal Fund Rate



Technology

(p, q) (0, 1) (1, 1) (0, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 -0.0208 (0.2003)

-0.0122 (0.7588)

0.0139 (0.4487)

-0.0569 (0.0054)***

-0.0103 (0.5673)

-0.0894 (0.0066)***

PGE 0.2015 (0.0344)**

0.2856 (0.3088)

0.1681 (0.2707)

0.3376 (0.0321)**

0.1969 (0.3005)

0.1917 (0.2638)

PtGE -0.1667 (0.1338)

-0.3655 (0.1564)

-0.0859 (0.3712)

-0.1770 (0.0781)*

-0.1405 (0.1770)

-0.3381 (0.0138)**

1tR 0.1733 (0.0000)***

0.0440 (0.0135)**

0.1452 (0.0000)***

0.1633 (0.0000)***

0.1157 (0.0000)***

0.1735 (0.0000)***

1 tFFR 0.1448 (0.0274)**

-0.0675 (0.6576)

0.0650 (0.4295)

0.0537 (0.5442)

-0.0182 (0.8056)

0.6944 (0.0491)**

Variance Equation

0 0.0145 (0.0000)***

0.1552 (0.0000)***

0.0204 (0.0000)***

0.0137 (0.0000)***

0.0081 (0.0000)***

-0.0003 (0.7616)

1 -- --

0.0944 (0.0000)***

-- --

0.1025 (0.0000)***

0.0448 (0.0000)***

0.0058 (0.0406)**

i 0.1504 (0.0000)***

0.0870 (0.0000)***

0.1107 (0.0000)***

0.0459 (0.0000)***

0.0810 (0.0000)***

0.0264 (0.0000)***

1 0.9224 (0.0000)***

0.8557 (0.0000)***

0.9305 (0.0000)***

0.8840 (0.0000)***

0.9171 (0.0000)***

0.9805 (0.0000)***

PGE -0.0567 (0.0000)***

-0.3762 (0.0000)***

-0.0501 (0.0107)**

-0.0886 (0.0009)***

-0.0864 (0.0125)**

-0.0166 (0.4353)

PtGE 0.0834 (0.0000)***

0.2854 (0.0137)**

0.0421 (0.0015)***

0.0310 (0.0182)**

0.0460 (0.0106)**

0.0156 (0.0186)**


5 lags 0.0000 0.2714 0.0000 0.0848 0.1817 0.0000 10 lags 0.0001 0.5306 0.0000 0.2359 0.3943 0.0000




F-stat 0.0000 0.0001 0.0022 0.0010 0.0145 0.0422 Chi-Square 0.0000 0.0001 0.0022 0.0010 0.0145 0.0420


99




(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0242 (0.0336)**

0.0282 (0.1132)

0.0372 (0.0006)***

0.0310 (0.0181)**

0.0140 (0.2885)

PGE 0.1404 (0.4033)

-0.0636 (0.9148)

-0.0405 (0.7049)

0.0795 (0.7342)

0.0215 (0.8761)

PtGE -0.0533 (0.6538)

-0.1295 (0.4484)

0.1310 (0.0387)**

-0.0180 (0.8923)

-0.0519 (0.6560)

1tR 0.1492 (0.0000)***

0.1139 (0.0000)***

0.0592 (0.0083)***

0.1452 (0.0000)***

0.0791 (0.0002)***

1 tFFR -0.2808 (0.0508)*

-0.0637 (0.7079)

-0.2961 (0.0207)**

-0.0136 (0.9335)

0.0510 (0.7144)

Variance Equation

0 0.0116 (0.0000)***

0.0356 (0.0000)***

0.0243 (0.0000)***

0.0150 (0.0000)***

0.0135 (0.0000)***

1 0.0684 (0.0000)***

0.1039 (0.0000)***

0.0827 (0.0000)***

0.0871 (0.0000)***

0.0276 (0.0006)***

i 0.0846 (0.0000)***

0.0803 (0.0000)***

0.0902 (0.0000)***

0.0724 (0.0000)***

0.0912 (0.0000)***

1 0.8667 (0.0000)***

0.8348 (0.0000)***

0.8047 (0.0000)***

0.8544 (0.0000)***

0.9044 (0.0000)***

PGE 0.0881 (0.0148)**

0.9985 (0.0000)***

0.0187 (0.2614)

0.1336 (0.0009)***

0.0668 (0.0083)***

PtGE 0.0001 (0.9915)

-0.0482 (0.1705)

-0.0143 (0.0274)**

0.0009 (0.9173)

-0.0039 (0.6168)


5 lags 0.8837 0.9275 0.3531 0.2487 0.9439 10 lags 0.3749 0.7928 0.0790 0.2354 0.9928



F-stat 0.5063 0.7500 0.1071 0.9016 0.8775 Chi-Square 0.5063 0.7500 0.1069 0.9016 0.8775



100




Services Technology

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0361 (0.0093)***

0.0784 (0.0936)*

0.0247 (0.1482)

0.0277 (0.0865)*

0.0195 (0.0886)*

0.0099 (0.6498)

PGE -0.0259 (0.9107)

-0.7563 (0.0300)**

0.0335 (0.8458)

-0.2110 (0.6588)

0.1365 (0.5110)

-0.1884 (0.4974)

PtGE 0.0677 (0.5631)

0.1047 (0.7054)

0.0108 (0.9370)

0.0949 (0.6378)

-0.0552 (0.6405)

0.2058 (0.2652)

1tR 0.1189 (0.0000)***

-0.1826 (0.0000)***

0.1312 (0.0000)***

0.1556 (0.0000)***

0.1069 (0.0000)***

0.1107 (0.0000)***

1 tFFR -0.2630 (0.0681)*

-1.7293 (0.0000)***

-0.0668 (0.7742)

0.1236 (0.3025)

-0.2271 (0.1268)

-0.0557 (0.8681)

Variance Equation

0 0.0273 (0.0000)***

0.9459 (0.0000)***

0.0163 (0.0000)***

0.0246 (0.0000)***

0.0098 (0.0000)***

0.1484 (0.0000)***

1 0.0853 (0.0000)***

0.1680 (0.0000)***

0.0726 (0.0000)***

0.1196 (0.0000)***

0.0559 (0.0000)***

0.1366 (0.0000)***

i 0.0583 (0.0000)***

0.1898 (0.0000)***

0.0348 (0.0000)***

0.0065 (0.5480)

0.0886 (0.0000)***

0.0319 (0.0466)**

1 0.8401 (0.0000)***

0.6759 (0.0000)***

0.8970 (0.0000)***

0.8529 (0.0000)***

0.8822 (0.0000)***

0.7629 (0.0000)***

PGE 0.1266 (0.0002)***

-0.2200 (0.1600)

0.0429 (0.1811)

0.5780 (0.0000)***

0.1319 (0.0039)***

0.1268 (0.0432)**

PtGE 0.0030 (0.7654)

-0.2472 (0.2884)

0.0107 (0.5233)

-0.0219 (0.5087)

-0.0113 (0.2439)

0.2975 (0.0000)***


5 lags 0.8074 0.9999 0.0451 0.3452 0.9924 0.6740 10 lags 0.6310 0.9999 0.0327 0.5340 0.6823 0.7415




F-stat 0.0010 0.2006 0.3105 0.0000 0.0108 0.0000 Chi-Square 0.0010 0.2004 0.3103 0.0000 0.0107 0.0000


101

CHAPTER 4

GENERAL ELECTION AND STOCK MARKET VOLATILITY IN MALAYSIA: EVIDENCE FROM GLCs AND NON-GLCs STOCK PERFORMANCE

4.1 Introduction

Domestic political events have a major influence on financial markets, especially national

election. Investors need to actively keep updated with market movement to rearrange their

investment strategies in times of political uncertainty. Negative news could destabilize

investor trading, thereby creating disarray and possible panic in the markets. It is, therefore,

crucial to examine whether this is the national election induces financial uncertainty.

In literature, Nordhaus (1975) is the pioneer in analysing the intimate link between

economics and political business cycles. The empirical literature on the relationship between

stock market performance and political elections dates back to Allivine and O’Neill (1980)

which focused on market behaviours at the time of U.S. elections. Motivated by the evidence

found in the U.S. market, academic studies commenced to investigate the impact of national

election on financial market in other regions, among them are Foerster and Schmitz (1997),

Pantzalis et al. (2000), Chiu et al. (2005), Wang and Lin (2009) and Hung (2013).

In the examination of election effect, previous studies largely focus on composite indices

which represent respective stock markets or top companies of the stock market division. For

example, Chiu et al. (2005) investigated the movement of KOSPI 200 index to see the effect

of presidential and parliamentary elections on South Korea financial market. For the same

purpose, Wang and Lin (2009) studied the Taiwan Stock Exchange Value Weighted Index

(TAIEX) and Lean and Yeap (2017) studied the FTSE Bursa Malaysia KLCI Index. From the

102

perspective of an investor, the existence of election effect in firm level is equally important.

Study on listed company index could be useful for investors because the individual firm may

react differently to election effect due to the nature of the business industry. However, this

remains an unexplored issue in the literature. Therefore, this study attempts to uncover

evidence of election effect in firm level. Selection of company index to perform the study on

election effect is another interesting point of this research. Since election effect is a political

factor to explain the fluctuation of stock returns, this study selects politically connected firm

as the sample to see the impact of the general election in stock returns of these firms.

Political connection and firm value are intimately linked. The incentives of political

connectedness have probably been recognized by citizens and investors. Investors generally

believe that political favours granted to a firm will enhance a firm’s market value. In

academic research, the positive relationship between political connectedness and firm value

has been a topic of intense debate. Evidence has been mounting over the years to suggest that

political connections allow politically connected firms to benefit from various governmental

interventions, including preferential access to credit (Dinç, 2005), lighter taxation (De Soto,

1989), reduced regulatory enforcement (Kroszner and Stratmann, 1998; Agrawal and

Knoeber, 2001), improved financial and accounting performance (Fisman, 2001; Johnson and

Mitton, 2003; Cooper et al., 2010), and many other forms.

Meanwhile, the impact of political connectedness on firm value is observable by its

stock returns. This is explained by Civilize et al. (2015) that in an informationally efficient

market if political connections create firm value, the existence of the value should be

systematically reflected in stock returns. In the finance literature, recent empirical studies

found evidence in supporting this view. A study by Niessen and Reunzi (2009) showed that

103

politically connected firms were significantly outperformed unconnected firms on Germany

stock market. Moreover, it is also evident from the study of Civilize et al. (2015) that

politically connected firms systematically enjoy higher realized stock returns over a long

period of time in Thailand. These studies suggest that the impact of political connections on

firm value is evident in stock returns.

Attempting to examine the link between political connections and stock returns is

fraught with difficulties. First, a researcher needs to be thorough in identifying the right

measure of political connections of a firm (Hassan et al., 2012). Different definitions of

political connections may lead to conflicting research results. It is possible to overcome this

problem in Malaysia by studying the Government-Link-Companies (GLCs) and Non-

Government-Link-Companies (Non-GLCs). The GLCs are controlled by the Malaysian

government and the GLCs play a significant role in the development of the country’s

economy. Second, a study would ideally be carried out in an environment with a political

shift to see the effect of the change in political leadership on politically connected firms

(Civilize et al., 2015). In Malaysia, potential political change happened during the 12th and

13th Malaysian General Election in the year 2008 and 2013. In view of that, Malaysian stock

market has a unique empirical setting in investigating the impact of political uncertainty on

politically connected firms in years of general election.

Given the significant role of GLCs firms in the development of Malaysia economy,

the effect of government intervention on companies’ performance has been empirically

assessed in previous studies (Razak et al., 2011; Lau and Tong, 2008). Evidence from

previous studies confirmed the impact of political connectedness on firm value. However, the

effect of political uncertainty is not considered in their study and the impact could be the

104

other way round. There is some evidence of adverse effects on firms with political

connections in Malaysia following the removal of regulation and handover of political power.

For example, the study by Mitchell and Joseph (2010) found adverse effects on political

connections firms during the removal of capital controls. Moreover, Mitchell and Joseph

(2010) also found that GLCs firms are more badly affected than non-GLCs firms during the

first month of the resignation of Tun Dr. Mahathir Mohammad as prime minister and the

handover of control to Datuk Seri Abdullah Ahmad Badawi. The study evidently showed that

political uncertainty has a negative impact on politically connected firms.

Even though Malaysia is politically stable in recent years, uncertainty still exists especially

during the election period. Fluctuation of stock market around election dates was clearly

observed in Malaysia especially during the 12th and 13th Malaysian General Election in recent

years. Evidently, as a proxy of the Malaysian stock market, the key index of FTSE Bursa

Malaysia KLCI experienced significant volatility during the general election years (Lean and

Yeap, 2017). Prior to the year 2005, the coalition Barisan Nasional won in the Malaysian

general elections in the year 1995, 1999 and 2004 and continued ruling with a stable two-

thirds majority. However, there was close fight between the two major coalitions in the 12th

and 13th general election. The coalition Barisan Nasional consecutively lost the two-thirds

majority in parliament, which is never happened in political history since Malaysia

independence. The political uncertainty due to potential shift of ruling party provides an

opportunity to conduct a research on political uncertainty and stock market performance.

Therefore, this paper aims to contribute to the current literature by empirically investigating

the performance of GLCs and Non-GLCs over the period of Malaysia general elections from

the year 1995 to 2013.

105

In brief, the contributions of this study are, first, the Threshold Generalized

Autoregressive Conditional Heteroskedasticity (Threshold GARCH / GJR GARCH) model

developed by Glosten et al. (1993) is applied to investigate the election effect on the stock

returns of GLCs firms and non-GLCs firms. Since empirical works have produced substantial

evidence on the influence of political outcomes to the main stock index, this present study

attempts to further examine the reaction of companies' stock returns and volatility in the

recent five Malaysian general elections in Malaysia. Second, this study enhances the

knowledge in the case of Malaysia by investigating the election effect in two different stages

which represent the general up and down and the drastic rise and fall. In concern of the

different effect of the general election on market volatility, the general election years of 1995,

1999 and 2005 are classified as general ups and downs periods, while the general election

years of 2008 and 2013 is drastic shock periods. Third, trading volume is also included in this

study. The theoretical model of Foster and Viswanathan (1990) suggests that low volume

comes with high volatility. Conversely, Admati and Pfleiderer (1988) speculated that trading

volume would be high when price volatility is high. Hence, this study further analyze the

observed volatilities to see whether they are related to trading volume. This study may be of

interest to investors as the results will contribute the information that most investors require

particularly in constructing an effective equity portfolio investment during the times of

election.

The rest of the paper is organized as follows. The next section summarizes the

literature of related studies. Section 4.3 outlines the research design and data used in this

study. Section 4.4 reports the empirical results, followed by the conclusion in Section 4.5.

106


The study on election effect started with the relationship between economic performance and

political business cycle in the United States. With evidence from the literature, it is

undeniable that government policies have great influence on all aspects of economic life. In

the literature, Nordhaus (1975) mentioned that the pattern of government policies are

predictable within an incumbent's term in office because the government actively manage the

economy to gain public support. Since then, studies such as Allivine and O’Neill (1980) and

Huang (1985) concentrated on the stock market performance during the U.S. presidential

election cycle to see the impact of government policies on the economy. Generally, evidence

from these studies indicated the impact of U.S. presidential elections on its stock market, in

which the U.S. stock markets make larger gains in the third and fourth years of a presidential

term. The political business cycle is not a short-term trend as shown by the study of Wong

and McAleer (2009). They found that the cyclical trend existed for the last ten

administrations from the year 1965 through 2003, particularly when the incumbent is

Republican.

Other than the US presidential election, empirical investigations have examined

financial market movements in relation to political events. Earlier studies presented evidence

indicating that elections do affect financial markets across their sample countries (Gemmill,

1992; Pantzalis et al., 2000; Smales, 2016). In Britain general election, Gemmill (1992)

found an extremely close relationship between opinion polls and the FTSE 100 share index

during the 1987 election. The analysis by Pantzalis et al. (2000) is the first of its type on an

international scale which covered stock market indices across 33 countries around political

election dates during the sample period 1974 - 1995. They found a positive abnormal return

in two weeks prior to the election week. The result was generally in line with the models of

107

Harrington (1993) which implies that the existence of a higher degree of uncertainty before

an election process will lead to a corresponding increase in equity prices as the uncertainty is

resolved. In a recent study, Smales (2016) provided evidence that the turbulent Australian

federal elections induce higher levels of uncertainty in financial markets. Moreover, the effect

is more pronounced as election-day draws near. In view of the role of political uncertainty in

financial markets around election dates, the study in this field has crucial implications for

investors looking to make investment decision before the election day and after the election

day.

In the Malaysian stock market, Ali et al. (2010) have attempted to show the

relationship between political uncertainty and market uncertainty. Ali et al. (2010) discovered

a significant over-reaction behaviour in the Malaysian market upon the announcement of the

removal of the deputy prime minister and announcement of the resignation of the prime

minister. Moreover, Ali et al. (2010) also investigated the market behaviour during general

election and evidence of under-reaction was found upon the announcement of the election

result. They interpreted that investors are well predicted with the election outcome and the

finding of under-reaction is in line with the stable political condition in the year 1987 to 2006.

However, the study of Ali et al. (2010) was limited to gauging the market reaction to political

uncertainty and general election, abnormal stock returns and volatility were not investigated

in the study.

Then, the study of Lean (2010) and Lean and Yeap (2017) circumvented the

limitation and examined the performance of Kuala Lumpur Composite Index (KLCI) on

stock returns and volatility during general election periods. Lean (2010) showed that there are

different effect for the pre-general election and post-general election over the period of 1994-

108

2009, where stock returns react positively before the election and negatively after the election.

Moreover, Lean (2010) also found strong evidence of GARCH effect in the model. Lean and

Yeap (2017) extended the period of study from 1989 to 2014, which covers six Malaysian

general elections. They found significant election effect in stock volatility but not in the stock

returns, where stock volatility is higher during the pre-general election but lower in the post-

general election. Moreover, the post-election effect only last for one month as there is no

unexpected outcome from the general elections. The changes of high volatility before the

election to low volatility after the election are not consistent with the theory that market is

nervous and downward-bound due to the political uncertainty. In fact, Lean and Yeap (2017)

explained the results based on the real situation of Malaysian general election. The ruling

coalition of Barisan Nasional (BN) had been in office since Malaysia independence, therefore,

investors in the Malaysian market do not expect any major changes of the election outcome

and stay confident with the market.

Despite the different level of election tension in the past six general elections, Lean

and Yeap (2017) employed a long history of stock returns that incorporates an unusually high

spike during the 12th and 13th general elections. During the 8th, 9th, and 10th general elections,

there was significant victory for BN that signaled voters' confidence in the governance.

However, the election outcome of the 12th general election was described as a political

tsunami where the incumbent lost its two-thirds majority in the Parliament. The 13th general

election created election uncertainty with a strong expectation of change from the ruling

coalition. Although BN retained its power, the worst results of BN induced surprise to the

investors that affected the stock volatility in the market. Due to the significant difference of

political condition, the analysis that covers all the previous Malaysian general elections under

one sample period may produce erroneous inferences. For that reason, this study intends to

109

divide the general election periods into two stages. One stage represents the general ups and

downs periods in election years of 1995, 1999, and 2004. The other stage represents drastic

shock periods in election years of 2008 and 2013.

Prior empirical work as discussed earlier has provided some insights into the election effect

in Malaysia. This study extends the scope of research to cover the election effect in politically

connected firms. As a primer, the Malaysian government exerts a significant influence over

the corporate sector through direct equity ownership of listed firms, namely government-

linked companies (GLCs). In addition, the Malaysian government also has influence in the

appointment of members of the GLCs board of directors and senior management positions,

including making major decisions such as contract awards, strategy, restructuring, and

financing, acquisition and divestments. Hence, the political patron in the corporate sector and

the political uncertainty due to the general election in Malaysia produce an interesting and

important case study.

Empirical evidence of election effect on the politically connected firm is lacking in Malaysia.

Nevertheless, previous literature has shown the close link between political connection and

firm value (Lau and Tong, 2008; Razak et al., 2011; Poon et al., 2013). Lau and Tong (2008)

revealed a significant positive relationship between the degree of government ownership and

firm value on 15 GLCs over the years 2000-2005. The results implied that Malaysia

government intervention improves the firm value of GLCs. Meanwhile, based on a sample of

210 GLCs and Non-GLCs firms for the period 1995-2005, Razak et al. (2011) used a panel

based regression approach to determine the impact of ownership mechanism on firm’s

performance. Results from their study appear to support the findings of Lau and Tong (2008),

where GLCs outperformed Non-GLCs in term of market-based valuation measures and

110

accounting-based measures. On the other hand, when the sample is matched for comparison

for small size companies in the sectors of Trading, Production, Plantation, Properties, and

Consumer Product, findings highlighted that Non-GLCs performance is better GLCs.

With a similar objective, Poon et al. (2013) narrowed down the research scope to Malaysian

commercial bank industry. Specifically, Poon et al. (2013) examined the effect of politically

connected boards on commercial bank performance by considering the interaction effect

between age, ethnicity, and the political connections of board directors. Generally, the result

of the study revealed that bank performance of politically connected firms improves

compared to the non-connected firms. Other than that, Poon et al. (2013) also investigated the

performance of political-connected banks in the years (2005-2009) after the 4th Prime

Minister, Tun Dr. Mahathir Mohammad, officially left the office. From the results, Poon et al.

(2013) concluded that high political-connected banks performance were poorer than high

political-connected banks in the later five years due to the handover of control among

politicians. The negative effect on the changing hand is consistent with an earlier study of

Mitchell and Joseph (2010). The resignation was genuinely unexpected and it created a shock

to the market. Mitchell and Joseph (2010) examined the immediate effect of the resignation

on political-connected firms and found that GLCs were more badly affected than Non-GLCs

during the first month of resignation in the year 2002.

Furthermore, remarkable studies of Johnson and Mitton (2003) and Mitchell and Joseph

(2010) focused on regulation changes that have occurred in Malaysia and discuss their likely

effect on the performance of individual Malaysian firms. In particular, Johnson and Mitton

(2003) analyzed the performance of the politically connected firm over the Asian Financial

crisis period, including the period of the imposition of capital control, while Mitchell and

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Joseph (2010) focused on the effect of the removal of regulation. Johnson and Mitton (2003)

found evidence that politically favoured firms outperformed unconnected firms in the period

immediately after the imposition of capital controls during the 1997 Asian currency crisis in

Malaysia. However, when the capital controls were gradually reduced and the exchange rate

peg was removed, financial firms with political connections have not performed as well as

others since the measures set up to support them have been removed (Mitchell and Joseph,

2010).

A number of studies have investigated the link between political connections and firm

performance in countries in which relationship is required in doing business effectively, such

as China. In China, the local economy is less market-oriented or the government has more

discretion in allocating economic resources, and hence political connections are important for

Chinese firms (Chen et al., 2011). Therefore, it is intuitive to expect that political

connections have a positive effect on Chinese firms. But, empirical evidence in the literature

related to political connections in China is mixed. Negative effects of political connections on

firm performance were demonstrated by Fan et al. (2007) and Wu et al. (2012). Both the

studies found that firms with connected CEOs and chairman have lower values, whereas the

private firms have higher values. In this case, Su and Fung (2013) argued that definition of

political connections is problematic and lead to conflicting results. Hence, they compiled a

new database of political connections to include the firm’s top management team or board

members that have a close relationship to the Chinese government. By using the new data set,

a robust result confirmed the positive relationship between political connections and firm

performance. Hence, the China case study clearly indicates the importance of the right

measurement for political connections.

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Besides the close link between political connections and firm value, a growing body

of evidence also supports the notion that share prices of politically connected firms are

strongly related to political news. In Malaysia, Hassan et al. (2012) examined share price

reaction to political events by using a sample of 138 firms for the period of 1990 - 2001. The

two important political events included in the study were the rumour of ill health of Prime

Minister Tun Dr. Mahathir Mohamad, and the sacking of Deputy Prime Minister Anwar

Ibrahim. They found that share prices of the politically connected firms increase with the

announcement of favourable political events, which is the sacking of the deputy prime. On

the other hand, the politically connected firms reacted negatively to the news of ill health of

the prime minister. Similarly, in the Philippines, the stock price of connected firms was badly

impacted by the negative news on the health of President Suharto during the final years of his

life (Fisman, 2001).

Turning to election effects on politically connected firms, only a couple of studies

have been done in other countries (Goldman et al., 2009; Yeh et al., 2013), but none of them

include Malaysia. Goldman et al. (2009) showed that political connections are important in

US company that connected to the parties. The company's stock return appeared to be

abnormally high following the announcement of the nomination of a politically connected

individual to the board. Moreover, in the 2000 presidential election, the Republican-

connected firm increased in value while Democratic-connected firms decreased in value

when the Republican candidate won the presidential election. A similar pattern was found in

Taiwan during the 2004 presidential election when there was an unexpected shock of the

election outcome (Yeh et al., 2013). Before the election, the Kuomintang was expected to

win and Kuomintang-connected firms were associated with higher abnormal returns.

Eventually, the incumbent President Chen from Democratic Progressive Party was re-elected

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by a slim margin. The investors were shocked with the election result and led to a reversal in

stock returns for the Kuomintang-connected firm.

In summary, empirical results have shown that stock markets responded to the elections in

Presidential system countries and Parliamentary system countries. Moreover, studies on other

countries provide confidence results that the stock price of politically connected firm reacted

to political events as well as the election. The studies in Malaysian market have proved that

there is a link between political connections and firm value, and the share price of connected

firm react to political events. Based on the above literature review, none of the studies in

Malaysia has attempted to explore the election effect on politically connected firm or the

GLCs. Therefore, this present study would like to fill in the research gap in the literature by

examining the election effect on GLCs and Non-GLCs in Malaysia.

4.3 Data and Empirical Methodology

The daily closing stock price of 11 selected Government-Link-Companies (GLCs) and Non-

Government-Link-Companies (Non-GLCs) employed in this study was collected from Bursa

Malaysia, and the election dates were obtained from the Electoral Commission of Malaysia.

The selection of the sample companies in this study is based on the following sample

selection method which fulfilling two basics condition: (1) most active traded stock at the end

of the year 2015, and (2) must be listed in Bursa Malaysia since 4 January 1994. The full

sample period covers from 4 January 1994 to 31 December 2015, with a total of 5,738

observations, which covers the recent five Malaysia general elections. All data are collected

from the Bursa Malaysia (http://www.bursamalaysia.com). The important dates of general

elections are summarized in Table 4.1, which are the date of dissolution of parliament,

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election date or voting date and the 1st parliament assembly after the election. The pre-general

election period refers to the duration from the day of dissolution of the parliament until the

day before voting, while the post-general election period refers to the duration from the day

after voting until the first parliament assembly.

[Insert Table 4.1: Malaysia General Election]

Table 4.2 and 4.3 presents the descriptive statistics for daily returns series for the full sample

(1994 – 2015) period of GLCs and Non-GLCs stock return. Daily returns are calculated as

the first difference in the natural logarithms of the stock market index, )/ln(100 1 ttt IIR

where tI and 1tI are the values of each index for periods t and 1t , respectively. In the

case of a trading day following a non-trading day, the return is calculated using the closing

price of the last trading day. From the descriptive statistics, the null hypothesis of normally

distributed daily returns is rejected by the Jarque-Bera normality test. This finding is in line

with most of the previous findings, saying that daily stock returns are not normally

distributed.

[Insert Table 4.2: Descriptive Statistics for GLCs Stock Return (1994 – 2015)]

[Insert Table 4.3: Descriptive Statistics for Non-GLCs Stock Return (1994 – 2015)]

Furthermore, mean returns for the sub-sample periods of pre-general election and

post-general election for GLCs and Non-GLCs are presented in Table 4.4 and 4.5. It is

observed that the mean returns prior to the pre-general election are mixed for the sub-sample

period of 1994-2005. However, for the sub-sample period of 2006-2015, the mean returns

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(GLCs and Non-GLCs) are mostly negative (9 out of 11) prior to the pre-general election. On

the other hand, for the period of the post-general election, the mean returns for the indices are

mixed for the sub-sample period of 1994-2005. For the period of 2006-2015, most of the

mean returns (GLCs and Non-GLCs) are positive (8 out of 11) after the general election.

From the descriptive statistics and mean returns for the two sub-sample periods, it is notable

that there could be different election effects on the stock market for the general elections in

the year 1994 to 2005 and 2006 to 2015. The preliminary statistics justify the aim of this

study in dividing the full sample period into two sub-samples in order to study the election

effects under different political condition.

[Insert Table 4.4: Sub-sample Mean Return for GLCs]

[Insert Table 4.5: Sub-sample Mean Return for Non-GLCs]

Table 4.6 and 4.7 present the selected sample of Malaysian Government-Link

Companies and Non-Government-Link Companies information in this study. Information

included in the tables are company name, industry or sector, symbol, market capitalization,

number of shares, earning per share, revenue, profit before tax and net profit.

[Insert Table 4.6: Selected Sample of Malaysian Government-Link Companies (GLCs)]

[Insert Table 4.7: Selected Sample of Malaysian Non-Government-Link Companies

(Non-GLCs)]

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In this study, the test for market volatility during general elections is carried out by using the

Threshold Generalized Autoregressive Conditional Heteroskedasticity (Threshold GARCH /

GJR GARCH) model developed by Glosten et al. (1993), Threshold GARCH / GJR GARCH

(1, 1)5 model with dummy variables:

ttttt RPtGEPGER 13210 (1)

ttttttt PtGEPGEN 212

1112

12

1102

(2)


variables which take on value 1 if the corresponding return for day t is the period of pre-

general election, and the period of post-general election respectively, and 0 otherwise; t is

the error term. Meanwhile, 30 ,..., are the parameters to be estimated. Among them, 0

measures the mean return (in percentage) on other trading days; whereas 1 and 2 capture

the average return of the stock index for the period of pre-general election and post-general

election.

The null hypothesis of the test is 0: 210 H , which implies that average daily

returns (volatility) for the period of pre-general election and post-general election are

significant different from zero. If the null hypothesis does not hold, then it can be concluded

that the market index is characterized by statistically different on average returns (volatility)

for the period of pre-general election and post-general election. In another word, this would

imply that general election effect is indeed present in the market.

5 According to Bollerslev et al. (1992), in testing the GARCH models, p = q = 1 is sufficient for most financial series. Hence, the highest order of p and q considered in this study for the Threshold GARCH model, is (1, 1).

117

In the Equation (2), tN takes on value 1 when the stock quote falls in a period and 0

for increments of the stock quotation. Besides, the parameter is used to capture the

asymmetrical effect of bad news (decrease in stock indices, hence negative tR ) and good

news (increase stock indices, hence positive tR ). If 0 by the t test of significance, then it

can be concluded that the impact of news is asymmetric. If the parameter is positive, then

good news has an impact of i on volatility while bad news has an impact of ( i ) on

volatility. Thus, positive value of indicates the existence of a leverage effect in that bad

news increases volatility. The additional parameters, t , which makes this specification

different from the original Threshold GARCH model, are employed to capture the daily

effect. Furthermore, a lagged value of the return variable is introduced in the equations to

avoid serial correlation error terms in the model, which may yield misleading inferences.

Next, this paper also investigates whether the observed return volatilities on the

various pre-general election and post-general election are related to trading volume, indirectly

testing the Admati and Pfleiderer (1988) and Foster and Viswanathan (1990) models. This

paper model the natural logarithm of the volume as the following Threshold GARCH (1,1)

process.

ttttt VolPtGEPGEVol 13210 lnln (3)

2111

21

2110

2 ttttt N (4)

where tVolln is the natural logarithmic trading volume at day t ; tPGE and tPtGE are dummy

variables which take on value 1 if the corresponding trading volume for day t is the period of

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pre-general election, and the period of post-general election respectively, and 0 otherwise; t

is the error term. Meanwhile, 30 ,..., are the parameters to be estimated. For example, if the

result showed high volatility (Equation 2) and low trading volume (Equation 3), then this

result is concluded to support the theoretical model of Foster and Viswanathan (1990) which

stated that liquidity traders being unwilling to trade in the periods where the prices are more

volatile. However, if the result showed high volatility and high trading volume, then this

result is concluded to support Admati and Pfleiderer (1988) theoretical argument which stated

that liquidity traders are willing to trade in the periods where the prices are more volatile.


Firstly, the results of pre-general election effect and post-general election effect for the full-

sample period of 1994-2015 are presented in Table 4.8(a), 4.8(b), 4.9(a) and 4.9(b). Table

4.8(a) and 4.8(b) report the results of the mean equation and variance equation of the

Threshold GARCH (1, 1) model for Government-Link Companies (GLCs). Meanwhile,

Table 4.9(a) and 4.9(b) report the estimation results for Non-Government-Link Companies

(Non-GLCs). The diagnostic test result is included in the lower part of the tables to support

the validity of the models. Under the mean equation, the dummy coefficients of the GLCs

and Non-GLCs are all insignificant in the pre-general election period. For the post-general

election, there are only three out of eleven GLCs recorded significant returns in the full

sample period. Specifically, the stock returns of the TNB and MRC are negatively significant,

while the CCM is positively significant after the general elections. For Non-GLCs, the NESZ

is the only positive and significant stocks return in the post-general election.

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The estimation results of the variance equations are also presented in Table 4.8(a),

4.8(b), 4.9(a) and 4.9(b). For the variance equation, the pre-general election dummy

coefficients are positively significant for seven GLCs and six Non-GLCs. This result

indicates that the share prices of GLCs (AHB, CIMB, MAY, BOUS, TNB, UMWH, and

MRC) and Non-GLCs (GENM, GENT, MISC, YTL, IOI, and KLK) experienced significant

high volatility in the pre-general election periods. Besides, significant low volatility is found

in a GLCs (TMK) and a Non-GLCs (RHBC) in the pre-general election periods. Meanwhile,

for the post-general election periods, the dummy coefficients of the variance equations are

positive and significant for MBS (GLCs), BOUS (GLCs) and PEP (Non-GLCs). Thus, it is

evident that general election result increases the volatility in these three companies. On the

other hand, significant low volatility is found in BIMB, CIMB, TMK, and MRC for GLCs,

and RHBC, MISC, YTL, NESZ, ROTH, and KLK for Non-GLCs.

In the variance equations, the leverage effect term, , in the variance equation is

positive and statistically different from zero in seven GLCs and nine Non-GLCs. The positive

value of indicates that the leverage effect in bad news increased the volatility. In particular,

the bad news has an impact of ( i ), while good news has an impact of ( i ) only. For

example, refer to Table 4.8(a), bad news in the AHB (GLCs) has an impact of 0.9085

(0.8864+0.0221), while good news only has an impact of 0.8864. Hence, the results indicate

the existence of the asymmetric effect on stock volatility in seven GLCs (AHB, CIMB, MAY,

BOUS, TNB, UMWH, CCM, and MRC) and nine Non-GLCs (PBK, RHBC, GENM, GENT,

MISC, YTL, PEP, ROTH, and IOI).


General Election (1994 - 2015) – Government Link Companies (GLCs)]

120




General Election (1994 - 2015) – Non-Government Link Companies (Non-GLCs)]



Next, this study examines the presence of pre-general election effect and post-general

election effect in GLCs and Non-GLCs for the sub-sample period of 1994-2005 and the

results are presented in Table 4.10(a), 4.10(b), 4.11(a) and 4.11(b). For the stock return, most

of the dummy coefficients for the mean equations of the pre-general election are positive for

GLCs (BIMB, CIMB, MAY, MBS, BOUS, TMK, TNB, CCM, and MRC), however, they are

insignificant. All the Non-GLCs are found to have insignificant dummy coefficients for stock

return, but the sign of the dummy coefficient is positive for the stock of RHBC, GENT, YTL,

NESZ, ROTH, and KLK and negative for the other five Non-GLCs. On the other hand, for

post-general election, MRC is the only GLCs in the Property sector that has a significant and

negative dummy coefficient. This indicates that the outcome of the general election has

negatively impacted the share price of this company.

Furthermore, the estimation results of the variance equations are presented in Table

4.10(a), 4.10(b), 4.11(a) and 4.11(b). For the sub-sample period of 1994-2005, the results

show that the GLCs and Non-GLCs react differently as compared with the results of the full-

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sample period. Among the GLCs, six of them experienced significant volatility changed

before the general elections. In particular, BIMB, MBS, BOUS, UMWH, CCM, and MRC

experienced significant low volatility before the general elections. On the other hand, six of

the Non-GLCs (PBK, MISC, YTL, PEP, IOI, and KLK) also experienced significant low

volatility during the period of pre-general election. However, after the announcement of the

election result, the stock volatility increased significantly in CIMB, BOUS, TNB, and

UMWH for GLCs, while PBK, MISC, and PEP for Non-GLCs. Thus, it is evident that most

of the GLCs and Non-GLCs in this study experienced significant volatility change due to the

general election and the finding is contradicted with the full sample period.

The results of variance equations also confirm that there is an asymmetric effect of

political elections on stock volatility in most of the GLCs and Non-GLCs for the sub-sample

period of 1994-2005. The positive value of the leverage effect term is statistically significant,

and this indicates the existence of asymmetrical effect in the sample of this study. This

finding implies that negative shocks or bad news from election may have larger impact on

stock volatility than good news in the sub-sample period of 1994-2005. Lastly, the validity of

the model is checked by the diagnostic tests. No remaining ARCH effect and serial

correlation are found in the estimated models.





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For the second sub-sample period of 2006-2015, Table 4.12(a) and 4.12(b) present the

results of pre-general election effect and post-general election effect for the GLCs, while

Table 4.13(a) and 4.13(b) report the estimation results for the Non-GLCs. From the

estimation of mean equations, TMK (GLCs) is the only company from the sector of Trade

and Services, that showed significant negative stock returns after the general elections. On

the other hand, NESZ (Non-GLCs) has a positive and significant return during the period of

the post-general election, which indicates that the general election result brought a positive

impact to this company’s share price.

As mentioned earlier, the turbulent political condition in the 12th and 13th Malaysia

due to the fierce competition between the two major coalition induced market uncertainty.

Prior to the general elections, the market condition experienced significant volatility changes,

and the empirical results of this study are in line with the situation. From the estimation

results of the TGARCH variance equations, seven out of eleven (CIMB, MAY, MBS, BOUS,

TNB, UMWH, and MRC) of the GLCs encountered significant high volatility in the pre-

general election periods, and six of the Non-GLCs (GENM, GENT, MISC, YTL, IOI, and

KLK) also recorded the same pattern of stock volatility. Nevertheless, two Non-GLCs,

namely PBK and NESZ, recorded significant low volatility during the pre-general election

periods. For the post-general election periods, this study also finds some evidence of election

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effect in stock volatility. The results of the post-general election show significant low

volatility in three GLCs (CIMB, TMK, and MRC). Notably, ten of the Non-GLCs has

negative volatility during the post-general election and four of them are statistically

significant (RHBC, MISC, YTL, and KLK). The result on the second sub-sample period of

2006-2015 is evidently different compared to the first sub-sample period in early years,

where most of the GLCs and Non-GLCs recorded significant low volatility before general

elections and significant high volatility after general elections.

The asymmetric effect of the general elections is also reported in Table 4.12(a),

4.12(b), 4.13(a), and 4.13(b). The asymmetric effect appeared only on a few of the GLCs and

Non-GLCs. The leverage effect term, , is statistically different from zero for BIMB, CIMB,

MAY, BOUS, and TMK (GLCs), indicating the existence of the asymmetrical effect in the

stock index. Unlike the results of the first sub-sample, some of the company stock has a

significant negative leverage effect term, which indicates that good news increased the

volatility. In particular, the good news has an impact of ( i ), while bad news has an impact

of ( i – ). For the case of BIMB, good news has an impact of 0.7231, while bad news has a

lower impact of 0.5917 (0.7231–0.1314). Therefore, during the sub-sample period of 2006-

2015, positive shocks create a greater impact on the conditional volatility of the GLCs

(BIMB, BOUS, and TMK) than negative shocks. Besides, the validity of the model is

supported by the diagnostic test with no remaining ARCH effect and serial correlation in

most of the estimated models.



124







High volatility in stock returns is always a concern for market participants, therefore

this study also investigates whether the high volatility is associated with high trading volume

as proposed by Admati and Pfleiderer (1988), or low trading volume as proposed by Foster

and Viswanathan (1990). Preliminarily, the relationship between trading volume and stock

price volatility before and after the election dates is briefly explained in Figure 4.1 and Figure

4.2. For GLCs, weaker relationship is found in both the pre-general election period and post-

general election period. On the other hand, the relationship between Non-GLCs trading

volume and stock volatility appears to be weaker before general elections. Then, after general

elections, stronger relationship is found on Non-GLCs share prices. For both the GLCs and

Non-GLCs stock prices, although the pattern is not quite as tight, the relationship between

trading volume and stock volatility remain the same pattern in the two sub-sample of 1994-

2005 and 2006-2015.

[Insert Figure 4.1: Relationship between Trading Volume and Stock Price Volatility for

GLCs and Non-GLCs from 1994 - 2005]

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[Insert Figure 4.2: Relationship between Trading Volume and Stock Price Volatility for

GLCs and Non-GLCs from 2006 - 2015]

Further, the examination of election effect in trading volume is done by modeling the

logarithm of trading volume in the Threshold GARCH (1,1) process. The results of the pre-

general election and post-general election on trading volume for GLCs and Non-GLCs during

the period of 1994-2005 are reported in Table 4.14(a), 4.14(b), 4.15(a) and 4.15(b). In early

years of 1994-2005, the empirical results show that NESZ (Non-GLCs) is the only company

with a low volume of trading during the pre-general election. For the post-general election

periods, three of the GLCs (BOUS, TMK, and CCM) and four of the Non-GLCs (GENM,

MISC, PEP, and IOI) have high trading volume.

In summary, the volatility of returns and trading volume findings for GLCs and Non-

GLCs during the 1994 – 2005 period is as follows: High volatility of returns and high trading

volume are observed during the post-general election for BOUS (GLCs) and PEP (Non-GLCs)

stocks. Investors were confident and started to trade in the market after the coalition of

Barisan Nasional won in the general elections. In other words, the high volatility after the

general election is not induced by uncertainties of the general election. In fact, the high

volatility could be induced by the active trading activity in the market right after the election.

These findings support the argument of Admati and Pfleiderer (1988), which speculate that

trading volume would be high when price volatility is high because of the willingness of

liquidity traders to trade in the periods where the prices are more volatile.

[Insert Table 4.14a: Threshold GARCH Results for Trading Volume during Pre- and

Post-General Election (1994 - 2005) – Government Link Companies (GLCs)]

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[Insert Table 4.14b: Threshold GARCH Results for Trading Volume during Pre- and



Post-General Election (1994 - 2005) – Non-Government Link Companies (Non-GLCs)]



For the second sub-sample period of 2006-2015, the results of the pre-general election

and post-general election in trading volume for GLCs and Non-GLCs are reported in Table

4.16(a), 4.16(b), 4.17(a), and 4.17(b). The empirical results show that the significant low

volume of trading during the pre-general election is recorded by ROTH (Non-GLCs) only.

Meanwhile, significant high trading volume occurs on the pre-general election periods in four

of the GLCs (CIMB, MAY, TNB, and UMWH) and two of the Non-GLCs (MISC and

NESZ). For the post-general election periods, all the significant trading volume are with a

positive sign, which indicates a high trading volume in four of the GLCs (CIMB, MAY,

UMWH, and CCM) and three of the Non-GLCs (NESZ, ROTH, and IOI).

By combining the finding on the returns volatility and trading volume for the pre-

general election periods in 2006-2015, there are high volatility of returns and high trading

volume for GLCs (CIMB, MAY, TNB, and UMWH) and Non-GLCs (MISC). For post-

general election periods, the findings show that high volatility of returns of NESZ (Non-

GLCs) is also associated with a high trading volume. All these findings are in line with the

127

predictions of Admati and Pfleiderer (1988). In addition, a plot of trading volume and stock

prices as shown in Figure 4.3 and Figure 4.4 would further explain the relationship between

the volume of the highly traded stock and its stock volatility. Figure 3 shows the trading

volume and stock prices for the four highly traded GLCs before general elections in the years

2008 and 2013. In year 2008, high trading volume of the four GLCs stock is associated with a

sharp price drop before the general election. In another words, these GLCs stock is sold in

large quantities which caused a sharp decrease of stock price before the 12th general election.

The same case happened on the highly trade Non-GLCs stock of MISC as shown in Figure 4.

Nevertheless, before the 13th general election in year 2013, the market is very volatility and

there is a mixture of buying and selling activity in the market. For example, the stock price of

CIMB is highly volatile before general election. In some of the days, the stock is purchased in

large quantities and the stock price went up sharply. But selling activity also existed as shown

by the high trading volume with sharp decrease in price. For the Non-GLCs of MISC, the

stock price was not as volatile as the GLCs, it decreased on a particular day and then stabilize.

Selling activity is shown by the high trading volume and low stock price of the MISC.







128



[Insert Figure 4.3: Trading Volume and Stock Prices for the Highly Traded GLCs

before the General Elections]

[Insert Figure 4.4: Trading Volume and Stock Prices for the Highly Traded Non-GLCs

before the General Elections]

4.5 Conclusion

This study investigates the election effects on stock market volatility for Malaysian

Government Link Companies (GLCs) and Non-Government Link Companies (Non-GLCs)

for the period of January 4, 1994, through December 31, 2015. Using the Threshold GARCH

or GJR GARCH (1,1) model, this study analyzes the daily stock prices of eleven GLCs and

eleven Non-GLCs to see the pre-general election effect and post-general election effect

specifically. Furthermore, to test whether the pattern of the stock volatility changes according

to the political condition, the full sample period is divided into early years (1994-2005), and

later years (2006-2015). The year 2006 has been chosen as the cut-off date because the 12th

general election (2008) and the 13th general election (2013) induced election uncertainty with

a strong expectation of political changeover and eventually the incumbent lost its two-thirds

majority in the Parliament. Interestingly, the findings of this study show that the full sample

period result is likely dominated by the results of the second sub-sample period of (2006-

2015). By dividing the sample period, the pattern of the stock volatility in GLCs and Non-

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GLCs is clearly different in the two sub-samples, which is in line with the political condition

in the early years of 1994-2005 and the later years of 2006-2015.

For the first sub-sample period of 1994 to 2005, there is an asymmetric effect of

political elections on stock volatility in both the GLCs and Non-GLCs. Moreover, this study

observes the low volatility of returns during the pre-general election period in both the GLCs

and Non-GLCs stock indices. No significant result is found on the analysis of trading volume,

except for one GLCs stock index (BOUS) and one Non-GLCs stock index (NESZ). The low

volatility in the market before election gives a positive signal that there is no uncertainty due

to the general election. After the general election, although there were no unexpected

outcomes as the coalition of Barisan Nasional won in the general elections, the stock

volatility increased for some of the GLCs and Non-GLCs stocks. Further analysis of trading

volume shows that there is high trading volume for a few of the GLCs and Non-GLCs stock

indices, which lend support to the argument of Admati and Pfleiderer (1988). During the

years of 1994-2005, investors were expecting positively aligned with the stable political

condition in the country. When the stock market reopened after the election day, the active

trading activity typically explained the high stock volatility in both the GLCs and Non-GLCs

stock.

In later years of 2006-2015, the findings from the second sub-sample show that most

of the GLCs and Non-GLCs stock prices were highly volatile before the general election.

Specifically, seven of the GLCs stock indices and six of the Non-GLCs stock indices

encountered significant high volatility in pre-general election periods. According to the

political condition during that period, the high volatility in the market was due to

uncertainties associated with the 12th and 13th Malaysian general election. Interestingly, those

130

GLCs and Non-GLCs with high market capitalization that encountered high volatility is also

associated with a significant high trading volume, for example, MAY, CIMB, TNB of GLCs,

and MISC, IOI of Non-GLCs. This pattern of trading was probably due to the willingness of

liquidity traders to trade in the periods where the prices are more volatile (Admati and

Pfleiderer, 1988). Nonetheless, during that period, it is not surprising that local investors took

the opportunity to trade actively before the general election, which is totally different with the

foreign investors trading strategy and expectation. Majority of the liquidity traders in

Malaysian stock market are from the local institutional investors, such as domestic pension

funds, insurance companies, and mutual funds. They would closely monitor the local market

development and trade confidently in those high market capitalized stock index before the

general election.

Another interesting point found in this study is that when there is uncertainty in the

market, the stock price of the GLCs and Non-GLCs in the finance sector react differently in

term of volatility and trading volume. High volatility and high trading volume are found in

GLCs before and after the 12th and 13th general election. Meanwhile, the Non-GLCs in the

finance sector have low volatility with insignificant trading volume around the general

election. Despite the market uncertainties, investors are still willing to actively trade the

GLCs stock. However, this trading pattern is not observed in early years with the stable

political condition. This indicates that investors are very careful during the time of market

uncertainty as it is always safer to trade GLCs stock which is more liquid than others.

Nonetheless, based on the finding of this study, this trading pattern appear only in the finance

sector, but not in other sectors.

131


is associated with investors' behaviour during the periods of the general election. The

presence of a political shock during general election makes it possible to investigate how the

politically connected firms react to the market uncertainty. Our study provides an exemplar

for further studies to explore further details by employing a comprehensive disaggregated

data for different sectors and firm characteristics and sectors.

132

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135

Table 4.1: Malaysia General Election

Dissolution of Parliament Election Day 1st Parliament Assembly after Election 9th General Election

(1995) 6 April 1995 (Thursday)

25 April 1995 (Tuesday)

7 June 1995 (Wednesday)

10th General Election (1999)

11 November 1999 (Wednesday)

29 November 1999 (Monday)

20 December 1999 (Monday)


4 March 2004 (Thursday)

21 March 2004 (Sunday)

17 May 2004 (Monday)







5 May 2013 (Sunday)


136

Table 4.2: Descriptive Statistics for GLCs Stock Return (1994 – 2015) AHB BIMB CIMB MAY MBS BOUS TMK TNB UMWH CCM MRC Sectors Finance Finance Finance Finance Finance Trade &

Services Trade & Services

Trade & Services

Consumer Products

Industrial Products

Property

Mean -0.0111 -0.0024 0.0171 0.0112 -0.0064 0.0015 0.0088 0.0007 0.0192 -0.0087 -0.0243 Max 37.3026 29.7458 39.0198 28.2448 25.9996 19.2706 24.0617 31.7173 45.8182 15.2078 42.5611 Min -39.7003 -25.9605 -26.8912 -26.6786 -28.7955 -17.0642 -31.2550 -26.5606 -26.7595 -13.2311 -41.8419 Std. Dev. 2.5777 2.4351 2.5542 1.8591 3.0686 1.8999 1.9791 2.1068 2.3287 1.8089 3.6110 Skewness 1.0396 0.7895 1.1848 0.8466 0.6498 0.2262 -0.5603 1.0175 1.9510 0.4567 0.4850 Kurtosis 34.5299 22.3824 32.6010 33.3981 14.0606 16.8216 31.7123 32.8133 62.4940 11.0319 18.2961 Jarque-Bera

238714.40 90414.70 210832.50 221608.70 29652.64 45722.67 197398.90 213494.80 849885.40 15622.91 56163.37

Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Table 4.3: Descriptive Statistics for Non-GLCs Stock Return (1994 – 2015) PBK RHBC GENM GENT MISC YTL NESZ PEP ROTH IOI KLK Sectors Finance Finance Trade &

Services Trade & Services

Trade & Services

Trade & Services

Consumer Products

Consumer Products

Consumer Products

Plantation Plantation

Mean 0.0333 -0.0012 0.0041 0.0072 0.0168 0.0191 0.0255 0.0291 0.0168 0.0336 0.0344 Max 21.8915 45.2147 23.4994 19.9001 23.7293 28.2004 25.9958 18.0921 18.6877 25.7966 27.6331 Min -19.1699 -23.7534 -25.8235 -18.4922 -27.5225 -29.9099 -25.9511 -13.8150 -34.1749 -27.9109 -23.7400 Std. Dev. 1.7523 2.7412 2.3699 2.0926 1.8754 2.2346 1.4925 1.6799 1.6442 2.3958 1.9847 Skewness 1.1010 1.6224 0.4367 0.1034 -0.0231 0.2928 -0.4409 0.2213 -1.3616 -0.0685 0.1652 Kurtosis 30.7617 32.7859 14.1505 9.9857 31.5312 34.4055 82.0309 13.4096 44.0384 19.2672 26.8057 Jarque-Bera

185423.20 214631.80 29908.55 11677.54 194622.10 235891.00 1493473.00 25953.88 404426.10 63270.90 135517.00

Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

137

Table 4.4: Sub-Sample Mean Return for GLCs

GLCs AHB BIMB CIMB MAY MBS BOUS TMK TNB UMWH CCM MRC Sectors FIN FIN FIN FIN FIN TRAD TRAD TRAD CONPR INDPRO PROP

Sub-sample 1 1994 – 2005

Pre-GE -0.4516 0.1128 0.3219 0.1348 -0.1630 0.2596 0.2042 0.3196 -0.0678 -0.0179 -0.2635 Post-GE 0.0084 -0.0343 -0.0121 -0.0038 0.0374 -0.1548 0.1105 -0.1983 0.2109 0.1962 -0.4906

Sub-sample 2 2006 - 2015

Pre-GE 0.0572 0.0001 -0.1066 -0.1449 -0.3423 -0.3282 -0.1117 -0.0900 -0.2315 -0.2659 -0.9675 Post-GE 0.0978 0.0267 0.0643 -0.0613 0.3985 0.0405 0.1867 -0.2835 0.1219 0.0868 -0.3581

Note: FIN: Finance, TRAD: Trade and Services, CONPR: Consumer Product, INDPRO: Industrial Product, PROP: Property.

Table 4.5: Sub-Sample Mean Return for Non-GLCs

Non-GLCs PBK RHBC GENM GENT MISC YTL NESZ PEP ROTH IOI KLK Sectors FIN FIN TRAD TRAD TRAD TRAD CONPR CONPR CONPR PLANT PLANT

Sub-sample 1 1994 - 2005

Pre-GE -0.0060 0.0449 -0.0766 0.0130 -0.0801 0.0398 0.2246 0.0522 0.1717 -0.2019 0.1058 Post-GE 0.0319 -0.0568 0.0502 -0.1604 -0.0114 -0.1198 0.1561 -0.1123 0.1533 0.0738 -0.1022

Sub-sample 2 2006 - 2015

Pre-GE -0.2400 -0.2356 -0.2755 -0.3407 -0.8856 -0.1532 0.0805 -0.2540 -0.0938 0.0239 -0.3134 Post-GE 0.1808 0.0898 0.0028 -0.0093 0.3637 -0.0111 0.3261 0.2371 -0.1405 0.0879 0.0995

Note: FIN: Finance, TRAD: Trade and Services, CONPR: Consumer Product, PLANT: Plantation.

138

Table 4.6: Selected Sample of Malaysian Government-Link Companies (GLCs)

No Company (Industry) Symbol Market Capitalization

(RM)*

Number of Share*

Earning Per Share

(Cent)*

Revenue (RM,000)#

Profit Before Tax

(RM,000)#

Net Profit (RM,000)#

1 Affin Holdings Berhad (Finance)

AHB 4.216b 1.943b 23.30 464,834 139,160 97,407

2 BIMB Holding Berhad (Finance)

BIMB 6.943b 1.589b 35.29 884,257 200,261 161,864

3 CIMB Group Holdings Berhad (Finance)

CIMB 44.165b 8.868b 37.39 4,0,41,563 1,132,161 825,739

4 Malayan Banking Berhad (Finance)

MAY 80.119b 10.193b 60.21 11,052,259 2,376,103 1,652,082

5 Malaysian Building Society Berhad (Finance)

MBS 5.277b 5.799b 2.51 825,687 1,312 -15,809

6 Boustead Holdings Berhad (Trading-Services)

BOUS 4.358b 2.027b 10.58 2,442,300 49,700 4,200

7 Telekom Malaysia Berhad (Trading-Services)

TMK 24.539b 3.758b 21.85 3,184,430 224,696 192,427

8 Tenaga Nasional Berhad (Trading-Services)**

TNB 81.268b 5.644b 130.55 11,744,000 1,412,500 820,900

9 UMW Holdings Berhad (Consumer Products)

UMWH 6.811b 1.168b -22.94 4,160,904 -334,250 -286,040

10 Chemical Company of Malaysia Berhad (Industrial Products)

CCM 414.16m 457.63m -10.12 160,105 19,313 -76,672

11 Malaysian Resources Corporation Berhad (Properties)

MRC 2.829b 2.080b 3.96 388,200 377 26,789

Note: *Calculated based on the net profit of the trailing twelve months and latest number of shares issued. #Calculated based on the amount declared for financial year ended quarter 4, 2015-12-31. **Calculated based on the amount declared for financial year ended 2015-08-31.

139

Table 4.7: Selected Sample of Malaysian Non-Government-Link Companies (Non-GLCs)

No Company (Industry) Symbol Market Capitalization

(RM)*

Number of Share*

Earning Per Share

(Cent)*

Revenue (RM,000)#

Profit Before Tax

(RM,000)#

Net Profit (RM,000)#

1 Public Bank Berhad (Finance)

PBK 77.643b 3.882b 134.37 4,929,046 1,857,776 1,492,428

2 RHB Bank Berhad (Finance)

RHBC 19.288b 4.010b 35.23 2,848,457 475,206 316,120

3 Genting Malaysia Berhad (Trading-Services)

GENM 28.503b 5.938b 21.94 2,291,879 357,846 338,558

4 Genting Berhad (Trading-Services)

GENT 29.175b 3.750b 30.02 4,919,421 726,691 338,946

5 MISC Berhad (Trading-Services)

MISC 33.568b 4.464b 70.65 3,312,062 763,133 752,720

6 YTL Corporation Berhad (Trading-Services)

YTL 17.443b 10.902b 8.51 4,115,753 520,117 298,928

7 NESTLE Malaysia Berhad (Consumer Products)

NESZ 18.380b 234.50m 285.70 1,198,942 118,677 99,789

8 PPB Group Berhad (Consumer Products)

PEP 18.826b 1.185b 67.76 1,090,600 378,748 341,021

9 British American Tobacco (M) (Consumer Products)

ROTH 13.882b 285.53m 220.32 1,057,992 272,557 196,121

10 IOI Corporation Berhad** (Plantation)

IOI 28.884b 6.462b 10.76 2,942,000 203,300 159,700

11 Kuala Lumpur Kepong Berhad*** (Plantation)

KLK 25.577b 1.068b 131.47 3,932,083 232,510 186,288

Note: *Calculated based on the net profit of the trailing twelve months and latest number of shares issued. #Calculated based on the amount declared for financial year ended quarter 4, 2015-12-31. **Calculated based on the amount declared for financial year ended 2015-06-30. ***Calculated based on the amount declared for financial year ended 2015-09-30.

140


Variables AHB BIMB CIMB MAY MBS

Sector Finance Finance Finance Finance Finance (p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1)

Mean Equation

0 -0.0149 (0.4830)

-0.0202 (0.4525)

0.0149 (0.4661)

0.0107 (0.4917)

0.0172 (0.6183)

PGE 0.1183 (0.6269)

0.0373 (0.8750)

0.1322 (0.7640)

0.0809 (0.6745)

-0.1407 (0.6923)

PtGE 0.0914 (0.5682)

0.0663 (0.5722)

-0.0992 (0.4905)

-0.1271 (0.3134)

-0.1407 (0.5507)

1tR 0.0158 (0.2480)

-0.1039 (0.0000)***

0.0535 (0.0000)***

0.0044 (0.7420)

-0.0593 (0.0001)***

Variance Equation

0 0.0459 (0.0000)***

0.4124 (0.0000)***

0.0196 (0.0000)***

0.0242 (0.0000)***

0.5563 (0.0000)***

1 0.1054 (0.0000)***

0.1569 (0.0000)***

0.0450 (0.0000)***

0.0584 (0.0000)***

0.1616 (0.0000)***

i 0.0221 (0.0005)***

0.0072 (0.3593)

0.0475 (0.0000)***

0.0296 (0.0000)***

0.0022 (0.8343)

1 0.8864 (0.0000)***

0.7757 (0.0000)***

0.9312 (0.0000)***

0.9188 (0.0000)***

0.7901 (0.0000)***

PGE 0.1115 (0.0640)*

0.0149 (0.8965)

0.4018 (0.0000)***

0.0767 (0.0042)***

0.0841 (0.3964)

PtGE 0.0196 (0.5686)

-0.2277 (0.0000)***

-0.1039 (0.0000)***

-0.0002 (0.9873)

0.8614 (0.0000)***


5 lags 0.0368 0.9252 0.0034 0.0009 0.6350 10 lags 0.0804 0.9962 0.0079 0.0108 0.7070



F-stat 0.7572 0.8384 0.7290 0.5299 0.7806 Chi-Square 0.7572 0.8384 0.7290 0.5298 0.7806


Chi-Square 0.1254 0.0001 0.0000 0.0104 0.0000 Note: ***, ** and * denote significance at 1, 5 and 10% levels respectively. Numbers in parentheses depict p-value. The null hypothesis of the Wald Test is 0: 210 H (same average return / volatility for pre- and post-General Election).

141


Variables BOUS TMK TNB UMWH CCM MRC

Sector Trade & Services

Trade & Services

Trade & Services

Consumer Products

Industrial Products

Property

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0089 (0.6511)

0.0247 (0.1780)

0.0212 (0.2812)

0.0302 (0.0999)*

-0.0113 (0.5746)

-0.0223 (0.5193)

PGE 0.0544 (0.8005)

0.0667 (0.6445)

0.2706 (0.4103)

-0.1150 (0.5328)

0.0285 (0.8474)

0.0452 (0.9191)

PtGE -0.0844 (0.5453)

-0.0925 (0.2368)

-0.3141 (0.0973)*

0.1064 (0.4128)

0.2164 (0.0808)*

-0.5235 (0.0202)**

1tR 0.0192 (0.1533)

0.0591 (0.0000)***

0.0085 (0.5112)

-0.0051 (0.6997)

-0.0896 (0.0000)***

-0.0259 (0.0494)**

Variance Equation

0 0.0844 (0.0000)***

0.0448 (0.0000)***

0.0348 (0.0000)***

0.0224 (0.0000)***

0.1793 (0.0000)***

0.1515 (0.0000)***

1 0.0957 (0.0000)***

0.1224 (0.0000)***

0.0595 (0.0000)***

0.0616 (0.0000)***

0.1411 (0.0000)***

0.0603 (0.0000)***

i 0.0060 (0.3109)

-0.0143 (0.0917)*

0.0399 (0.0000)***

0.0275 (0.0000)***

0.0376 (0.0000)***

0.0510 (0.0000)***

1 0.8812 (0.0000)***

0.8860 (0.0000)***

0.9155 (0.0000)***

0.9223 (0.0000)***

0.8036 (0.0000)***

0.9062 (0.0000)***

PGE 0.0643 (0.0725)*

-0.0288 (0.0342)**

0.3639 (0.0000)***

0.0788 (0.0160)**

-0.0762 (0.1965)

1.0793 (0.0000)***

PtGE 0.1121 (0.0000)***

-0.0267 (0.0194)**

0.0271 (0.3242)

0.0252 (0.1462)

0.0096 (0.8351)

-0.1084 (0.0420)**


5 lags 0.3619 0.8070 0.1535 0.0088 0.7910 0.0110 10 lags 0.5018 0.8701 0.2008 0.0195 0.7298 0.0768

Ljung-Box Q2 Statistic (p-value) 5 lags 0.3650 0.8030 0.1560 0.0070 0.7930 0.0110

10 lags 0.4930 0.8660 0.1740 0.0150 0.7270 0.0690 Return Equation: Wald Test (p-value)

F-stat 0.8034 0.4180 0.2156 0.5940 0.2137 0.0523 Chi-Square 0.8034 0.4180 0.2155 0.5940 0.2136 0.0522

Variance Equation: Wald Test (p-value) F-stat 0.0000 0.0019 0.0000 0.0029 0.4320 0.0000

Chi-Square 0.0000 0.0019 0.0000 0.0029 0.4319 0.0000 Note: ***, ** and * denote significance at 1, 5 and 10% levels respectively. Numbers in parentheses depict p-value. The null hypothesis of the Wald Test is 0: 210 H (same average return / volatility for pre- and post-General Election).

142


Variables PBK RHBC GENM GENT MISC

Sector Finance Finance Trade and Services

Trade and Services

Trade and Services

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0424 (0.0009)***

0.0264 (0.2622)

-0.0015 (0.9537)

0.0082 (0.7276)

0.0166 (0.3882)

PGE -0.0782 (0.4959)

-0.1258 (0.5892)

-0.1168 (0.6947)

-0.1560 (0.5337)

-0.3930 (0.2648)

PtGE 0.0341 (0.6294)

-0.0670 (0.5910)

-0.0664 (0.7006)

-0.2341 (0.1581)

0.0404 (0.8064)

1tR 0.0463 (0.0005)***

0.0112 (0.3528)

-0.0249 (0.0514)*

0.0032 (0.8142)

-0.0984 (0.0000)***

Variance Equation

0 0.0077 (0.0000)***

0.0176 (0.0000)***

0.0423 (0.0000)***

0.0767 (0.0000)***

0.0334 (0.0000)***

1 0.0711 (0.0000)***

0.0256 (0.0000)***

0.0505 (0.0000)***

0.0733 (0.0000)***

0.0405 (0.0000)***

i 0.0263 (0.0000)***

0.0264 (0.0000)***

0.0228 (0.0000)***

0.0177 (0.0073)***

0.0225 (0.0000)***

1 0.9222 (0.0000)***

0.9591 (0.0000)***

0.9323 (0.0000)***

0.9016 (0.0000)***

0.9378 (0.0000)***

PGE -0.0029 (0.4954)

-0.0258 (0.0171)**

0.1289 (0.0158)**

0.1361 (0.0458)**

0.3335 (0.0000)***

PtGE -0.0051 (0.3323)

-0.0179 (0.0243)**

-0.0092 (0.8078)

0.0271 (0.4602)

-0.0439 (0.0002)***


5 lags 0.0320 0.0000 0.1918 0.2964 0.1537 10 lags 0.1592 0.0008 0.1028 0.5296 0.4830



F-stat 0.7122 0.7611 0.8735 0.2934 0.5307 Chi-Square 0.7122 0.7611 0.8735 0.2934 0.5306



143


Variables YTL NESZ PEP ROTH IOI KLK

Sector Trade and Services

Consumer Products

Consumer Products

Consumer Products


(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0050 (0.8126)

0.0281 (0.0567)*

0.0385 (0.0401)**

0.0213 (0.2129)

0.0344 (0.1165)

0.0549 (0.0144)**

PGE 0.0826 (0.7633)

0.1970 (0.2423)

-0.0847 (0.5738)

0.0531 (0.7787)

0.0260 (0.9254)

-0.0136 (0.9516)

PtGE -0.1602 (0.1528)

0.2545 (0.0027)***

0.0274 (0.8178)

-0.0579 (0.5384)

0.0454 (0.7859)

-0.0306 (0.7728)

1tR -0.0571 (0.0000)***

-0.1880 (0.0000)***

-0.0315 (0.0282)**

-0.1197 (0.0000)***

-0.0037 (0.7722)

-0.0277 (0.0454)**

Variance Equation

0 0.0482 (0.0000)***

0.0126 (0.0000)***

0.0661 (0.0000)***

0.0400 (0.0000)***

0.0444 (0.0000)***

0.0816 (0.0000)***

1 0.0545 (0.0000)***

0.0940 (0.0000)***

0.0684 (0.0000)***

0.0525 (0.0000)***

0.0602 (0.0000)***

0.0758 (0.0000)***

i 0.0431 (0.0000)***

-0.0642 (0.0000)***

0.0450 (0.0000)***

0.0124 (0.0127)**

0.0462 (0.0000)***

0.0071 (0.2191)

1 0.9164 (0.0000)***

0.9433 (0.0000)***

0.8892 (0.0000)***

0.9240 (0.0000)***

0.9109 (0.0000)***

0.8998 (0.0000)***

PGE 0.1270 (0.0020)***

0.0111 (0.2705)

-0.0308 (0.1187)

0.0309 (0.2744)

0.2412 (0.0295)**

0.0357 (0.0570)*

PtGE -0.0301 (0.0273)**

-0.0159 (0.0926)*

0.0260 (0.0893)*

-0.0289 (0.0004)***

-0.0215 (0.6584)

-0.0313 (0.0483)**


5 lags 0.5202 0.9095 0.1578 0.0029 0.0743 0.1952 10 lags 0.6257 0.9977 0.2779 0.0323 0.2249 0.5975



F-stat 0.3316 0.0061 0.8382 0.7880 0.9596 0.9580 Chi-Square 0.3315 0.0061 0.8382 0.7879 0.9596 0.9580



144

Table 4.10(a): Threshold GARCH Results for Pre-General Election and Post-General

Election (1994 - 2005) – Government Link Companies (GLCs)

Variables AHB BIMB CIMB MAY MBS Sector Finance Finance Finance Finance Finance (p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1)

Mean Equation

0 -0.0341 (0.4168)

-0.0849 (0.0245)**

0.0152 (0.6806)

0.0229 (0.4298)

-0.0265 (0.6264)

PGE -0.2566 (0.5354)

0.0556 (0.8371)

0.2765 (0.5547)

0.1404 (0.6213)

0.2051 (0.6291)

PtGE 0.0614 (0.7724)

0.0691 (0.5973)

-0.2131 (0.4149)

-0.0867 (0.6060)

0.0601 (0.8472)

1tR 0.0336 (0.0680)*

-0.1230 (0.0000)***

0.0667 (0.0001)***

-0.0060 (0.7349)

-0.0739 (0.0015)***

Variance Equation

0 0.1484 (0.0000)***

0.3736 (0.0000)***

0.0603 (0.0000)***

0.0582 (0.0000)***

1.7300 (0.0000)***

1 0.0862 (0.0000)***

0.1073 (0.0000)***

0.0473 (0.0000)***

0.0497 (0.0000)***

0.2932 (0.0000)***

i 0.0409 (0.0000)***

0.0730 (0.0000)***

0.0523 (0.0000)***

0.0465 (0.0000)***

-0.0730 (0.0011)***

1 0.8795 (0.0000)***

0.8052 (0.0000)***

0.9213 (0.0000)***

0.9139 (0.0000)***

0.6204 (0.0000)***

PGE 0.0046 (0.9767)

-0.2311 (0.0092)***

-0.0011 (0.9911)

-0.0257 (0.7234)

-1.3497 (0.0000)***

PtGE 0.0061 (0.9528)

-0.2689 (0.0000)***

0.1212 (0.0579)*

-0.0295 (0.4275)

-0.1361 (0.4613)


5 lags 0.0079 0.9996 0.0284 0.0041 0.9625 10 lags 0.0120 1.0000 0.0425 0.0332 0.9935



F-stat 0.7905 0.8467 0.6083 0.7736 0.8752 Chi-Square 0.7905 0.8467 0.6083 0.7735 0.8752



145




Trade & Services

Trade & Services

Consumer Products

Industrial Products

Property

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 -0.0128 (0.6891)

-0.0406 (0.1867)

-0.0144 (0.6546)

0.0121 (0.7153)

0.0034 (0.9065)

-0.0895 (0.0999)*

PGE 0.2402 (0.3748)

0.1684 (0.5929)

0.4230 (0.2477)

-0.2255 (0.2980)

0.1506 (0.3903)

0.1461 (0.7442)

PtGE -0.1136 (0.5128)

0.0199 (0.9208)

-0.3757 (0.2116)

0.0824 (0.6677)

0.1798 (0.1974)

-0.4796 (0.0872)*

1tR 0.0012 (0.9505)

0.0428 (0.0104)**

-0.0097 (0.5782)

0.0134 (0.4561)

-0.0317 (0.1177)

-0.0256 (0.1575)

Variance Equation

0 0.1425 (0.0000)***

0.0460 (0.0000)***

0.0778 (0.0000)***

0.0550 (0.0000)***

0.1977 (0.0000)***

0.2952 (0.0000)***

1 0.0646 (0.0000)***

0.0431 (0.0000)***

0.0524 (0.0000)***

0.0474 (0.0000)***

0.1408 (0.0000)***

0.0621 (0.0000)***

i 0.0247 (0.0006)***

0.0440 (0.0000)***

0.0637 (0.0000)***

0.0637 (0.0000)***

0.0519 (0.0000)***

0.0708 (0.0000)***

1 0.8891 (0.0000)***

0.9258 (0.0000)***

0.9018 (0.0000)***

0.9144 (0.0000)***

0.8027 (0.0000)***

0.8902 (0.0000)***

PGE -0.1576 (0.0073)***

-0.0837 (0.4251)

0.0225 (0.8506)

-0.1361 (0.0100)**

-0.2677 (0.0000)***

-0.4658 (0.0556)*

PtGE 0.1007 (0.0001)***

0.0942 (0.1145)

0.2205 (0.0004)***

0.1818 (0.0006)***

-0.0211 (0.6488)

-0.0349 (0.6900)


5 lags 0.8799 0.0000 0.0047 0.0442 0.6829 0.0021 10 lags 0.9688 0.0006 0.0098 0.1035 0.5070 0.0162



F-stat 0.5850 0.8640 0.2080 0.5288 0.2834 0.2300 Chi-Square 0.5849 0.8640 0.2078 0.5288 0.2833 0.2298



146




Trade and Services

Trade and Services

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0246 (0.3898)

-0.0161 (0.7033)

-0.0188 (0.6455)

0.0168 (0.6481)

0.0352 (0.2723)

PGE -0.0838 (0.6761)

0.0009 (0.9988)

-0.0707 (0.8630)

0.0238 (0.9424)

-0.1619 (0.1473)

PtGE -0.0608 (0.7152)

-0.3541 (0.1352)

-0.1058 (0.6737)

-0.3971 (0.1005)

-0.1204 (0.4091)

1tR 0.0283 (0.1063)

0.0328 (0.0357)**

0.0310 (0.0714)*

0.0052 (0.7827)

-0.1800 (0.0000)***

Variance Equation

0 0.0583 (0.0000)***

0.0188 (0.0000)***

0.0402 (0.0000)***

0.1591 (0.0000)***

0.1923 (0.0000)***

1 0.0478 (0.0000)***

0.0147 (0.0000)***

0.0404 (0.0000)***

0.0947 (0.0000)***

0.0805 (0.0000)***

i 0.0581 (0.0000)***

0.0328 (0.0000)***

0.0329 (0.0000)***

0.0006 (0.9530)

0.0366 (0.0040)***

1 0.9109 (0.0000)***

0.9682 (0.0000)***

0.9406 (0.0000)***

0.8760 (0.0000)***

0.8507 (0.0000)***

PGE -0.1120 (0.0011)***

0.0421 (0.7397)

-0.0367 (0.7716)

-0.0370 (0.8109)

-0.2352 (0.0000)***

PtGE 0.1296 (0.0000)***

-0.0227 (0.6512)

0.0814 (0.2723)

0.1050 (0.2583)

0.1116 (0.0472)**


5 lags 0.0323 0.0001 0.6665 0.8752 0.9954 10 lags 0.1723 0.0029 0.5142 0.5297 1.0000



F-stat 0.8630 0.3253 0.9047 0.2584 0.2792 Chi-Square 0.8630 0.3251 0.9047 0.2583 0.2790



147




Consumer Products

Consumer Products

Consumer Products


(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0211 (0.5526)

0.0339 (0.2346)

0.0432 (0.1122)

0.0258 (0.2943)

0.0665 (0.0778)*

0.0318 (0.2901)

PGE 0.1475 (0.6249)

0.0979 (0.5496)

-0.0599 (0.7322)

0.2192 (0.4754)

-0.2694 (0.4809)

0.1547 (0.5052)

PtGE -0.2498 (0.1032)

0.1266 (0.3959)

-0.1123 (0.4473)

0.0138 (0.9025)

0.0631 (0.7999)

-0.0930 (0.5662)

1tR -0.0193 (0.2799)

-0.1624 (0.0000)***

-0.0519 (0.0061)***

-0.1082 (0.0000)***

0.0039 (0.8266)

-0.0536 (0.0055)***

Variance Equation

0 0.0544 (0.0000)***

0.7306 (0.0000)***

0.0512 (0.0000)***

0.0263 (0.0000)***

0.0952 (0.0000)***

0.0960 (0.0000)***

1 0.0414 (0.0000)***

0.1313 (0.0000)***

0.0445 (0.0000)***

0.0441 (0.0000)***

0.0510 (0.0000)***

0.0993 (0.0000)***

i 0.0578 (0.0000)***

0.3652 (0.0000)***

0.0617 (0.0000)***

0.0132 (0.0198)**

0.0548 (0.0000)***

0.0073 (0.4303)

1 0.9258 (0.0000)***

0.4913 (0.0000)***

0.9121 (0.0000)***

0.9404 (0.0000)***

0.9079 (0.0000)***

0.8767 (0.0000)***

PGE -0.1136 (0.0848)*

-0.4460 (0.0000)***

-0.0752 (0.0006)***

0.0609 (0.1418)

0.1079 (0.4887)

-0.0840 (0.0727)*

PtGE 0.0482 (0.1507)

-0.0150 (0.9220)

0.0575 (0.0028)***

-0.0417 (0.0000)***

0.0877 (0.2966)

0.0688 (0.1030)


5 lags 0.8860 0.9983 0.0669 0.0125 0.3523 0.7499 10 lags 0.9097 1.0000 0.2974 0.0852 0.7173 0.8518



F-stat 0.2393 0.5932 0.7178 0.7704 0.7576 0.6814 Chi-Square 0.2391 0.5932 0.7178 0.7703 0.7576 0.6813



148




Mean Equation

0 -0.0044 (0.8562)

0.0509 (0.1999)

0.0165 (0.5140)

0.0049 (0.7773)

0.0517 (0.2247)

PGE 0.2480 (0.3363)

-0.0239 (0.9542)

0.0526 (0.9379)

0.0339 (0.8954)

-0.3488 (0.5589)

PtGE 0.1746 (0.5322)

0.1641 (0.5098)

-0.1832 (0.3725)

-0.1300 (0.4321)

-0.2758 (0.5419)

1tR -0.0139 (0.5141)

-0.0871 (0.0001)***

0.0350 (0.0764)*

0.0177 (0.4065)

-0.0271 (0.2198)

Variance Equation

0 0.0740 (0.0000)***

0.5095 (0.0000)***

0.0219 (0.0000)***

0.0302 (0.0000)***

0.3394 (0.0000)***

1 0.1608 (0.0000)***

0.2402 (0.0000)***

0.0407 (0.0000)***

0.0840 (0.0000)***

0.1422 (0.0000)***

i -0.0187 (0.1216)

-0.1314 (0.0000)***

0.0501 (0.0000)***

0.0186 (0.0584)*

-0.0006 (0.9716)

1 0.8532

(0.0000)*** 0.7231

(0.0000)*** 0.9269

(0.0000)*** 0.8847

(0.0000)*** 0.8142

(0.0000)***

2 -0.0154 (0.9009)

-- --

-- --

-- --

-- --

PGE 0.1130 (0.1944)

0.4773 (0.1736)

0.5106 (0.0000)***

0.1434 (0.0014)***

0.6348 (0.0001)***

PtGE 0.1195 (0.0446)**

0.0957 (0.5813)

-0.1131 (0.0000)***

0.0480 (0.0602)*

1.4029 (0.0009)***


5 lags 0.5198 0.4934 0.5573 0.2690 0.8068 10 lags 0.8541 0.7372 0.7500 0.4778 0.7912



F-stat 0.4919 0.8048 0.6559 0.7165 0.6917 Chi-Square 0.4918 0.8048 0.6559 0.7165 0.6917



149




Trade & Services

Trade & Services

Consumer Products

Industrial Products

Property

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (2, 1) (1, 1) Mean Equation

0 0.0341 (0.1625)

0.0780 (0.0005)***

0.0447 (0.0727)*

0.0419 (0.0596)*

-0.0217 (0.4414)

0.0348 (0.4748)

PGE -0.4833 (0.2577)

-0.0933 (0.5442)

0.2852 (0.7308)

-0.0655 (0.8064)

-0.0578 (0.8484)

-0.6034 (0.6305)

PtGE -0.0119 (0.9709)

-0.1552 (0.0854)*

-0.2090 (0.2223)

0.0759 (0.7097)

0.1224 (0.6404)

-0.6795 (0.1527)

1tR 0.0342 (0.1127)

0.0657 (0.0004)***

0.0389 (0.0994)*

-0.0292 (0.1406)

-0.1496 (0.0000)***

-0.0302 (0.1478)

Variance Equation

0 0.1208 (0.0000)***

0.0559 (0.0000)***

0.0856 (0.0000)***

0.0256 (0.0000)***

0.2089 (0.0000)***

0.1276 (0.0000)***

1 0.1995 (0.0000)***

0.2925 (0.0000)***

0.1517 (0.0000)***

0.0768 (0.0000)***

0.0860 (0.0000)***

0.0689 (0.0000)***

2 -- --

-- --

-- --

-- --

0.0683 (0.0000)***

-- --

i -0.0518 (0.0045)***

-0.2329 (0.0000)***

0.0072 (0.5719)

-0.0054 (0.4702)

0.0090 (0.4532)

0.0123 (0.1385)

1 0.7848 (0.0000)***

0.8450 (0.0000)***

0.8238 (0.0000)***

0.9035 (0.0000)***

0.7726 (0.0000)***

0.9082 (0.0000)***

PGE 0.8275 (0.0000)***

-0.0351 (0.2080)

0.9302 (0.0013)***

0.1476 (0.0016)***

0.3486 (0.1012)

3.7682 (0.0000)***

PtGE 0.1871 (0.0052)***

-0.0439 (0.0255)**

-0.0234 (0.4004)

-0.0003 (0.9890)

0.3268 (0.1082)

-0.3252 (0.0206)**


5 lags 0.1833 0.9841 0.8293 0.1113 0.5415 0.9398 10 lags 0.3492 0.9973 0.8289 0.2836 0.8875 0.9944



F-stat 0.5267 0.1729 0.4349 0.9033 0.8796 0.3304 Chi-Square 0.5266 0.1727 0.4348 0.9033 0.8796 0.3302



150




Trade and Services

Trade and Services

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 0.0436 (0.0017)***

0.0530 (0.0616)*

0.0151 (0.6412)

-0.0084 (0.7769)

-0.0084 (0.7405)

PGE -0.0422 (0.5721)

-0.1697 (0.4651)

-0.1322 (0.7536)

-0.2152 (0.5149)

-0.5580 (0.3205)

PtGE 0.0797 (0.3192)

-0.0132 (0.9217)

-0.0108 (0.9607)

-0.1194 (0.5693)

0.3456 (0.1194)

1tR 0.0446 (0.0781)*

-0.0109 (0.5992)

-0.0944 (0.0000)***

0.0001 (0.9959)

-0.0169 (0.4269)

Variance Equation

0 0.0398 (0.0000)***

0.0855 (0.0000)***

0.1148 (0.0000)***

0.0364 (0.0000)***

0.0447 (0.0000)***

1 0.2739 (0.0000)***

0.0680 (0.0000)***

0.0811 (0.0000)***

0.0515 (0.0000)***

0.0397 (0.0000)***

i -0.0157 (0.4211)

0.0367 (0.0007)***

-0.0012 (0.9025)

0.0432 (0.0000)***

0.0447 (0.0000)***

1 0.7302

(0.0000)*** 0.8804

(0.0000)*** 0.8853

(0.0000)*** 0.9191

(0.0000)*** 0.9166

(0.0000)***

PGE -0.0300 (0.0052)***

-0.0346 (0.1546)

0.3049 (0.0015)***

0.1584 (0.0305)**

0.8937 (0.0000)***

PtGE -0.0200 (0.1479)

-0.0613 (0.0000)***

-0.0753 (0.3091)

-0.0219 (0.4915)

-0.1585 (0.0002)***


5 lags 0.4049 0.4069 0.5937 0.1550 0.7116 10 lags 0.5864 0.5941 0.6331 0.4455 0.8043



F-stat 0.4949 0.7657 0.9518 0.6689 0.2133 Chi-Square 0.4948 0.7657 0.9518 0.6688 0.2131



151




Consumer Products

Consumer Products

Consumer Products


(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (2, 1) Mean Equation

0 0.0073 (0.7841)

0.0498 (0.0210)**

0.0395 (0.1309)

0.0216 (0.3683)

0.0199 (0.4574)

0.0814 (0.0069)***

PGE -0.1256 (0.7953)

0.0545 (0.6461)

-0.1281 (0.6035)

-0.1583 (0.5625)

0.3082 (0.4291)

-0.0745 (0.8096)

PtGE -0.1156 (0.4152)

0.3105 (0.0340)**

0.2055 (0.1887)

-0.1545 (0.2840)

0.0261 (0.9144)

-0.0709 (0.6584)

1tR -0.0994 (0.0000)***

-0.1339 (0.0000)***

-0.0151 (0.4904)

-0.1348 (0.0000)***

-0.0114 (0.5676)

-0.0307 (0.2627)

Variance Equation

0 0.1299 (0.0000)***

0.6739 (0.0000)***

0.1278 (0.0000)***

0.1570 (0.0000)***

0.0580 (0.0000)***

0.0197 (0.0000)***

1 0.0873 (0.0000)***

0.0919 (0.0000)***

0.1210 (0.0000)***

0.0826 (0.0000)***

0.0773 (0.0000)***

0.2312 (0.0000)***

2 -- --

-- --

-- --

-- --

-- --

-0.1951 (0.0000)***

i 0.0163 (0.1845)

-0.0328 (0.2226)

0.0070 (0.6560)

0.0206 (0.1988)

0.0466 (0.0000)***

-0.0056 (0.1185)

1 0.8466 (0.0000)***

0.1492 (0.0792)*

0.8213 (0.0000)***

0.8089 (0.0000)***

0.8832 (0.0000)***

0.9622 (0.0000)***

PGE 0.5143 (0.0000)***

-0.2748 (0.0000)***

0.0114 (0.8617)

0.1220 (0.1499)

0.3539 (0.0883)*

0.0531 (0.0186)**

PtGE -0.1085 (0.0043)***

0.4146 (0.0003)***

-0.0529 (0.3173)

-0.0345 (0.2672)

-0.0464 (0.6217)

-0.0414 (0.0000)***


5 lags 0.3428 0.9993 0.9980 0.6505 0.3333 0.9989 10 lags 0.4585 1.0000 0.7519 0.8337 0.3233 1.0000



F-stat 0.6958 0.0978 0.3918 0.4783 0.7281 0.8888 Chi-Square 0.6958 0.0976 0.3917 0.4782 0.7280 0.8888



152




Mean Equation

0 2.9114 (0.0000)***

3.2686 (0.0000)***

4.8322 (0.0000)***

6.1296 (0.0000)***

3.1741 (0.0000)***

PGE -0.0310 (0.7729)

0.0511 (0.7613)

0.0412 (0.6994)

-0.1373 (0.1315)

-0.1533 (0.3316)

PtGE 0.0202 (0.8111)

-0.0270 (0.8007)

0.0899 (0.2676)

0.0089 (0.9012)

0.0743 (0.4580)

1tVol 0.7718 (0.0000)***

0.6793 (0.0000)***

0.6860 (0.0000)***

0.5933 (0.0000)***

0.7038 (0.0000)***

Variance Equation

0 0.0328 (0.0000)***

0.0315 (0.0001)***

0.0346 (0.0000)***

0.1112 (0.0000)***

0.0748 (0.0000)***

1 0.0862 (0.0000)***

0.0115 (0.0365)**

-0.0238 (0.0024)***

0.0421 (0.0195)**

0.0354 (0.0000)***

2 -0.0876

(0.0000)*** -- --

-- --

-- --

-- --

i 0.0745 (0.0000)***

0.0270 (0.0009)***

0.0776 (0.0000)***

0.0753 (0.0011)***

0.0505 (0.0001)***

1 0.9135 (0.0000)***

0.9497 (0.0000)***

0.9220 (0.0000)***

0.6180 (0.0000)***

0.8719 (0.0000)***


5 lags 0.6724 0.0052 0.0330 0.4982 0.0000 10 lags 0.8910 0.0540 0.0893 0.3662 0.0000


10 lags 0.8930 0.0510 0.0840 0.3560 0.0000 Note: ***, ** and * denote significance at 1, 5 and 10% levels respectively. Numbers in parentheses depict p-value.

153

Table 4.14(b): Threshold GARCH Results for Trading Volume during Pre- and Post-

General Election (1994 - 2005) – Government Link Companies (GLCs)



Trade & Services

Trade & Services

Consumer Products

Industrial Products

Property

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 3.9284 (0.0000)***

5.4750 (0.0000)***

4.8288 (0.0000)***

6.1811 (0.0000)***

2.8946 (0.0000)***

3.0601 (0.0000)***

PGE 0.2900 (0.0348)**

0.0789 (0.5084)

-0.0636 (0.5591)

-0.1464 (0.3665)

-0.1518 (0.4487)

0.0539 (0.6230)

PtGE 0.3339 (0.0021)***

0.1667 (0.0248)**

-0.0275 (0.7688)

0.0331 (0.7845)

0.2124 (0.0928)*

-0.0765 (0.1902)

1tVol 0.6540 (0.0000)***

0.6132 (0.0000)***

0.6701 (0.0000)***

0.5387 (0.0000)***

0.7304 (0.0000)***

0.7854 (0.0000)***

Variance Equation

0 0.0278 (0.0000)***

0.1197 (0.0001)***

0.0044 (0.0008)***

0.0845 (0.0000)***

0.8075 (0.0000)***

0.0234 (0.0000)***

1 0.0123 (0.0543)*

0.0333 (0.0154)**

0.0162 (0.0000)***

-0.0134 (0.1974)

0.0876 (0.0005)***

0.0163 (0.0331)**

i 0.0520 (0.0000)***

0.0789 (0.0026)***

0.0096 (0.0129)**

0.1067 (0.0000)***

0.1208 (0.0066)***

0.0543 (0.0000)***

1 0.9357 (0.0000)***

0.6528 (0.0000)***

0.9695 (0.0000)***

0.8869 (0.0000)***

0.2279 (0.0116)**

0.8993 (0.0000)***


5 lags 0.0761 0.3555 0.0006 0.1133 0.7451 0.6811 10 lags 0.1978 0.7890 0.0045 0.4198 0.8350 0.6927


10 lags 0.1970 0.7720 0.0040 0.3820 0.8190 0.6960 Note: ***, ** and * denote significance at 1, 5 and 10% levels respectively. Numbers in parentheses depict p-value.

154

Table 4.15(a): Threshold GARCH Results for Trading Volume during Pre- and Post-

General Election (1994 - 2005) – Non-Government Link Companies (Non-GLCs)



Trade and Services

Trade and Services

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 3.6402 (0.0000)***

3.7746 (0.0000)***

6.2348 (0.0000)***

6.8774 (0.0000)***

4.4942 (0.0000)***

PGE -0.0676 (0.6098)

0.0925 (0.4105)

0.0287 (0.8200)

-0.0794 (0.4593)

0.1876 (0.4132)

PtGE 0.0059 (0.9285)

0.0307 (0.6421)

0.1167 (0.0969)*

0.0878 (0.2466)

0.2721 (0.0405)**

1tVol 0.7391 (0.0000)***

0.7318 (0.0000)***

0.5970 (0.0000)***

0.5384 (0.0000)***

0.6292 (0.0000)***

Variance Equation

0 0.0191 (0.0012)***

0.0640 (0.0000)***

0.1229 (0.0000)***

0.1441 (0.0032)***

0.0361 (0.0000)***

1 0.0029 (0.6698)

0.0009 (0.9331)

0.0103 (0.1311)

0.0274 (0.1197)

0.0251 (0.0000)***

i 0.0355 (0.0010)***

0.1054 (0.0000)***

0.0909 (0.0000)***

0.0353 (0.1466)

0.0317 (0.0001)***

1 0.9315 (0.0000)***

0.7989 (0.0000)***

0.6651 (0.0000)***

0.6374 (0.0000)***

0.9358 (0.0000)***


5 lags 0.0129 0.3903 0.0906 0.2709 0.0325 10 lags 0.1007 0.5424 0.0099 0.3554 0.0518


10 lags 0.1150 0.5450 0.0180 0.3290 0.0610 Note: ***, ** and * denote significance at 1, 5 and 10% levels respectively. Numbers in parentheses depict p-value.

155





Consumer Products

Consumer Products

Consumer Products


(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 6.3771 (0.0000)***

5.9684 (0.0000)***

5.7836 (0.0000)***

6.4712 (0.0000)***

6.4558 (0.0000)***

5.8326 (0.0000)***

PGE -0.1114 (0.3895)

-0.2762 (0.0913)*

0.0686 (0.6870)

-0.1096 (0.5239)

-0.0762 (0.5906)

-0.2019 (0.1406)

PtGE 0.1029 (0.1670)

-0.0564 (0.6778)

0.2829 (0.0161)**

-0.0873 (0.4935)

0.1034 (0.0989)*

0.0715 (0.4886)

1tVol 0.5814 (0.0000)***

0.4451 (0.0000)***

0.5356 (0.0000)***

0.4552 (0.0000)***

0.5857 (0.0000)***

0.5542 (0.0000)***

Variance Equation

0 0.0256 (0.0000)***

-0.0015 (0.1115)

0.0573 (0.0000)***

0.0839 (0.0000)***

0.0131 (0.0000)***

0.0197 (0.0000)***

1 0.0047 (0.4721)

0.0078 (0.0000)***

0.0055 (0.4904)

-0.0209 (0.0074)***

0.0072 (0.1900)

0.0092 (0.0740)*

i 0.0603 (0.0000)***

-0.0022 (0.1582)

0.0559 (0.0000)***

0.0786 (0.0000)***

0.0285 (0.0000)***

0.0404 (0.0000)***

1 0.9177 (0.0000)***

0.9948 (0.0000)***

0.9105 (0.0000)***

0.8816 (0.0000)***

0.9522 (0.0000)***

0.9478 (0.0000)***


5 lags 0.4638 0.0006 0.4580 0.6550 0.2650 0.0097 10 lags 0.0300 0.0000 0.5065 0.7386 0.4904 0.0175



156



Variables AHB BIMB CIMB MAY MBS Sector Finance Finance Finance Finance Finance (p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1)

Mean Equation

0 3.1199 (0.0000)***

2.6901 (0.0000)***

6.7374 (0.0000)***

6.2437 (0.0000)***

2.8216 (0.0000)***

PGE -0.0195 (0.8790)

-0.0035 (0.9796)

0.1784 (0.0123)**

0.2192 (0.0066)***

-0.0399 (0.7007)

PtGE -0.0269 (0.7576)

-0.0135 (0.9020)

0.1235 (0.0384)**

0.1973 (0.0004)***

0.1019 (0.2588)

1tVol 0.7647 (0.0000)***

0.7870 (0.0000)***

0.5807 (0.0000)***

0.6034 (0.0000)***

0.7963 (0.0000)***

Variance Equation

0 0.0352 (0.0002)***

0.0444 (0.0000)***

0.0565 (0.0000)***

0.0194 (0.0000)***

0.0236 (0.0000)***

1 0.0259 (0.0068)***

0.0029 (0.6957)

0.0285 (0.1137)

-0.0087 (0.3723)

-0.0147 (0.0007)***

i 0.0543 (0.0021)***

0.0927 (0.0000)***

0.0866 (0.0009)***

0.0621 (0.0000)***

0.0492 (0.0000)***

1 0.8901

(0.0000)*** 0.9152

(0.0000)*** 0.6945

(0.0000)*** 0.9069

(0.0000)*** 0.9575

(0.0000)*** (Diagnostic Checking)

ARCH – LM Statistic (p-value) 5 lags 0.1688 0.0000 0.4437 0.8418 0.0022

10 lags 0.3466 0.0001 0.3850 0.9930 0.0033 Ljung-Box Q2 Statistic (p-value)

5 lags 0.1780 0.0000 0.4430 0.8400 0.0030 10 lags 0.3550 0.0000 0.3620 0.9920 0.0030

Note: ***, ** and * denote significance at 1, 5 and 10% levels respectively. Numbers in parentheses depict p-value.

157





Trade & Services

Trade & Services

Consumer Products

Industrial Products

Property

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 4.5936 (0.0000)***

5.8369 (0.0000)***

6.1201 (0.0000)***

6.9978 (0.0000)***

2.8813 (0.0000)***

3.0884 (0.0000)***

PGE 0.0429 (0.7397)

0.0210 (0.7701)

0.1780 (0.0082)***

0.2291 (0.0354)**

0.3025 (0.1615)

0.0675 (0.5304)

PtGE 0.0141 (0.8872)

0.0129 (0.8112)

0.0602 (0.3605)

0.1751 (0.0508)*

0.5449 (0.0016)***

0.1299 (0.1024)

1tVol 0.6442 (0.0000)***

0.6252 (0.0000)***

0.6107 (0.0000)***

0.5002 (0.0000)***

0.7227 (0.0000)***

0.7986 (0.0000)***

Variance Equation

0 0.0309 (0.0000)***

0.0163 (0.0000)***

0.0196 (0.0000)***

0.0263 (0.0000)***

0.0935 (0.0000)***

0.0302 (0.0001)***

1 0.0244 (0.0001)***

0.0233 (0.0102)**

-0.0130 (0.0651)*

0.0424 (0.0000)***

0.0574 (0.0000)***

-0.0170 (0.0136)**

i 0.0432 (0.0000)***

0.0786 (0.0000)***

0.0932 (0.0000)***

0.0335 (0.0253)**

0.0303 (0.0388)**

0.0690 (0.0000)***

1 0.9127 (0.0000)***

0.8819 (0.0000)***

0.8934 (0.0000)***

0.8943 (0.0000)***

0.8708 (0.0000)***

0.9134 (0.0000)***


5 lags 0.0033 0.5892 0.1901 0.2809 0.0027 0.0031 10 lags 0.0061 0.8449 0.1585 0.1177 0.0104 0.0119



158





Trade and Services

Trade and Services

(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 5.8027 (0.0000)***

6.2867 (0.0000)***

5.1987 (0.0000)***

5.5374 (0.0000)***

4.5732 (0.0000)***

PGE 0.0557 (0.6303)

0.1175 (0.4428)

0.0202 (0.8561)

0.0944 (0.2518)

0.3235 (0.0041)***

PtGE 0.0147 (0.8368)

0.0257 (0.7700)

0.0192 (0.8017)

-0.1163 (0.1090)

0.1562 (0.2001)

1tVol 0.6061 (0.0000)***

0.5472 (0.0000)***

0.6626 (0.0000)***

0.6330 (0.0000)***

0.6711 (0.0000)***

Variance Equation

0 0.1109 (0.0003)***

0.3328 (0.0000)***

0.3341 (0.0000)***

0.1600 (0.0000)***

0.0193 (0.0000)***

1 0.0037 (0.7888)

0.1938 (0.0000)***

0.0815 (0.0014)***

0.0115 (0.5086)

0.0025 (0.6486)

i 0.1129 (0.0007)***

-0.1168 (0.0002)***

0.0302 (0.4283)

0.2145 (0.0000)***

0.0587 (0.0000)***

1 0.5985

(0.0000)*** 0.3595

(0.0000)*** 0.0578

(0.6368) 0.4452

(0.0000)*** 0.9413

(0.0000)*** (Diagnostic Checking)

ARCH – LM Statistic (p-value) 5 lags 0.5251 0.9660 0.9884 0.9229 0.2841

10 lags 0.7912 0.9983 0.9956 0.9602 0.5958 Ljung-Box Q2 Statistic (p-value)

5 lags 0.5430 0.9660 0.9880 0.9200 0.2970 10 lags 0.7850 0.9980 0.9950 0.9570 0.5910

Note: ***, ** and * denote significance at 1, 5 and 10% levels respectively. Numbers in parentheses depict p-value.

159





Consumer Products

Consumer Products

Consumer Products


(p, q) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) (1, 1) Mean Equation

0 6.6284 (0.0000)***

5.6604 (0.0000)***

5.9638 (0.0000)***

7.1552 (0.0000)***

5.2645 (0.0000)***

6.3827 (0.0000)***

PGE 0.1188 (0.2160)

0.6086 (0.0224)**

0.0359 (0.7393)

-0.1692 (0.0831)*

0.0685 (0.4666)

0.0043 (0.9731)

PtGE 0.0389 (0.5925)

0.3465 (0.0311)**

0.0986 (0.3116)

0.1728 (0.0892)*

0.1738 (0.0128)**

0.0971 (0.3070)

1tVol 0.5740 (0.0000)***

0.3709 (0.0000)***

0.5435 (0.0000)***

0.3913 (0.0000)***

0.6636 (0.0000)***

0.5335 (0.0000)***

Variance Equation

0 0.0022 (0.0098)***

0.1716 (0.0000)***

0.2169 (0.0000)***

0.0020 (0.0085)***

0.0117 (0.0000)***

0.2147 (0.0000)***

1 0.0181 (0.0000)***

0.1075 (0.0000)***

0.0436 (0.0771)*

0.0187 (0.0000)***

0.0184 (0.0033)***

0.0177 (0.3757)

i 0.0109 (0.0218)**

-0.0408 (0.0303)**

0.0769 (0.0342)**

0.0049 (0.0845)*

0.0161 (0.0123)**

0.1072 (0.0002)***

1 0.9714 (0.0000)***

0.8466 (0.0000)***

0.5236 (0.0000)***

0.9757 (0.0000)***

0.9341 (0.0000)***

0.5122 (0.0000)***


5 lags 0.1922 0.1230 0.6653 0.2048 0.0004 0.4774 10 lags 0.3505 0.2328 0.6199 0.4296 0.0060 0.8200



160


Pre-General Election Post-General Election

GLC

s - V

olat

ility

Non

-GLC

s - V

olat

ility

Trading Volume Trading Volume

161

Figure 4.2: Relationship between Trading Volume and Stock Price Volatility for GLCs

and Non-GLCs from 2006 – 2015

Pre-General Election Post-General Election

GLC

s - V

olat

ility

Non

-GLC

s - V

olat

ility

Trading Volume Trading Volume

162

Figure 4.3: Trading Volume and Stock Prices for the Highly Traded GLCs before the General Elections

Pre-General Election 2008 Pre-General Election 2013

CIM

B

MA

Y

TNB

UM

WH

163

Figure 4.4: Trading Volume and Stock Prices for the Highly Traded Non-GLCs before the General Elections

Pre-General Election 2008 Pre-General Election 2013

MIS

C

164

CHAPTER 5

GENERAL CONCLUSION

This study thoroughly examines the effect of the Malaysian general elections on its stock

market volatility from the year 1994 to 2015. Overview of Malaysian general elections and

the impact of political events on the stock market are presented in Chapter 1. Next, in Chapter

2, the analysis on election effect is firstly conducted on the benchmark composite indices,

including the Shariah-compliant indices, which represent the large, medium and small market

capitalization in the stock market. Then, in Chapter 3, the analysis is extended to the industry

by investigating the ten sectoral indices in the stock market. Lastly, at the firm level, eleven

Government-Link-Companies (GLCs) and eleven Non-Government-Link-Companies (Non-

GLCs) are selected as the sample of analysis in Chapter 4. By performing the analysis level

by level, this study is able to provide a complete understanding of election effects on the


In Chapter 2, evidence of election effect is found in the sample period of 2007 to

2015, which covers the 12th and 13th Malaysian general elections. These are the most two

recent general elections with a turbulent political condition. Generally, the result is consistent

with Wang and Lin (2009), Smales (2016), and Lean and Yeap (2017), who found higher

volatility in the pre-election periods. Specifically, this chapter shows the relevance of market

capitalization to stock market volatility when there is political uncertainty surrounding

elections. Companies with small capital experienced higher stock volatility prior to general

election. Conversely, the stock volatility is lower for larger companies' stock. Furthermore,

lower stock volatility is observed in Shariah-compliant stock indices which suggest that

Shariah-compliant companies have a lower risk during the pre-general election periods. The

165

finding from Chapter 2 implies that risk-averse investors could mitigate the political risk by

diversifying their portfolio in large companies’ stock and Shariah-compliant companies

stock. On the other hand, investors should be vigilant during the pre-general election periods

as their profits are underlying high volatility and compensation for abnormal high returns is

negligible.

The objective of Chapter 3 is to identify the influence of general elections on the

movement of the ten selected sectoral indices in Malaysian stock market for the period of

1994 to 2015. Beside the full sample period, the five general elections period is divided into

two stages. The first sub-sample covers the 9th, 10th and 11th Malaysia general election where

the ruling party continued to win 2/3 majority seats in all the three elections. The second sub-

sample period represents drastic shock periods during the 12th and 13th Malaysia general

election, from the year 2006 to 2015. Interestingly, the finding of the first sub-sample period

is obviously different from the second sub-sample period. While volatility on the stock return

is lower during the pre-general election periods of 1994-2005, it did show its negative and

significant influence in the 2008 and 2013 general election years. The result is quite in accord

with the political condition. For the first sub-sample period of 1994 to 2005, lower volatility

in the market is a good indicator showing that there is no uncertainty before the general

elections. Nevertheless, during the pre-general election period in the second sub-sample of

2006 to 2015, higher volatility in the market was induced by the uncertainties associated with

the general elections. Hence, the break-down of the full sample period into two sub-sample is

able to illustrate the impact of general elections more precisely. The result sheds light on the

importance of addressing the difference of political condition when testing for asymmetry

effect during election periods. Besides, the finding also indicates that the sectors of

Construction, Finance, Mining, and Property are more sensitive to the market condition with

166

significant result found in stock volatility. This is in line with Tuyon and Ahmad (2016)

where they also classified these few sectors as the cyclical sector. While Consumer Product is

a defensive sector where the estimated results are mostly insignificant.

Provided the evidence of election effect on the Malaysian main stock indices in

Chapter 2 and sectoral indices in Chapter 3, Chapter 4 further explores the reaction of stock

returns and volatility in the firm level. Eleven GLCs and eleven Non-GLCs are selected as

the sample and the sample period covers from January 4, 1994, through December 31, 2015.

Similar to Chapter 3, the full sample period is divided into two sub-samples. The finding in

this chapter also shows that the pattern of the stock volatility in GLCs and Non-GLCs is

clearly different in the two sub-samples, and thus, lends support to the observation in Chapter

3. As well, lower volatility of returns is found before the general elections in years 1994-

2005, for both the GLCs and Non-GLCs stock indices. In the general election years of 2006-

2015, the finding shows that most of the GLCs and Non-GLCs stock prices were highly

volatile before the general elections. Interestingly, further analysis on trading volume shows

that those GLCs and Non-GLCs with higher market capitalization that encountered higher

volatility are also associated with a significant higher trading volume. This pattern of trading

was probably due to the willingness of liquidity traders to trade in the periods where the

prices are more volatile (Admati and Pfleiderer, 1988). Another interesting point found in the

finance sector is that investors are still willing to actively trade the GLCs stock despite

market uncertainties. This indicates that investors are very careful during the time of market

uncertainties as it is always safer to trade GLCs stock which is more liquid than others.

Nonetheless, this trading pattern appears only in the finance sector, but not in other sectors.

167


is associated with investors' behaviour during the period of the general elections. The

presence of a political shock in the 12th and 13th Malaysia general election changed the

trading pattern in the market that reflects how investors react to the market uncertainty.

Therefore, this study is of great importance to risk managers, portfolio managers,

policymakers, and market participants to understand the pattern of volatility in the Malaysian

stock market during general election years. Thus, the results of this study perhaps provide an

insight for investors in adjusting their portfolio around the next general election. Future work

in this area can proceed in several directions. First, microdata on investors' personal

investment choices can be used to study their influence on stock market performance during

general election. Second, future study can be conducted to compare the market performance

of different stocks characteristics to evaluate the volatility during general election. This study

provides an exemplar for further studies to explore further details by employing a

comprehensive disaggregated data for different sectors and firm characteristics and sectors.

168

References

Admati, A. and Pfleiderer, P. (1988). “A theory of intraday patterns: Volume and price variability”, Review of Financial Studies, Vol. 1, pp. 3 – 40.

Lean, H. H. and Yeap, G. P. (2017). “Asymmetric Effect of Political Elections on Stock Returns and Volatility in Malaysia”, in Munir, Q. and Kok, S. C. (Ed.), Information Efficiency and Anomalies in Asian Equity Markets, Routledge, Taylor and Francis Group, pp. 228 - 245.

Smales, L. A. (2016). “The role of political uncertainty in Australian financial markets”, Accounting and Finance, Vol. 56, No. 2, pp. 545–575.

Tuyon, J. and Ahamd, Z., (2016). “Behavioural finance perspectives on Malaysian stock market efficiency”, Borsa Istanbul Review "Vol.16, No.1, pp. 43 - 61. Wang, Y. H. and Lin, C. T. (2009). “The Political Uncertainty and Stock Market Behavior in Emerging Democracy: The Case of Taiwan”, Quality and Quantity, Vol. 43, No. 2, pp. 237 - 248.

v wrfn dunhw ricky chia chee jiun academic advisor

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