assessing the determinants of foreign portfolio...

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"Assessing the Determinants of Foreign

Portfolio Investment to Emerging Markets:

The case of South Africa (1994-2010)"

A Research Report

Presented to

The Graduate School of Business

University of Cape Town

In partial fulfilment of the requirements for the

Masters of Business Administration Degree

Kirsten Wortmann

Full Time MBA 2010

Supervisor: Prof. Barry Standish

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DETERMINANTS OF FPI TO EMERGING MARKETS: SOUTH AFRICA (1994-2010) 1

ACKNOWLEDGEMENTS

This report is not confidential and may be freely used by the University Of Cape Town

Graduate School Of Business.

I wish to thank my supervisor, Prof Barry Standish for his valuable advice and guidance

during the writing of this report. Next, I would like to thank my MBA classmates Bruno

Mognayie, Piyush Bharti and Leor Hurwitz for their assistance with the EViews software and

data suggestions. I would also like to thank Kate Hunter from the GSB library for her useful

recommendations during my literature research and Sean Gossel for his assistance with

EViews and VAR analysis.

I certify that this report is my own work and that all references are accurately reported.

Signed:

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PLAGIARISM DECLARATION

1) I know that plagiarism is wrong. Plagiarism is to use another’s work and pretend that it is

one’s own.

2) I have used the APA convention for citation and referencing. Each contribution to, and

quotation in, this MBA research project from the work(s) of other people has been

attributed, and has been cited and referenced.

3) This MBA research project is my own work.

4) I have not allowed, and will not allow, anyone to copy my work with the intention of

passing it off as his or her own work.

5) I acknowledge that copying someone else’s assignment or essay, or part of it, is wrong,

and declare that this is my own work.

Signature _________________________________

Kirsten Wortmann

10 December 2010

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ABSTRACT

This report examines the determinants of foreign portfolio investment to South Africa

between 1994 and 2010 using the Push and Pull framework prevalent in the capital flow

literature. The paper presents an empirical analysis of the determinants of net portfolio flows

relative to real GDP and portfolio inflows relative to GDP. The study determines whether

external push factors or internal pull factors are the dominant drivers of foreign portfolio

investment to South Africa.

Stationarity and cointegration tests were conducted on all the variables. The results from the

Johansen cointegration test did not confirm the existence of a long run stable equilibrium

relationship among the variables studied. An unrestricted vector auto regression (VAR)

model found the significant determinants of FPI flows to be domestic pull factors.

The results of the analysis indicated that domestic pull factors namely the South African

domestic T-bill rate, real GDP, trade openness and the Standard and Poor sovereign credit

rating are the dominant factors affecting FPI flows to South Africa during the period under

observation. The only significant push factors found were the US Industrial Production

Index, which is a proxy for growth in developed countries.

Keywords: capital flows, South Africa, foreign portfolio investment, VAR analysis,

cointegration, push and pull factors

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ABBREVIATIONS

ADF: Augmented Dicky-Fuller unit root test

CA: Current Account

CPI: Consumer Price Index used as a measure of inflation

BOP: Balance of Payments

ECM: Error Correction Model

EMBI: Emerging Markets Bond Index produced by JP Morgan.

FA: Financial Account

FDI: Foreign Direct Investment

FPI: Foreign Portfolio Investment

GDP: Gross Domestic Product

IFC: International Finance Corporation

IFS: International Financial Statistics (IMF online database)

IMF: International Monetary Fund

INET: I-Net Bridge online database

IPI: Industrial Production Index

JSE: Johannesburg Stock Exchange

MDGs: Millennium Development Goals

NEPAD: New Partnership for Africa’s Development

OECD: Organisation for Economic Co-operation and Development

OLS: Ordinary Least Squares

PP: Phillips-Perron unit root test

SA: South Africa

SADC: Southern Africa Development Community

SARB: South African Reserve Bank

SSA: Sub Saharan Africa

SSR: Sum of squared residuals

SVAR: Structured Vector Autoregression Model

TIPS: Trade and Industry Policy Strategies

UNCTAD: United Nations Conference on Trade and Development

VAR: Vector Autoregression Model

WEF: World Economic Forum

WEO: World Economic Outlook

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS ....................................................................................................... 1

PLAGIARISM DECLARATION .............................................................................................. 2

ABSTRACT ............................................................................................................................... 3

ABBREVIATIONS ................................................................................................................... 4

1. INTRODUCTION .............................................................................................................. 9

1.1. Background ................................................................................................................. 9

1.2. Research Area and Problem ...................................................................................... 11

1.3. Research Questions and Scope .................................................................................. 12

1.4. Research Assumptions .............................................................................................. 13

1.5. Research Ethics ......................................................................................................... 13

2. LITERATURE REVIEW ................................................................................................. 14

2.1 The South African Context ....................................................................................... 14

2.2. Balance of Payments ................................................................................................. 17

2.3. Domestic Savings ...................................................................................................... 19

2.4. Capital Flows............................................................................................................. 20

2.5. FDI ............................................................................................................................ 22

2.6. FPI ............................................................................................................................. 23

2.7. Volatility.................................................................................................................... 28

2.8. Push and Pull factors ................................................................................................. 28

2.9. Risk............................................................................................................................ 32

2.10. Interest Rates ......................................................................................................... 35

2.11. Methodology Comparison ..................................................................................... 36

2.12. Conclusion ............................................................................................................. 40

3. RESEARCH METHODOLOGY ..................................................................................... 41

3.1. Research Approach and Strategy .............................................................................. 41

3.2. Data Collection .......................................................................................................... 42

3.3. Description of Variables............................................................................................ 42

3.3.1. Net Foreign Portfolio Investment (NET_FPI) ................................................... 43

3.3.2. Foreign Portfolio Investment inflows (FPI_INFLOWS) ................................... 43

3.3.3. Real GDP (LOG_REAL_GDP) ......................................................................... 44

3.3.4. Current Account Balance (CAB) ....................................................................... 45

3.3.5. Inflation (LOG_INFLATION)........................................................................... 45

3.3.6. Budget Deficit/Surplus relative to GDP (BUDGET) ......................................... 46

3.3.7. Trade Openness (OPENNESS) .......................................................................... 47

3.3.8. South Africa’s Treasury Bill Rate (LOG_SA_TBILL) ..................................... 48

3.3.9. Country Risk (COUNTRY_RISK) .................................................................... 49

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3.3.10. Exchange Rates .............................................................................................. 49

3.3.11. Real GDP per capita (LOG_GDP_PER_CAPITA) ....................................... 50

3.3.12. Sovereign Credit Ratings ............................................................................... 51

3.3.13. Interest Rate Differentials .............................................................................. 52

3.3.14. US Fed Fund rate (LOG_US_FED_RATE)................................................... 53

3.3.15. US Industrial Production Index (LOG_US_IPI) ............................................ 54

3.4. Research Instruments ................................................................................................ 55

3.5. Sampling.................................................................................................................... 55

3.6. Research Criteria ....................................................................................................... 55

3.7. Data Analysis ............................................................................................................ 56

4. RESEARCH RESULTS ................................................................................................... 61

4.1. Research Findings ..................................................................................................... 61

4.2. Research Analysis ..................................................................................................... 63

4.2.1. Descriptive Statistics .......................................................................................... 63

4.2.2. Stationarity and Unit Root Tests ........................................................................ 63

4.2.3. Johansen Cointegration Test .............................................................................. 65

4.2.4. Chow test for structural changes ........................................................................ 67

4.2.5. VAR analysis ..................................................................................................... 69

4.2.6. Pairwise Granger Causality Results ................................................................... 72

4.3. Discussion ................................................................................................................. 73

4.4. Limitations ................................................................................................................ 76

5. CONCLUSION ................................................................................................................ 77

6. FUTURE RESEARCH ..................................................................................................... 81

7. REFERENCES ................................................................................................................. 82

7.1. Journals...................................................................................................................... 82

7.2. Books ......................................................................................................................... 89

7.3. Websites .................................................................................................................... 90

8. APPENDICES .................................................................................................................. 91

APPENDIX A: Methodology Comparison ......................................................................... 91

APPENDIX B: Research variables ..................................................................................... 96

APPENDIX C: Sovereign Credit Rating index conversion ................................................ 97

APPENDIX D: Descriptive statistics ................................................................................ 100

APPENDIX E: Augmented Dicky-Fuller Unit Root Test Results .................................... 101

APPENDIX F: Phillips-Perron Unit Root Test Results .................................................... 103

APPENDIX G: Comparison of ADF and PP unit root tests .............................................. 106

APPENDIX H: Cointegration test results for FPI_INFLOWS ......................................... 106

APPENDIX I: Cointegration test results for NET_FPI .................................................... 107

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APPENDIX J: Chow Forecast breakpoint tests ................................................................. 108

APPENDIX K: Lag lengths ............................................................................................... 110

APPENDIX L: AR Graphs ............................................................................................... 111

APPENDIX M: VAR analysis ........................................................................................... 112

APPENDIX N: Pairwise Granger Causality Tests ............................................................. 116

FIGURES

Figure 1: Sum of FDI, net inflows and Portfolio investment, equity, South Africa 1970-2008.

Source: World Bank ................................................................................................................. 15

Figure 2: Comparison between net FDI and net FPI, South Africa 1985-2009. Source: IFS 16

Figure 3: Net Portfolio flows divided into net equity and net debt flows, South Africa 1985-

2009 Source: IFS...................................................................................................................... 17

Figure 4: Components of the Balance of Payments account. Source: Amaya G and Rowland

(2004) ....................................................................................................................................... 18

Figure 5: Current Account and Financial Account, South Africa 1970-2008 Source:

B.Standish, UCT GSB MBA, Economics, class notes 2010 ................................................... 19

Figure 6: South Africa’s gross savings as a percentage of GDP (1960-2008) Source: IMF

website ..................................................................................................................................... 20

Figure 7: Net FDI flows, South Africa 1985-2009. Source: IFS ............................................. 22

Figure 8: South Africa’s Current Account balance in millions US $ (1960-2008). Source:

IMF website ............................................................................................................................. 24

Figure 9: FDI net flows to the BRIC countries and South Africa (1970-2008) Source: World

Bank ......................................................................................................................................... 25

Figure 10: FPI net flows to the BRIC countries and South Africa (1979-2008) Source: World

Bank ......................................................................................................................................... 25

Figure 11: Net portfolio flows South Africa (1994-2009) and the division between debt and

equity flows Source: IFS .......................................................................................................... 25

Figure 12: GDP (annual %) in South Africa (1980 to 2008). Source: IMF website ............. 31

Figure 13: JSE ALSI (1997-2010) Source: INET.................................................................... 31

Figure 14: Comparison of Treasury bill rates 1985-2009 ........................................................ 36

Figure 15: Net FPI relative to real GDP South Africa (1994:Q1-2010:Q2) ............................ 43

Figure 16: FPI inflows relative to real GDP South Africa (1994:Q1-2010:Q2) ...................... 44

Figure 17: Real GDP (millions US$) South Africa (1994:Q1-2010:Q2) ................................ 44

Figure 18: Log of Real GDP South Africa (1994:Q1-2010:Q2) ............................................ 44

Figure 19: First Difference of Log_Real_GDP ....................................................................... 45

Figure 20: Current Account Balance relative to real GDP South Africa (1994:Q1-2010:Q2) 45

Figure 21: First Difference of Current Account Balance relative to real GDP South Africa

(1994:Q1-2010:Q2).................................................................................................................. 45

Figure 22: Consumer Price Index South Africa (1994:Q1-2010:Q2) ...................................... 46

Figure 23: Log of Consumer Price Index South Africa (1994:Q1-2010:Q2) ......................... 46

Figure 24: First Difference of Log of Consumer Price Index South Africa (1994:Q1-

2010:Q2) .................................................................................................................................. 46

Figure 25: Budget Deficit/Surplus relative to GDP South Africa (1994:Q1-2010:Q2) .......... 47

Figure 26: First Difference of Budget Deficit/Surplus relative to GDP South Africa

(1994:Q1-2010:Q2).................................................................................................................. 47

Figure 27: Trade Openness measure South Africa (1994:Q1-2010:Q2) ................................. 47

Figure 28: Log of Trade Openness South Africa (1994:Q1-2010:Q2) .................................... 47

Figure 29: First Difference of Log of Trade Openness South Africa (1994:Q1-2010:Q2) ..... 48

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Figure 30: Treasury Bill Rate South Africa (1994:Q1-2010:Q2) ............................................ 48

Figure 31: Log of Treasury Bill Rate South Africa (1994:Q1-2010:Q2) ................................ 48

Figure 32: First Difference of Log of Treasury Bill Rate South Africa (1994:Q1-2010:Q2) . 48

Figure 33: EMBI Global stripped spread over US Treasury Quarterly change ....................... 49

Figure 34: Exchange Rates South Africa (1994:Q1-2010:Q2) ................................................ 50

Figure 35: Log of Exchange Rates South Africa (1994:Q1-2010:Q2) .................................... 50

Figure 36: Real GDP per Capita South Africa (1994:Q1 - 2010:Q2)...................................... 51

Figure 37: Log of Real GDP per Capita South Africa (1994:Q1 - 2010:Q2) .......................... 51

Figure 38: Sovereign Credit ratings converted to indices South Africa (1994:Q1 - 2010:Q2)52

Figure 39: Treasury bill interest rate differentials (1994:Q1 - 2010:Q2) ................................ 52

Figure 40: Log of Treasury bill interest rate differentials (1994:Q1 - 2010:Q2) ..................... 53

Figure 41: First Difference of Log of US/SA T-bill differentials (1994:Q1 - 2010:Q2) ......... 53

Figure 42: US Fed Fund Rate (1994:Q1 - 2010:Q2) ............................................................... 54

Figure 43: Log of US Fed Fund Rate (1994:Q1 - 2010:Q2).................................................... 54

Figure 44: First Difference of Log of US Fed Fund Rate (1994:Q1 - 2010:Q2) ..................... 54

Figure 45: US Industrial Production Index (1994:Q1 - 2010:Q2) ........................................... 54

Figure 46: Log of US Industrial Production Index (1994:Q1 - 2010:Q2) ............................... 54

Figure 47: First Difference of Log of US Industrial Production Index (1994:Q1 - 2010:Q2) 55

Figure 48: Results of AR Roots Graph for NET_FPI unrestricted VAR model ................... 111

Figure 49: Results of AR Roots Graph for FPI_INFLOWS unrestricted VAR model ......... 111

Figure 52: Granger Causality tests for NET_FPI .................................................................. 116

Figure 53: Granger Causality tests for FPI _INFLOWS ....................................................... 117

Figure 54: VAR Granger Causality/Block Exogeneity Walk tests ....................................... 118

TABLES

Table 1: Unit root tests for sub-samples of NET_FPI and FPI_INFLOWS ............................ 64

Table 2: Summary statistics from unrestricted VAR analysis ................................................ 72

Table 3: Methodology comparison .......................................................................................... 95

Table 4: Dependent and Independent variables under consideration ...................................... 96

Table 5: Sovereign Credit Ratings for South Africa Source:(Aron et al., 2010) ..................... 97

Table 6: Conversion of sovereign credit ratings into an index ................................................ 99

Table 7: Descriptive Statistics ............................................................................................... 100

Table 8: ADF unit root tests Level ........................................................................................ 101

Table 9: ADF unit root tests 1st Difference ........................................................................... 102

Table 10: PP unit root tests Level .......................................................................................... 103

Table 11: PP unit root tests 1st Difference ............................................................................ 104

Table 12: PP unit root tests 2nd Difference ........................................................................... 105

Table 13: Comparison of ADF and PP unit root tests ........................................................... 106

Table 14: Results of Johansen cointegration Trace test FPI_INFLOWS .............................. 107

Table 15: Results of cointegration Maximum Eigenvalue test for FPI_INFLOWS .............. 107

Table 16: Results of Johansen cointegration Trace test NET_FPI ........................................ 107

Table 17: Results of cointegration Maximum Eigenvalue test for NET_FPI ........................ 108

Table 18: Chow Forecast test NET_FPI 1994q1 - 2007q1 .................................................... 108

Table 19: Chow Forecast test NET_FPI 2002q1 - 2010q12 .................................................. 109

Table 20: Chow Forecast test FPI_INFLOWS 1994q1 - 2007q1 .......................................... 109

Table 21: Chow Forecast test FPI_INFLOWS 2002q1 - 2010q2 .......................................... 110

Table 22: Unrestricted VAR estimates for lags 0 to 4 ........................................................... 110

Table 23: Results of NET_FPI unrestricted VAR analysis ................................................... 113

Table 24: Results of FPI_INFLOWS unrestricted VAR analysis ......................................... 115

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1. INTRODUCTION

Foreign portfolio flows are extremely influential in today’s global financial markets. Many

developing countries rely on foreign portfolio investment to finance their current account

deficits and fund economic growth. In recent years, portfolio flows have caused substantial

appreciation in the currencies of many emerging markets. Yet there is a dark side to portfolio

flows - they are volatile, subject to abrupt reversals, and have been blamed for many of the

latest financial crises in emerging markets.

This study attempts to understand the causes of changes in South African foreign portfolio

investment flows. Market sentiment drives portfolio flows and not country fundamentals

according to some commentators. However, with economic information becoming readily

available due to globalization and the internet, data on country fundamentals has become

easily accessible to global investors. Over time, capital flows should become less sensitive to

news and rumours and more sensitive to economic fundamentals.

The study examined the role of various push and pull factors that could affect foreign

portfolio investment flows to South Africa between 1994 and 2010. The unrestricted VAR

model analyses the external push factors of the US and SA Treasury bill rate differential, the

US Fed Fund Rate and the US Industrial Production Index. The internal pull factors included

in the model were real GDP, domestic inflation, the SA Treasury bill rate, sovereign credit

ratings, country risk and trade openness.

1.1. Background

This section introduces capital flows - specifically foreign portfolio investment (FPI). A

discussion of how capital flows affect developing countries and especially South Africa

follows.

As global markets become established and more countries liberalise their capital markets, so

capital is able to move around the globe freely seeking the highest returns. There has been

much debate in recent years about the costs and benefits of foreign capital flows especially

concerning developing countries such as South Africa due to their vulnerability to financial

crises.

Some economic theory follows to understand the capital flow phenomenon. The open

economy investment formula is:

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1 (1)

In other words, the supply of savings and the current account (exports less imports) satisfies

the demand for investment. The current account of the Balance of Payments (BOP) account

reflects a deficit when domestic investment exceeds domestic savings. Inflows of foreign

savings or a country’s foreign reserves finance this current account deficit (Prinsloo, 2000).

Therefore, assuming constant foreign reserves, foreign capital flows could supplement

domestic savings to facilitate investment and economic growth in a country (Durham, 2000).

Since the 1980s, institutions such as the IMF and World Bank have advised developing

countries to liberalise their capital markets and reform their economies in order to attract

foreign savings to finance economic development. Many developing countries, including

South Africa, have followed specific economic policy recommendations, for example the

Washington Consensus, to open up their economies (Epstein, 2002).

In addition, NEPAD has identified capital flows as a potential means of achieving Africa’s

Millennium Development Goals (MDGs). NEPAD encourages African countries to increase

domestic savings as well as make their financial markets more attractive to foreign capital

inflows to finance the gap between investment and savings (Loots, 2005). NEPAD further

recommends that countries should address the perceptions of investors in developed countries

who regard Africa as high risk, around financial regulations and property rights in particular

(Namibian Economic Policy Research Unit, 2004).

The Southern African Development Community (SADC), of which South Africa is a

member, is attempting the daunting task of meeting the MDGs by 2015. In order to achieve

these goals, NEPAD estimated extra resources of between $30 billion and $100 billion were

required. Since domestic savings in the SADC region are low and declining and nowhere

near enough to meet the resource requirements of the MDG’s, foreign capital inflows provide

an attractive source of debt free funds to SADC countries (Mlambo, 2005).

In the past, South Africa has experienced large capital outflows between 1984 and 1994 due

to political instability. However, since the change of government in 1994, South Africa has

embarked on a series of macroeconomic reforms to liberalise its capital market and open its

economy to the rest of the world (Prinsloo, 2000). This has resulted in renewed interest from

foreign investors and foreign lenders of capital in South Africa’s economy since 1994.

1 Formula source: (Durham, 2000)

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The Literature Review describes capital flows in more detail. Briefly, private capital flows

consist of FDI, portfolio equity flows, portfolio debt flows (portfolio bond or bank lending),

and other investment. FDI is the least volatile component and the most desirable as a source

of funds from a country’s point of view whereas portfolio investment (FPI) is short term,

volatile and subject to sudden reversals (Macias & Massa, 2009). Sudden reversals of

portfolio investment have caused currency, debt and financial crises in emerging markets in

the past making portfolio investment more risky than direct investment for an economy

(Makhubela, 2004).

In summary, South Africa’s FPI flows are particularly important because South Africa’s

capital flows play a role in the capital flows of some of its neighbouring countries. In

addition, South Africa forwards its economic growth to other SADC countries through its

financial links with these countries (Arora & Vamvakidis, 2005).

1.2. Research Area and Problem

The research investigates the role that interest rates, country risk and other domestic and

external factors play in the movement of FPI to South Africa. The data sample used was

quarterly data from 1994:Q1 to 2010:Q2, sourced mainly from the IMF’s International

Financial Statistics database.

To illustrate the importance of FPI flows in South Africa, Mboweni (2005), in reference to

Ahmed, Arezki, and Funke (2005) and their study of the composition of capital flows to

South Africa, notes that SA attracted three times more FPI than any other emerging market,

as a percentage of GDP. This implied that the composition of capital flows to South Africa

appears to contradict that of other emerging markets. A key point emphasized by Mboweni,

was that SA seemed to attract FPI more consistently than other developing countries. This

research examined the factors that influence FPI investment to South Africa to help explain

this paradox.

The reviewed literature proposed that there were a number of push and pull factors that

affected FPI flows. Interest rate differentials and country risk are two such factors. The

analysis attempted to establish that they are the major factors that influence FPI flows to

South Africa. The Literature Review describes the Push and Pull framework in more detail.

According to the prevailing literature, country risk should have a negative effect on FPI

inflows. Interest rates should have a positive effect as larger interest rate differentials attract

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foreign investors. Conversely, according to some commentators, interest rates could have a

negative effect on FPI inflows since the higher the interest rate, the more risky investors

perceive the investment to be. This research investigated what occurs in the case of South

Africa, a developing country and the only emerging market in Sub-Saharan Africa (SSA).

The research investigated whether interest rate differentials and country risk were the major

factors influencing the movement of FPI to South Africa during the period 1994:Q1 to

2010:Q2. Other dependent variables considered were exchange rates, GDP growth,

GDP/capita, inflation, the current account deficit /surplus and sovereign credit ratings.

This study adds to the literature around foreign portfolio investment in South Africa. The

Literature Review describes the existing literature on FPI flows to South Africa in more

detail.

1.3. Research Questions and Scope

The research addressed the following questions:

1. Are external push factors the dominant factors to consider when assessing FPI flows

(net and inflows) to South Africa between 1994 and 2010?

2. Are internal pull factors the dominant factors to consider when assessing FPI flows

(net and inflows) to South Africa between 1994 and 2010?

Leedy and Ormrod (2010) defined a research hypothesis as a ―logical supposition, a

reasonable guess, an educated conjecture‖ (Leedy & Ormrod, 2010, p. 4). The reviewed

literature has directed the research hypotheses used in this study.

H0 below refers to the original research hypothesis whereas H1 is the alternative hypothesis,

which if true disproves the original hypothesis. The hypotheses addressed by the research are

as follows:

Test 1: Foreign interest rates and FPI flows

H0: Foreign interest rates have an effect on FPI flows to South Africa

H1: Foreign interest rates have no effect on FPI flows to South Africa

Test 2: Domestic interest rates and FPI flows

H0: Domestic interest rates has an effect on FPI flows to South Africa

H1: Domestic interest rates has no effect on FPI flows to South Africa

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Test 3: Country risk and FPI flows

H0: Country risk ratings have an effect on FPI flows to South Africa

H1: Country risk ratings have no effect on FPI flows to South Africa

Test 4: Domestic economic growth and FPI flows

H0: Domestic economic growth has an effect on FPI flows to South Africa

H1: Domestic economic growth has no effect on FPI flows to South Africa

Test 5: Sovereign credit ratings and FPI flows

H0: Sovereign credit ratings have an effect on FPI flows to South Africa

H1: Sovereign credit ratings have no effect on FPI flows to South Africa

Test 6: Trade openness and FPI flows

H0: Trade openness has an effect on FPI flows to South Africa

H1: Trade openness has no effect on FPI flows to South Africa

1.4. Research Assumptions

The research has been based on the assumption that the specific push and pull factors chosen

as independent variables; do in fact have an impact on FPI flows to South Africa. In other

words that a relationship exists between the chosen independent variables and the dependent

variable of net FPI or FPI inflows over time. It has also been assumed that the relationship

between the variables does not change over time i.e. there was a condition of long-run

stationarity which has been tested for in the data analysis.

In completing the econometric analysis, the researcher further assumed that the statistical

analysis methodology followed in the study was suitable as a technique to study the

determinants of foreign portfolio investment based on the literature reviewed.

1.5. Research Ethics

A signed Ethics clearance form was submitted with the research proposal for this study.

Since this research project did not involve interviewing human subjects, it was not necessary

to address confidentiality concerns. All sources of information were referenced avoiding

plagiarism. Ethical collection of data was also important and the researcher sourced all data

from publicly accessible online databases with the exception of the JP Morgan EMBIG Index

sourced from the JP Morgan online database through a colleague with access.

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2. LITERATURE REVIEW

The literature review is presented in twelve sections. The first introduces FPI in the South

African context, and the next two explain South Africa’s balance of payments account and

the role of domestic savings in South Africa. Four sections explaining capital flows follow

and then three sections explaining push and pull factors with interest rates and risk described

in more detail. The subsequent section describes and compares existing studies conducted on

the determinants of capital flows. The final section concludes the literature review and sets

the basis for the analysis.

2.1 The South African Context

This section describes FPI and its importance to South Africa followed by a brief outline of

South Africa’s capital flow history.

In the last 25 years, world financial markets have become more complex and technologically

advanced. Globalisation has enabled financial technology developed in sophisticated markets

to be transferred to emerging markets shortly afterwards. African emerging markets do not

have a shortage of complex financial instruments; instead, they lack depth and infrastructure

making them more risky to investors (Nellor, 2008). The attraction of emerging markets for

foreign investors was the high rates of return offered and the opportunity to diversify risk in

their investor portfolios.

Sovereign credit ratings for South Africa were launched towards the end of 1994 facilitating

South Africa’s re-entry into the global bond markets. In 1995, almost all the exchange

controls on foreign investors were lifted when the financial rand was discontinued and the

existing dual exchange rate was combined into one (Aron, Leape, & Thomas, 2010) aiding

the liberalisation of SA’s capital markets post-democracy.

Moreover, South Africa, the only emerging market in the SADC region, has a well-developed

financial market that includes subsidiaries of large foreign-owned banks (IMF, 2009). There

exists a wide range of financial products and services and the JSE has recently been ranked

1st out of 139 countries for its regulation of securities exchanges in the latest World

Economic Forum Competiveness Report 2010-2011 (www.weforum.org). South Africa is

also the only Sub-Saharan economy to be included in the Morgan Stanley Emerging Market

Index (McCarthy, 2009). These facts make South Africa an attractive destination for foreign

http://www.weforum.org/

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investment. In addition, Jenkins and Thomas (2002) proposed that many investors also see

South Africa, the largest market in SSA, as key to regional trade.

South Africa needs foreign capital flows to fund economic growth. In fact, the liberalisation

of South Africa’s capital markets since 1994 has been aimed at making South Africa more

attractive to foreign investors (De Gama, 2003; Ibi Ajayi & Al, 2006; Makhubela, 2004;

Wesso, 2001).

As described by Aron et al. (2010) , financial sanctions during apartheid and the 1985 debt

crisis meant that South Africa had large net outflows of capital, which forced the government

to maintain current account surpluses prior to democracy. With the newly elected democratic

government in 1994 and the resultant macroeconomic reforms, there were net capital inflows.

Between 2004 and 2007, aided by an increasing current account deficit and rapid domestic

growth, there were further capital inflows.

The South African financial system remained surprisingly resilient during the global financial

crisis of 2008 and 2009 whereas the developed world suffered massive losses and a severe

global recession resulting in economic instability in many countries. However, the global

recession had a knock-on effect on domestic growth and employment in South Africa when

foreign investors repatriated capital to their home countries as the global recession worsened

(Aron, Leape, & Thomas, 2010)

To illustrate, Figure 1 shows the net capital flows to South Africa between 1970 and 2008

while Figure 2 shows the division between FDI and FPI between 1985 and 2009. Both

figures use nominal values in millions of US dollars.

Figure 1: Sum of FDI, net inflows and Portfolio investment, equity, South Africa 1970-2008. Source: World Bank

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Figure 2 shows the composition of capital flows to South Africa between 1985 and 2009.

The data shows net inflows of FDI (new investment inflows less disinvestment) and portfolio

equity investment that includes net inflows from equity securities other than those recorded

as direct investment, and direct purchases of shares in local stock markets by foreign

investors.

Figure 2: Comparison between net FDI and net FPI, South Africa 1985-2009. Source: IFS

As illustrated by Figure 2, South Africa experienced strong portfolio inflows between 1997

and 2000 averaging over 5 percent of GDP. These inflows were partly attributable to capital

flight from the Asian countries, which experienced a financial crisis in 1998 (IMF, 2004).

The dotcom crisis in 2001 resulted in portfolio outflows followed by strong inflows again as

the global economy experienced a growth phase. Portfolio inflows dropped dramatically in

2006 through to 2009 as developed countries experienced a severe global recession.

Furthermore, the majority of portfolio flows to South Africa have been in the form of equity

investments. According to the IMF (2004), as a percent of GDP, South Africa has attracted

more equity flows than comparable countries. Despite weak stock market performances after

the dotcom bubble in 2001, equity flows to South Africa remained above levels in other

emerging markets.

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Figure 3: Net Portfolio flows divided into net equity and net debt flows, South Africa 1985-2009 Source: IFS

Figure 3 illustrates the division between South Africa’s foreign portfolio equity and debt

flows between 1985 and 2009.

In conclusion, Carlson and Hernandez (2002) proposed that with capital inflows becoming

more important to developing and emerging countries and their economies, understanding

whether it is the push factors (country external) or pull factors (country internal) that drive

capital flows is becoming the central question in capital flow literature.

2.2. Balance of Payments

This section describes how capital flows interact with the Balance of Payments (BOP)

account.

Firstly, the Balance of Payments account keeps track of the transactions of a country with

other countries and international institutions. The current account and financial account are

the two main accounts in the Balance of Payments account. Figure 4 shows the

categorisation of the accounts in the Balance of Payments account. The Current Account

records trade and services transactions, whereas the Financial Account records direct

investment, portfolio investment, financial derivatives, other investment, and reserve assets

(Amaya G & Rowland, 2004).

As described by Amaya G and Rowland (2004), direct investment includes the transactions

between projects and direct investors. Portfolio investment includes the transactions in

equity and debt securities. These securities are classified as bonds and notes, money market

instruments or financial derivatives. Other investments are defined as short and long-term

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trade credits, loans, currency and deposits, and other account receivables and payables. The

reserve assets are those assets available for use in meeting balance of payment needs and

include monetary gold, SDRs, the reserve position in the Fund, and foreign exchange assets

among other government assets (Amaya G & Rowland, 2004).

Figure 4: Components of the Balance of Payments account. Source: Amaya G and Rowland (2004)

In addition, the Balance of Payment accounts record the net capital inflows and outflows

during a period. Inflows record the net purchases or sales by non-residents of domestic

assets, whereas outflows measure the net purchases or sales of foreign assets by residents

(Binici, Hutchison, & Schindler, 2009; Lane & Milesi-Ferretti, 2007).

Consequently, inflows and outflows could have negative or positive values. In the financial

account, an increase (or decrease) in liabilities to non-residents is entered as positive (or

negative) amount. An increase (or decrease) in net foreign assets owned by residents is

entered as negative (or positive). If a resident sells foreign assets, the financial account

records the transaction as a reduction in net foreign assets held by residents, resulting in an

inflow of capital (Deléchat et al., 2008).

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The financial account balance shown in Figure 5 includes the balance of net inflows of non-

resident capital (flows associated with SA’s foreign liabilities) and net outflows of resident

capital (flows associated with SA’s foreign assets) as described by Aron et al. (2010).

Figure 5: Current Account and Financial Account, South Africa 1970-2008 Source: B.Standish, UCT GSB MBA,

Economics, class notes 2010

After the 1994 democratic elections until 2000, the Balance of Payment flows showed

increases in volatility and magnitude that decreased between 2000 and 2002. However, since

2004, the magnitude of the BOP balances has increased dramatically. According to Smit

(2008), the current account balance increased from a deficit of 1.1 percent of GDP in 2003 to

7.3 percent of GDP in 2007. Total capital flows meanwhile increased from 0.7 percent of

GDP in 2003 to 5.9 percent of GDP in 2007 (Smit, 2008).

Finally, increases in foreign capital flows since 2004 have financed the sustained current

account deficit and allowed South Africa to increase its foreign exchange reserves (Smit,

2008).

2.3. Domestic Savings

This section describes domestic savings in South Africa and its relation to capital flows.

Economic growth in South Africa is constrained by low domestic savings and limited FDI

inflows. Investment levels can be sustained allowing economic growth only if foreign

savings, in the form of FPI inflows, supplement the available domestic resources (Lewis,

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2001). This results in South Africa relying on foreign capital inflows to finance its capital

account deficits (Aron & Muellbauer, 2000).

In addition, South Africa has had historically low domestic savings rates, which have

declined further since the 1980’s. The falling government saving rate since 1980 was mostly

to blame for this decline although personal saving rates and corporate saving rates have also

fallen in the same period (Aron & Muellbauer, 2000). Figure 6 illustrates the declining trend

in South Africa’s gross savings as a percentage of GDP since 1960.

Figure 6: South Africa’s gross savings as a percentage of GDP (1960-2008) Source: IMF website

2.4. Capital Flows

This section describes capital flows and the different types of capital flows in more detail.

Firstly, Loungani and Razin (2001) stated that economists supported the free flow of capital

across country borders since it allowed global capital to seek the highest return. This

improved global resource allocation and facilitated the transfer of technology to less

developed countries.

Capital flows allowed investors to reduce their risk through portfolio diversification and

promoted good policies in governments seeking to attract foreign capital. Lemi and Asefa

(2003), in agreement, commented that capital flows also reduced the shortage of capital in

developing countries, especially African countries, however economic uncertainty in

emerging and developing markets discouraged foreign investors.

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Secondly, capital flow research has mostly concentrated on the composition of international

capital flows and the differences in the risk and reward of the different types of capital flows.

Different types of capital flows have diverse ranges of risks and returns, liquidity, control,

and sustainability (IMF, 2004). Generally, capital flow research has found that direct

investment flows tend to be more stable than other forms of capital flows like portfolio debt

and equity flows (Neumann, Penl, & Tanku, 2009).

In addition, the South African Reserve Bank has reclassified capital flows in line with

international norms into three main categories. FDI involves investment into a firm such that

the foreign investor has at least 10% of the voting rights. FPI encompasses investment in

bonds and equities listed on international or domestic markets. Finally, other foreign

investment includes loans and deposits that occur between government, banks and companies

(De Gama, 2003; De Vita & Kyaw, 2008; Goldin & Reinert, 2005; Kyaw & Macdonald,

2009; Wesso, 2001).

Thirdly, capital flows to South Africa have largely comprised of volatile FPI flows as studied

by De Gama (2003) and Wesso (2001). Frankel (1999) proposed that the higher the

prevalence of FDI in capital inflows, the lower the chance of currency crashes, since FDI is a

long-term investment that is less volatile than FPI and takes longer to reverse.

In contrast, FPI is extremely volatile and may be withdrawn at the first sign of adverse

conditions. According to Sau (1994), FDI is felt more strongly in the goods market whereas

FPI is more flexible and felt in the asset market.

Next, Carlson and Hernandez (2002), citing Rodrik and Velasco (1999), concluded that FPI

inflows are driven by investors seeking high returns who leave when uncertainty arises which

could be highly destabilizing to fragile economies. Many countries therefore view FPI as

speculative and harmful since FPI is short term and driven by interest rate differentials

whereas FDI comes with tangible benefits for the country (De Gama, 2003; Frankel, 1999;

Kyaw & Macdonald, 2009; Schoeman, Robinson, & Wet, 2000).

Finally, Brink and Viviers (2003) proposed that FPI complements FDI and a liquid FPI

market may encourage FDI investment by allowing a foreign owned company to list on a

liquid domestic stock market to raise additional funds. Frankel (1999) added that FPI forces

governments to examine and review their economic policies as they become subject to the

scrutiny of the international capital markets and risk-averse foreign investors.

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2.5. FDI

This section has only a brief discussion of Foreign Direct Investment since the topic was not

the focus of the research.

Ibi Ajayi et al. (2006) stated that FDI was important to the economic development of

developing countries and many governments consider it a driver of growth. FDI supplements

domestic savings and helps develop the country’s economy through improving resource

allocation and human capital (Kandiero & Chitiga, 2006; Loungani & Razin, 2001; Musila &

Sigué, 2006). The IMF (2004) added that one of the benefits of FDI was the transfer of new

technology, which improves the skills of the host country’s labour force.

In contrast, Musila and Sigué (2006) citing Razin et al. (1999), asserted that there was a risk

that FDI may have unfavourable effects on employment, income distribution, and national

sovereignty and autonomy in low-income economies.

Figure 7 shows the FDI flows to South Africa between 1985 and 2009.

Figure 7: Net FDI flows, South Africa 1985-2009. Source: IFS

FDI to South Africa has increased since 1994 however, has remained low compared to the

volume of portfolio investment. As described by Aron et al. (2010), a number of large

foreign investments have occurred post-1994, which caused increases in FDI flows:

1997: the partial privatisation of Telkom

2001: the purchase of De Beers shares from minority shareholders during the

restructuring of Anglo American and De Beers

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2005: the partial purchase of ABSA by Barclays Bank

2008: the partial purchase of Standard Bank by the Industrial and Commercial Bank of

China.

A panel study done by Ahmed et al. (2005) between 1994 and 2002 found that FDI accounted

for 30 percent of capital inflows to South Africa during the period, compared to just over 70

percent for a group of emerging market economies with similar risk features.

The prevailing literature agreed that FDI was the more desirable foreign capital because of its

long-term nature, stability and direct benefits to the economy (Durham, 2000; Lipsey, 1999;

UNCTAD, 1999). However, more detail was not pertinent to this research study.

2.6. FPI

This section describes FPI in more detail since foreign portfolio investment was the focus of

the study. This is followed by a short discussion on the carry trade and ―hot money‖ which is

the volatile portfolio investment often blamed for financial crises.

Firstly, South Africa, like many African countries, has inadequate levels of domestic savings

to finance investment. Foreign donor aid and flows of FDI have also dwindled in recent

years particularly since the global financial crisis in 2007. In recent years, the predominant

form of foreign capital flows to South Africa has been portfolio investment (Mlambo, 2005).

Secondly, the classification of FPI by the SARB has played a role in explaining why South

Africa depends more on FPI inflows than FDI. SARB classifies a purchase of more than

10% of a company by a foreign investor as FDI whereas SARB defines a stake of 9.9% as

FPI.

Furthermore, the JSE is a large stock exchange with a significant number of companies

having large market capitalisations. Consequently, foreign investors tend to prefer portfolio

stakes (less than 10%) in South African companies, which SARB classifies as FPI (Gidlow,

2009).

In addition, the JSE has become one of the most important equity markets in Africa. The

stock exchange is large, technologically sophisticated and liquid compared to exchanges in

other developing countries. Strong growth in emerging markets, notably in commodity

producers like South Africa, has made South Africa an attractive investment destination,

especially during periods of commodity booms (Gidlow, 2009).

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Thirdly, Gidlow (2009) contends that flows of FPI since the early 1990’s, notably foreign

equity capital, have helped South Africa finance its current account deficit. The integration

of South Africa in global financial markets also has encouraged foreign investors to increase

their exposure to South African companies in their investment portfolios. Figure 8 shows

South Africa’s current account balance since 1960.

Figure 8: South Africa’s Current Account balance in millions US $ (1960-2008). Source: IMF website

Furthermore, since the transition to democracy, portfolio investment has dominated capital

inflows to SA, compared to other developing and emerging economies where FDI has been a

more significant component of capital flows (Aron, Leape, & Thomas, 2010). Next, Smit

(2008), pointed out that FPI to South Africa was comprised more of equity than bond

investments and this contrasts with other emerging markets where FDI was the dominant type

of capital flow.

To illustrate this difference, Figure 9 below shows the FDI flows for Brazil, Russia, China

and India (the so-called BRIC countries) compared to South Africa’s FDI flows between

1970 and 2008 while Figure 10 shows the portfolio flows to the same countries between 1979

and 2008.

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Figure 9: FDI net flows to the BRIC countries and South Africa (1970-2008) Source: World Bank

Figure 10: FPI net flows to the BRIC countries and South Africa (1979-2008) Source: World Bank

Figure 11 shows the increases (1997-1999 and 2004-2006) and decreases (2000-2003 and

2008) in net portfolio investment and net equity investment that South Africa has experienced

since 1994.

Figure 11: Net portfolio flows South Africa (1994-2009) and the division between debt and equity flows Source: IFS

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As described by Aron et al. (2010), the first increase between 1996 and 1999 was consistent

with portfolio rebalancing effects by both non-residents and residents. A decrease between

2001 and 2003:Q3 followed the increase. The decline in the Rand at the end of 2001 may

have increased domestic uncertainty, deterring foreign investment as a result. A general

decline in capital flows to emerging economies occurred during in 2001 as the dotcom asset

bubble burst. Equity inflows increased again in 2004-2005 and increased further in 2006 to

5.6 percent of GDP, its highest recorded level since 1999. This was consistent with increases

in foreign portfolio equity investment in developing countries.

Equity investment decreased towards the end of 2007, followed by large outflows as the

global financial crisis worsened in 2008. However, this outflow appeared to be temporary

since net inflows of equity investment resumed in the first half of 2009. According to Aron

et al. (2010), this suggested that foreign investors have regained their confidence in emerging

markets since the global recession.

In addition, Schoeman et al. (2000) believed that index traders invested the majority of

portfolio investment. Index traders are investors who purchased a basket of equities of

different countries according to their proportion in an emerging markets index such as the

International Finance Corporation (lFC) emerging market index.

Brink and Viviers (2003) proposed that although FPI was volatile and perceived as risky,

these risks were generally known and could be managed to a degree. In addition, FPI could

increase the liquidity of domestic capital markets and bring about greater market efficiency.

As a country’s capital markets deepen and become more liquid so governments could finance

more development projects through debt or equity instruments, which in turn stimulate

economic growth (De Vita & Kyaw, 2009; Jarita & Salina, 2008).

A different view, stated by Knill (2005), was that in the long-term FPI might reduce a

country’s dependence on capital flows by improving and stabilising the domestic markets

environment enough to lessen the impact of sudden outflows of FPI.

An argument against FPI was its role in causing financial crises. Short-term inflows may

cause an appreciation in exchange rates thereby increasing domestic inflation and resulting in

interest rates rising in an effort to curb inflation. These higher interest rates could cause

borrowers to default increasing the country’s risk for investment thereby resulting in capital

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outflows. If the outflows are severe enough, financial institutions risk collapse and the

country risks an economic crisis (FitzGerald, 1999; Kose, Prasad, & Terrones, 2003).

Furthermore, volatile FPI flows could hamper the implementation of macroeconomic policies

by governments. Uncertain FPI flows cause unpredictability in the money supply, exchange

rate level and stock market of a vulnerable economy. For example, sustained inflows could

result in asset price bubbles forming, setting off inflationary pressure. If a sudden withdrawal

of portfolio investment occurs followed by a correction in asset prices, this could present a

substantial risk of a financial crisis to the country’s economy (Jarita & Salina, 2008).

Analysts often blame the volatility of FPI for financial crises occurring. Sudden large

reversals of portfolio investment are usually linked with investor panics, since the reversal

was seen as a signal of impending financial crisis (Jarita & Salina, 2008) referencing Knill

(2004) and Sula & Willet (2006). Panicked investors who withdraw their capital can

exacerbate the financial crisis in a host country.

To clarify, volatile FPI is also known as ―hot money‖ and typically consists of equity or bond

investments that are short-term, mostly unproductive and which move rapidly between

countries (CUTS, 2003). The flows of ―hot money‖ are chiefly driven by the decisions of

investment managers in developed countries (Adelegan, 2009; Bae, Chan, & Ng, 2004;

Goldstein & Razin, 2002).

Furthermore, these investment managers are notoriously unpredictable and redirect

investment quickly at the first signs of increased riskiness or higher returns elsewhere in the

world. Emerging economies that have more sophisticated financial markets are more likely

to be harmed by market sentiment and ―sudden stops‖ of FPI than less advanced developing

countries (Adelegan, 2009;IMF, 2009).

Mboweni (2005) described signs of ―hot money‖ in South Africa as:

A high turnover of foreign exchange swaps, mainly with non-resident counterparties.

An accumulation of net forward commitments against the Rand by authorised dealers.

Non-residents increasing their investments of domestic bonds and equities by a large

amount.

In addition, Adelegan (2009) proposed that derivatives could stimulate domestic savings by

offering a wider range of investment options to domestic investors. However, the risks

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associated with the derivatives market included ―hot money‖ flows. He added that the

derivatives market in South Africa needed adequate regulation and supervision to reduce

volatile FPI and the effects of ―hot money‖.

Finally, the so-called ―carry trade‖ evident in Asia in the 1980s, 1990s and 2000s, was driven

by investors seeking to benefit from interest rate differentials and high growth in emerging

markets. This ―carry trade‖ is now becoming evident in Africa’s markets and is highly

destabilising when portfolio investment is suddenly withdrawn (Nellor, 2008).

To conclude, higher interest rates in host countries compared to investors’ home countries

(usually developed countries) typically drive FPI inflows to emerging markets. The riskiness

of the emerging market economy deters these inflows (Carlson & Hernandez, 2002).

2.7. Volatility

This section describes the volatility of capital flows and the attached risks.

In the past, portfolio equity investment in Africa has tended to be more stable and long term

than FPI flows in recent periods. For example, in 1980, investors generally followed a buy-

and-hold strategy and traded bonds in global financial market centres. Nowadays, investors

are entering Africa’s markets through a range of complex financial instruments including

derivatives. These instruments are far more volatile and prone to speculation (Nellor, 2008).

As a result, the volatility of FPI flows has become a major risk for emerging market

economies (Knill, 2005).

To illustrate, Neumann et al. (2009) discussed a study by Broner and Rigobon (2004) who

measured capital flow volatility for a selection of countries between 1990 and 2003. They

found that capital flows to emerging markets were 80% more volatile than capital flows to

developed markets.

Finally, Jordaan and Harmse (2001) described examples of emerging markets that have

experienced excessive foreign exchange rate instability due to volatile capital flows resulting

in financial crises. These included Mexico (1994), South Africa (1996 and 1998), Thailand

(1997) and Malaysia (1997).

2.8. Push and Pull factors

This section describes the push and pull factors that the prevailing literature sees as the major

influences on capital flows.

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To begin with, much of the literature examined divides the determinants of capital flows into

two categories: external push factors that ―push‖ capital flows towards developing countries,

and domestic pull factors that attract or ―pull‖ capital flows into developing countries (Alfaro,

Kalemli-Ozcan, & Volosovych, 2008; Brana & Lahet, 2010; Fernandez-Arias, 1996; Montiel

& Reinhart, 1999). This is often referred to as the ―Push and Pull framework‖ in the

literature.

According to Brana and Lahet (2010), if internal pull factors determine capital flows then

positive or improving economic policies in emerging countries is an essential condition for

stable growth. Conversely, if external push factors determine capital flows, this suggested

that the capital flows are highly unstable and likely to be ―hot money‖.

Pull factors are attractive domestic conditions that attract capital flows into a host country. In

contrast, push factors are undesirable home country conditions that push capital flows out of

a home country to seek higher returns in a foreign country (Fernandez-Arias, 1994).

To illustrate, pull factors include financial liberalization and privatization of the host

country’s capital markets (IMF, 2003); rates of return on domestic assets (Ahlquist, 2006);

macroeconomic stability; stable exchange rates; stable inflation rates; attractive tax

structures; a developed telecommunications infrastructure and availability of information to

investors (Brink & Viviers, 2003; Wesso, 2001). The absence of these pull factors in the host

country either reduces the rate of return a foreign investor can expect or increases the

riskiness of the investment (Brink & Viviers, 2003).

On the other hand, push factors include conditions external to the host country such as

business cycles; asset price behaviour in developed countries; and interest rates in developed

countries. Push factors are out of the control of the host country in attracting FPI (De Vita &

Kyaw, 2008; IMF, 2003) and occur in countries that are capital suppliers (mostly developed

countries). Growth rates, industrial production indexes and interest rates in developed

countries are good proxies for these types of variables (Amaya G & Rowland, 2004).

In addition, Calvo (1993) proposed that push factors such as low US interest rates

significantly explained increasing inflows of capital to Latin America. In contrast,

Hernandez and Rudolf (1995) declared that domestic variables (pull factors) were crucial to

explain the increase of capital flows into developing countries. They found that sound

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macroeconomic policies were likely to encourage foreign investment and maintain capital

inflows in the long term.

Montiel and Reinhart (1999), in agreement, found that domestic macroeconomic policy (pull

factors) had attracted more short-term capital inflows to Asia than to Latin America. The

researchers also found that push factors like international interest rates encouraged more

capital inflows to Latin American countries than Asian countries.

Meanwhile, Dasgupta and Ratha (2000) in their study of pull factors affecting capital flows,

found that net portfolio flows were negatively affected by a country’s current account

balance, and positively affected by both the country’s per capita income and growth

performance. In addition, the researchers found a significant positive relationship between

FDI flows and portfolio flows to developing countries.

Furthermore, a study carried out by Mody, Taylor, and Kim (2001) supported the importance

of pull factors in attracting capital inflows to emerging markets. The researchers found that

pull factors such as inflation, domestic credit, the industrial production index, the domestic

interest rate, credit ratings, reserve-import ratio and domestic stock market index were

significant in explaining capital inflows into all of the countries studied.

In contrast, Aron et al. (2010) concluded that push factors such as shocks in US high-yield

spreads, swap rates and US interest rates have a short-term affect on capital flows however

capital flows returned to an equilibrium state in the long term.

A few push factors in the form of financial crises affected the flow of FPI to South Africa

during the period studied. These were notably the Asian financial crisis in 1998, the dotcom

asset bubble bursting in 2001 and the global financial crisis in 2007. These financial crises

caused large FPI outflows and influenced the analysis of the time series data.

The most recent push factor to influence South Africa’s FPI flows negatively was the global

financial crisis in 2007. This crisis resulted in stricter worldwide credit regulations and more

risk-averse foreign investors (IMF, 2009). FPI flows to South Africa reversed as investors

turned to safer, liquid investments. The slowdown in global economic growth and the decline

in foreign savings have put South Africa’s development plans and the MDG plans of SADC

at risk (Macias & Massa, 2009). Figure 12 illustrates the trends in South Africa’s annual

GDP percentage since 1980 as an indicator of economic growth trends.

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Figure 12: GDP (annual %) in South Africa (1980 to 2008). Source: IMF website

Besides the FPI outflows, the global financial crisis was felt in South Africa as a drop in

share prices and a weakening in exchange rates as well as the expected economic growth

slowdown. Figure 13 shows the behaviour of the JSE All Share Index from 1997 to 2010.

The decrease in 2008 is evident.

Figure 13: JSE ALSI (1997-2010) Source: INET

Finally, Montiel and Reinhart (1999) suggested that push factors could aid in explaining the

timing and size of capital inflows whereas pull factors could explain the regional distribution

of flows.

In conclusion, the significant reversal in FPI flows in 2007and 2008, and South Africa’s

dependence on foreign investment for economic growth makes studies of FPI and its

influences especially relevant.

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2.9. Risk

This section discusses the different measures of risk linked with FPI and capital flows.

Firstly, most of the capital flows literature agrees that uncertainty deters capital inflows to a

developing country. This uncertainty includes perceptions of economic instability, political

instability, corruption and crime (Asiedu, 2006; Ibi Ajayi & Al, 2006; Musila & Sigué, 2006;

Reinhart & Rogoff, 2002).

Secondly, Lemi and Asefa (2003) added that this uncertainty originates from volatility in

macroeconomic variables such as exchange rates, resource prices, and interest rates. Ibi

Ajayi et al. (2006), citing Lehman (1999), proposed that less risky countries attracted more

FDI. However, foreign investors still perceived Africa as highly risky, which inhibited

capital flows to the continent.

To reinforce this statement, Muradzikwa (2002) quoted Arthur Levitt, former chair of the US

Securities and Exchange Commission:

―If a country does not have a reputation for strong corporate governance practices,

capital will flow elsewhere. If investors are not confident of the level of disclosure,

capital will flow elsewhere. If a country opts for lax accounting and reporting

standards, capital will flow elsewhere.‖ (p.16)

Thirdly, Brink and Viviers (2003) declared that foreign investors saw SSA as high risk for

investment. This risk derived from the unstable political environment, a perceived risk of

nationalisation, macroeconomic underperformance, and an immature financial market. There

were also contagion risks in the SSA region where high risks in one country (Zimbabwe for

example) could spill over into its neighbouring countries.

Analysts define country risk as the risk that a country defaults on its debt obligations.

Country risk includes the risk of war, revolution, expropriation of foreign property, or

confiscation of domestic property (Brink & Viviers, 2003).

Another measure of uncertainty, proposed by Mlambo (2005), was the rate of inflation and its

variability. High and variable rates of inflation as well as an unstable real exchange rate

created instability and increased investor risk. The variability of the real exchange rate was

another measure of risk for a country.

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In addition, some studies used the standard deviation of the GDP growth rate as a measure of

investment risk indicating the probability that the expected rate of return (growth rate) differs

from the actual rate of return. A high standard deviation indicated a high growth rate and

therefore a higher risk. Investors prefer a constant growth rate so a volatile growth rate

discourages foreign investment (Brink & Viviers, 2003).

Sovereign default risk, usually measured by sovereign credit ratings, reflected the

opportunities and risks of investing in a country and was an important stimulus for capital

flows (Al-Sakka & Gwilym, 2009; Kim & Wu, 2008; Taylor & Sarno, 1997).

Furthermore, Cantor and Packer (1996) pointed out that sovereign credit ratings included

macroeconomic indicators; country history; and social and political factors and advised credit

ratings as a measurement of country risk (Choi, Sharma, & Strömqvist, 2007).

Next Al-Sakka and Gwilym (2009), citing Biglaiser et al. (2008), added that foreign investors

observed sovereign credit ratings closely before investing capital in countries where the risk

was high and investor information was of a low quality. Brooks, Faff, Hillier, & Hillier

(2004), proposed that with investment portfolios becoming more global, understanding

country risk was critical.

Sovereign credit ratings communicate analysts’ views of a country’s economic and political

risk to investors. Rating agencies, like Moody’s, Fitch and Standard and Poor, see credit

ratings as a means of providing a forward-looking indication of the potential risk of the

ability (and willingness) of a debt issuing country to meet its financial obligations. These

financial obligations involved the country being able to make timely payments of the

principal and interest for the duration of a rated debt instrument (Sy, 2001).

Furthermore, sovereign credit ratings are an institutional measure of country risk. Countries

used sovereign credit ratings to access global capital markets as South Africa did post-1994.

Larrain, Reisen, and von Maltzan (1997) advised that investors usually preferred a rated bond

over an unrated one since the rating provided more information to the investor on the

riskiness of the investment and reduced investor uncertainty.

There are a number of sovereign credit rating agencies in existence, with different agencies

placing emphasis on different factors in their rating calculations (Baek, Bandopadhyaya, &

Du, 2005). However, macroeconomic fundamentals of the rated country mostly determine

the sovereign risk ratings.

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In addition, Cantor and Packer (1995) contended that sovereign credit ratings reduced

investor uncertainty and allowed many governments, even those with a history of default, to

access the international capital markets. Brooks et al. (2004) added that a change in a

sovereign credit rating usually triggered a reweighting on international portfolios and played

a key role in the volatility of FPI in emerging markets.

Moreover, the definitions of ―sovereign risk‖ and ―country risk‖ are different according to

Canuto, Dos Santos, and De Sa Porto (2004), citing Claessens and Embrechts (2002). They

described country risk as having a broader definition than sovereign risk.

Country risk is the risk of exposure to default by other creditors in a country and the country

itself. Sovereign risk meanwhile is associated with factors that may be under the control of

the government but not necessarily controlled by private firms or individuals and also

included all the financial assets of a country.

In addition, Kim and Wu (2008) investigated the influence of sovereign credit ratings

(specifically Standard and Poor's rating) on foreign capital flows to emerging countries.

They found that sovereign credit ratings were significant indicators of financial development

and provided an important stimulus for international capital inflows. They further found all

three forms of capital inflows (FDI, international banking and portfolio) increased

significantly as long-term ratings of emerging market sovereign debt improved (Kim & Wu,

2008).

With regards to country risk, the EMBI+ index produced by JP Morgan was the best known

market indicator for the risk of emerging market bonds and has been used in empirical studies

to measure ―country risk‖ (Canuto et al., 2004; Gapen, Gray, Lim, & Xiao, 2005; Robinson,

2007). Indices like the EMBI+ are generally more volatile in the short term compared to

sovereign risk ratings, which tend to reflect changes with a longer lifespan. However, over

the long term, investors can expect sovereign risk and country risk to converge.

Furthermore, JP Morgan divided the EMBI+ into two sub-indices for each country. Analysts

often referred to the sovereign spread of these sub- indices as ―country risk‖ (Canuto et al.,

2004). In addition, sovereign spreads depended on interest rates and currency risk as well as

technical factors such as liquidity conditions (Sy, 2001).

Sovereign Spread is the yield difference between a risky (emerging market bond) and a risk-

free instrument (US Treasury bond) with similar characteristics. Collaterised cash flows are

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stripped out to generate in the Stripped Yield (Garcia, 2004; Kim, Byun, & Ying, 2004).

Empirical studies often refer to the Stripped Yield as the Sovereign Yield for emerging

markets sovereign bonds (Kim et al., 2004).

Finally, the EMBI Global (EMBIG) produced by J.P. Morgan is an expanded version of the

EMBI+ Index and a more up to date measure of country risk. The EMBIG covers more

eligible instruments than the EMBI+ by relaxing the strict EMBI+ limits on secondary market

trading liquidity (Canuto et al., 2004).

In conclusion, there are a number of measures of risk investigated by this research. The

existing research on risk and capital flows proposes that increasing risk deters foreign

investment while encouraging capital outflows, which in turn reduces the resources available

for investment and slows down economic growth in a host country (Firat, 2007).

2.10. Interest Rates

This section describes how interest rates act as a pull factor for capital flows.

Firstly, Brink and Viviers (2003) described generally accepted economic theory, which states

that nominal interest rate differentials drive capital flows from low interest rate regions to

high interest rate regions. However, expanded capital flow theory adds that capital flows

depend on the expected rate of return as well as the risk appetite of the foreign investor.

Brink and Viviers (2003) concluded that the expected return should compensate the investor

for the perceived riskiness of the investment.

Secondly, South Africa has had attractive nominal interest rate differentials between itself

and its major trading partners, which implies that South Africa should continue to attract

volatile short-term FPI (Makhubela, 2004). Figure 14 shows the trends in South Africa’s

nominal Treasury bill rate compared to those of its major trading partners.

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Figure 14: Comparison of Treasury bill rates 1985-2009

Furthermore, Calvo, Leiderman and Reinhart (1996) defined interest rate differentials as the

key factor driving capital flows. Jordaan and Harmse (2001) agreed adding that a capital

outflow should follow a reduction in domestic interest rates, followed by a depreciation of the

exchange rate.

In disagreement was Gidlow (2009) who argued that foreign investors see higher interest

rates as a sign that economic growth was slowing down in a country as the government raises

interest rates to curb inflation, thereby discouraging investment in the country.

2.11. Methodology Comparison

This section examines the empirical studies on capital flows for South Africa and other

countries. The research used a comparison of previous studies done by Aron et al. (2010) as

the basis for reviewing the existing research conducted on South Africa’s capital flows.

Montiel and Reinhart (1999) in reference to Lane and Milesi-Ferretti (2000) identified four

main approaches to explain the level and composition of capital flows in the literature. These

are the sovereign risk literature, the optimal portfolio choice theory, the corporate finance

approach, and the pull and push factor framework.

The sovereign risk literature focuses on the influence of country risk on the magnitude and

volatility of capital flows. The portfolio diversification literature studies the composition of

capital flows and argues that the portfolio decision of foreign investors decide the

composition of capital flows. Meanwhile, the corporate finance literature identifies the effect

of asymmetric information, agency problems, and corporate control issues; and argues that

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the level of control that foreign investors have over assets in a host country explains the flow

of FDI to countries (Montiel & Reinhart, 1999).

In turn, the pull and push literature attempts to bring together the various investment

considerations of foreign investors. It distinguishes between domestic factors (pull factors)

and external factors (push factors) and forms the basis of many empirical studies of capital

flows. Several broad categories of macroeconomic, institutional, and policy variables are

identified that have an influence on the level and composition of capital flows (Montiel &

Reinhart, 1999). The push and pull framework is the approach followed in this study.

There are surprisingly few studies analysing capital flows for South Africa given its status as

an emerging market and the importance of capital flows for South Africa’s macroeconomic

policies. Most of the existing studies concerned themselves with the determinants of FDI

flows such as studies by Ahmed et al. (2005), Arvanitis (2005) and Fedderke and Romm

(2006).

Meanwhile, Wesso (2001) and Fedderke and Liu (2002) analysed total capital flows for

South Africa which included portfolio investment in the measure of total capital flows.

Firstly, Arvantis (2005) compared South Africa to a group of similarly rated countries using a

cross-country analysis. The researcher found that lower rates of growth, less trade openness,

a less developed telecommunication infrastructure, and a lack of skilled labour partly

explained the limited flow of FDI to South Africa. However, his study did not identify the

factors driving portfolio flows. The study done by Ahmed et al. (2005) had similar findings

and proposed that lower levels of growth, trade openness and infrastructure weaknesses

deterred FDI flows to South Africa.

Secondly, Ahmed et al. (2005) used a panel of 81 developing countries, which included

South Africa, to examine capital flows for 1975-2002. The researchers differentiated

between FDI and portfolio investment, and between portfolio debt and equity investment.

The results indicated that GDP growth positively affected total portfolio investment and

portfolio equity investment and GDP growth was not significant for debt investment. The US

T-bill rate had a negative effect for total portfolio investment, portfolio debt investment and

portfolio equity investment. A constructed index to proxy law and order appeared to be

weakly significant for portfolio equity investment and total portfolio investment. Stock

market capitalisation appeared significant for total portfolio investment and portfolio debt

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investment however, not for portfolio equity investment. Multiple exchange rate regimes

appear to have had a negative effect on debt investment. Finally, inflation volatility and

exchange rate volatility did not have a significant effect for total portfolio investment, equity

investment and debt investment (Aron et al, 2010).

Thirdly, Wesso (2001) examined quarterly data for South Africa from 1991 to 2000, which

covered some of the period of South Africa’s capital account liberalisation. He used a

measure of total net capital flows relative to GDP as the dependent variable without

distinguishing between portfolio and direct investment and performed a cointegration

analysis on the data to identify a long-term relationship. Wesso found the dependent

variables namely domestic inflation relative to foreign inflation and real GDP growth rate, to

cointegrate with capital flows. The researcher used dummy variables to explain the sudden

increase in capital flows in 1997:Q2, 2000:Q2 and 2000:Q4. The ratio between the

exchange-rate-adjusted SA and US government ten year bond rates was also included in the

cointegrating relationship. The budget deficit relative to GDP was included as a stationary

variable outside the cointegrating relationship in a single equation ECM model. Wesso also

found stronger growth and increased (relative) returns to have a positive effect on capital

flows whereas higher relative inflation and the government deficit had a negative effect on

capital flows (Aron et al, 2010).

Next, the study done by Fedderke and Liu (2002) used annual data for South Africa until

1995, which was prior to South Africa’s market liberalisation. Fedderke and Liu tested for

stationarity using the Augmented Dicky-Fuller test followed by the Johansen cointegration

test to identify a long run equilibrium relationship. They used three main independent

variables: the real growth rate; the exchange rate adjusted interest rate differential between

South Africa and the US; and the over/undervaluation of the exchange rate between the Rand

and the US dollar expressed relative to a constant. The researchers also used two dummy

indices to capture political instability and political rights in reference to Fedderke (2001).

The authors dummied out the years 1980 and 1981-84 to remove the effect of financial

liberalisation in 1980 and the gold boom years. In their results, Fedderke and Liu found a

relationship between the SA growth rate and total capital flows, as well as relationships

between capital flows and the interest rate differential and differenced exchange rate

over/undervaluation. The study further found that political risk had an impact in attracting

capital inflows (Aron et al., 2010).

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Meanwhile, Amaya G and Rowland (2004) citing Calvo et al. (1993, 1994 and 1996) stated

that while pull factors were important in the capital flows of the early 1990s, nevertheless the

main determinants were push factors. The study by Calvo et al. (1993) concentrated on

external factors driving FDI flows and concluded that declining US interest rates and

economic growth were the main drivers of funds into Latin American countries.

Similarly, articles by Chuhan et al (1998), Jeanneau and Micu(2002) and Filer (2004)

proposed that in the 1990s, external push factors explained more capital flows into Latin

America than internal factors, whereas internal pull factors explained more capital flows into

Asian countries than external push factors (Brana & Lahet, 2010).

Conversely, Çulha (2006) using a Structural Vector Auto Regression model (SVAR) found

that domestic pull factors, namely real interest rates, budget balance and current account

balance, appeared to be the most significant factors explaining capital inflows to Turkey

between 1992:Q1 and 2005:Q4. The author also found that external push factors such as

foreign interest rates were likely to affect capital flows.

A more recent study by Aron et al. (2010) focused on the determinants of inward foreign

investment particularly portfolio equity investment. The researchers used data between

1985:Q1 and 2007:Q4 and used an error correction model for econometric analysis. Aron et

al. investigated the importance of domestic economic fundamentals, which included interest

rate differentials, inflation differentials, GDP growth exchange rate changes, market indices,

trade openness and sovereign credit ratings. They used dummy variables for significant stock

market listings. The researchers found that there were positive effects on portfolio flows

from annual rates of change of real US GDP, the real US stock market index, improvements

in the government surplus to GDP ratio and changes in an index based on the S&P credit

rating. They also found negative effects on capital flows from the annual change in the real

JSE index, inflation differential relative to the US and for the long-term bond differential and

changes in a US equity market volatility index.

Finally, in another recent study, Abdullah, Mansor, and Puah (2010) studied the push and pull

factors that affected capital inflows to Malaysia between 1985:Q1 and 2006:Q4. They used

the Johansen cointegration test, error correction model and Granger causality tests to analyse

the data and found that domestic pull factors, real GDP, domestic T-bill rate, budget balance,

current account balance and US production Granger-caused capital inflows to Malaysia in the

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short run. The study also found that pull factors notably the budget deficit/surplus and

current account balance was significant in explaining inflows of capital into Malaysia.

See Appendix A for a detailed comparison of empirical studies on capital flows.

2.12. Conclusion

Loungani and Razin (2001), citing Hausmann and Fernández-Arias (2000), contended that

countries should concentrate on improving the environment for investment and the

functioning of markets and they are likely to be rewarded with increasingly efficient overall

investment and increased capital inflows. Schoeman et al. (2000) agreed and argued that for

South Africa to address its development problems successfully, the country needed to attract

substantially more foreign investment than in the past.

The significant net capital outflow experienced by South Africa in 2008 has highlighted the

South African economy’s sensitivity to foreign capital flows. In October 2008, the

withdrawal of nearly R67 billion of foreign investment from the JSE resulted in a 12 percent

decline in the FTSE/JSE Africa All-Share Index and a fall of almost 20 percent of the rand

against the US dollar (Harris, 2008). See Figure 13 for a graph of the FTSE/JSE Index.

The literature studied suggested that this research should expect FPI flows to South Africa to

increase with higher domestic interest rate differentials. Some literature disagreed and

proposed that interest rates would have a negative effect on capital inflows. The literature

also proposed that increased country risk would negatively affect capital inflows. The

EMBIG index was deemed an accepted measure of country risk. The research used other

measures of risk such as sovereign credit ratings, and the volatility of economic growth to

investigate their effects on FPI flows. The research followed the push and pull framework

discussed previously as the basis for the study of the determinants of FPI to South Africa.

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3. RESEARCH METHODOLOGY

This section outlines the methodology used in the analysis. It starts by outlining the approach

and the data collection. A detailed description of the variables follows and the section

concludes with a detailed explanation of the statistical techniques employed in the study.

3.1. Research Approach and Strategy

The approach used in this study was quantitative. Quantitative research, as defined by Leedy

and Ormrod (2010), uses statistical analysis on measurable sample data in order to develop an

explanation that researchers can apply predictably to other sample sets. Quantitative research

starts by constructing a hypothesis, then gathering sample data, which is analysed. Finally,

quantitative research concludes by establishing whether the data supports the initial

hypothesis or not.

The methodology used here closely follows the methodologies used by Wesso (2001), Çulha

(2006), De Vita and Kyaw (2008), Abdullah et al. (2010) and Aron et al. (2010) with some

variation in the independent variables used as external push and internal pull factors.

The study tests whether a relationship exists between flows of net FPI relative to real GDP or

FPI inflows relative to GDP and a number of explanatory push and pull factors using time

series quarterly data for South Africa from 1994 to 2010.

Net (2)

(3)

First, unit root tests were performed to confirm stationarity. This was followed by

cointegration tests that test for a long-term equilibrium relationship. A VAR model requires

stationary variables with no cointegrating relationship. Since the test found no cointegration,

the study used a structural VAR model to test for Granger causality. If the test had found a

cointegrating relationship, then an ECM model as used by Aron et al. (2010) would have

been more appropriate (Gujarati & Porter, 2009).

The external push factors investigated were Treasury bill interest rate differentials between

South Africa and four developed countries: Japan, China, the UK and the US. The US Fed

fund rate was used to proxy interest rates in developed countries and the US Industrial

Production Index was used to proxy production in developed economies.

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The internal pull factors investigated were real GDP; South Africa’s T-bill rate; real GDP per

capita; inflation proxied by the Consumer Price Index (CPI); and trade openness. Sovereign

credit ratings from Fitch, Moody’s and Standard & Poor and the JP Morgan EMBIG

Sovereign Yield over US treasury measured country risk. Finally, exchange rates between

the South Africa Rand and the US Dollar, British Pound, Euro, Japanese Yen and Chinese

Yuan were included as pull factors. (See Appendix B for the complete list of variables

considered in the study)

Quarterly data for each of the variables for the period of 1994:Q1 to 2010:Q3 was collected

from the IFS and INET online databases. The study used EViews 6 Student Edition to

perform statistical analyses on the time series data to measure the effect of the independent

variables on the dependent variables of net FPI flows and FPI inflows.

3.2. Data Collection

The sample data set comprised of quarterly observations, from the first quarter of 1994 to the

second quarter of 2010. The majority of the data was collected from the International

Financial Statistics online database. The International Financial Statistics (IFS) database

published by the International Monetary Fund (IMF) is the standard data source for capital

flows research and previous empirical studies have mostly used IFS to source data. The IFS

database provided the most comprehensive data on international capital flows and other

economic variables compared to online sources such as INET or DataStream.

The SA budget deficit/surplus relative to GDP and US Fed Fund Rate data was collected

from the I-Net Bridge INET online database. Meanwhile, quarterly data for the US Industrial

Production Index (IPI) was sourced from the Federal Reserve Bank of St Louis’s economic

research database.

The study used the research of Aron et al. (2010) to collect data on South Africa’s sovereign

credit ratings between 1994 and 2009 while the JP Morgan EMBIG stripped spread over U.S.

Treasuries Quarterly Change data was sourced from the JP Morgan online research database.

The EMBIG data only started in the first quarter of 1995 and was available until the third

quarter of 2010 covering the majority of the period studied.

3.3. Description of Variables

A more detailed description of each variable used in the study follows below.

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DEPENDENT VARIABLES

3.3.1. Net Foreign Portfolio Investment (NET_FPI)

Total net foreign portfolio investment relative to nominal GDP i.e. the sum of portfolio equity

investment and portfolio debt investment (liabilities plus assets).

For net FPI, the research follows the approach of Deléchat et al. (2008) who took the

increases (or decreases) in liabilities to non-residents added to the decreases (or increases) in

net foreign assets held by residents to be capital inflows (or outflows). This covered all

transactions that result in an inflow of foreign portfolio investment and avoided the case of

negative inflows. Typically net flows of capital are generally calculated as the sum of the

inflows (+) and the outflows (-).

The study used the following IFS data series for South Africa: Portfolio Investment Assets

(78bfd) and Portfolio Investment Liabilities (78bgd). The time series included transactions

with non-residents in financial securities (for example corporate securities, bonds, notes, and

money market instruments) other than direct investment, exceptional financing, and reserve

assets.

Figure 15: Net FPI relative to real GDP South Africa (1994:Q1-2010:Q2)

Figure 15 shows two potential structural breaks evident in the data, these correspond with the

dotcom asset crisis in 2001 and the global recession in 2008 when large net portfolio

outflows occurred.

3.3.2. Foreign Portfolio Investment inflows (FPI_INFLOWS)

The study used the following IFS data series for South Africa: Portfolio Investment

Liabilities (78bgd) where liabilities measure the inflows of foreign portfolio investment.

-.08

-.06

-.04

-.02

.00

.02

.04

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Net FPI (millions US$) relative to nominal GDP (millions US$)

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Figure 16: FPI inflows relative to real GDP South Africa (1994:Q1-2010:Q2)

PULL FACTORS

3.3.3. Real GDP (LOG_REAL_GDP)

A rapidly growing economy is likely to offer higher future earnings and therefore higher

returns combined with lower risks. The percentage change in real GDP proxied by the

logarithm of real GDP was used to model growth prospects in the South African economy.

The study used real GDP to neutralise the effect of inflation as per Abdullah et al. (2010).

This variable is widely used in empirical studies of capital flows as a determinant of capital

flows.

Real GDP in US dollars (millions) calculated by:

(4)

where the GDP Deflator used was the IFS GDP deflator 2005 base year.

Figure 17: Real GDP (millions US$) South Africa (1994:Q1-

2010:Q2)

Figure 18: Log of Real GDP South Africa (1994:Q1-

2010:Q2)

-.03

-.02

-.01

.00

.01

.02

.03

.04

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Portfolio Investment inflows (millions US$) relative to nominal GDP (millions US$)

80,000

120,000

160,000

200,000

240,000

280,000

320,000

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Real GDP in millions US$

11.6

11.8

12.0

12.2

12.4

12.6

12.8

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Log of Real GDP

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Figure 19: First Difference of Log_Real_GDP

3.3.4. Current Account Balance (CAB)

The Current Account Balance (millions US$) was relative to real GDP (millions US$).

Abdullah et al. (2010) also used this variable as a domestic pull factor in their capital flow

study.

Figure 20: Current Account Balance relative to real GDP South

Africa (1994:Q1-2010:Q2)

Figure 21: First Difference of Current Account Balance

relative to real GDP South Africa (1994:Q1-2010:Q2)

3.3.5. Inflation (LOG_INFLATION)

The study used the Consumer Price Index (Headline CPI) as a proxy for domestic inflation.

Abdullah et al. (2010) and Mody et al. (2001) also used this variable as a domestic pull

factor in their studies.

Countries with a stable macroeconomic environment typically have high and sustained

growth rates and low and stable inflation rates. These countries should receive more

portfolio investment than more volatile economies.

-.3

-.2

-.1

.0

.1

.2

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

D_LOG_REAL_GDP

-.030

-.025

-.020

-.015

-.010

-.005

.000

.005

.010

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Current Account Balance relative to GDP

-.012

-.008

-.004

.000

.004

.008

.012

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

D_CAB

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Figure 22: Consumer Price Index South Africa (1994:Q1-

2010:Q2)

Figure 23: Log of Consumer Price Index South Africa

(1994:Q1-2010:Q2)

Figure 24: First Difference of Log of Consumer Price Index

South Africa (1994:Q1-2010:Q2)

3.3.6. Budget Deficit/Surplus relative to GDP (BUDGET)

The research sourced the Budget Deficit or Surplus relative to GDP (percentage) data from

the INET database. Abdullah et al. (2010) also used this variable as a domestic pull factor in

their study.

A large budget deficit implies increasing government liabilities which suggests the need to

raise taxes and in worst cases, default on international debt. Therefore large budget deficits

increase the country risk and should deter portfolio investment.

40

60

80

100

120

140

160

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

CONSUMER PRICE INDEX (HEADLINE) INFLATION

3.8

4.0

4.2

4.4

4.6

4.8

5.0

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Log of Consumer Price Index (Headline CPI)

-.02

-.01

.00

.01

.02

.03

.04

.05

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

D_LOG_INFLATION

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Figure 25: Budget Deficit/Surplus relative to GDP South Africa

(1994:Q1-2010:Q2)

Figure 26: First Difference of Budget Deficit/Surplus relative

to GDP South Africa (1994:Q1-2010:Q2)

3.3.7. Trade Openness (OPENNESS)

Sum of exports and imports of goods and services measured as a share of GDP (Alfaro,

Kalemli-Ozcan, & Volosovych, 2008) . Trade openness is associated with liberalisation of

economies and there presumed to positively affect capital flows (Ferreira & Laux, 2009)

The study measures trade openness as the sum of exports and imports relative to GDP ratio as

per Ying & Kim (2001).

Figure 27: Trade Openness measure South Africa (1994:Q1-

2010:Q2)

Figure 28: Log of Trade Openness South Africa (1994:Q1-

2010:Q2)

-10

-8

-6

-4

-2

0

2

4

6

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

BUDGET DEFICIT/SURPLUS TO GDP (%)

-12

-8

-4

0

4

8

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

D_BUDGET

.35

.40

.45

.50

.55

.60

.65

.70

.75

.80

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Trade Openness (sum imports & exports relative to nominal GDP)

-1.0

-0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Log of Trade Openness

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Figure 29: First Difference of Log of Trade Openness South

Africa (1994:Q1-2010:Q2)

3.3.8. South Africa’s Treasury Bill Rate (LOG_SA_TBILL)

This variable used the South African Treasury Bill Rate (percentage per annum) quarterly

data. Abdullah et al. (2010) and Çulha (2006) also used this variable as the proxy for

domestic interest rates, representing the investor returns on domestic securities.

-

Figure 30: Treasury Bill Rate South Africa (1994:Q1-2010:Q2)

Figure 31: Log of Treasury Bill Rate South Africa

(1994:Q1-2010:Q2)

Figure 32: First Difference of Log of Treasury Bill Rate South

Africa (1994:Q1-2010:Q2)

-.20

-.15

-.10

-.05

.00

.05

.10

.15

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

D_OPENNESS

6

8

10

12

14

16

18

20

22

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

South African Treasury Bill Rate

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Log of South African Treasury Bill Rate

-.3

-.2

-.1

.0

.1

.2

.3

.4

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

D_LOG_SA_TBILL

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3.3.9. Country Risk (COUNTRY_RISK)

The EMBI Global stripped spread over U.S. Treasuries Quarterly Change served as a proxy

for country risk. Aron et al. (2010) also used this variable as a domestic pull factor in their

study.

The EMBI Country risk premium is the spread in the external cost of borrowing. It is

calculated by taking the returns for U.S. dollar-denominated Brady bonds, loans, Eurobonds,

and U.S. dollar-denominated local markets instruments for emerging markets less the total

returns for U.S. Treasury bonds with similar maturity (i.e. the stripped yields) (J.P. Morgan,

1999).

Figure 33: EMBI Global stripped spread over US Treasury Quarterly change

3.3.10. Exchange Rates

Wesso (2001) advised that exchange rate overvaluation was an important determinant for

capital outflows since an overvalued exchange rate is followed by an expected future

depreciation. Therefore, to avoid losses in terms of domestic currency, investors were likely

to hold their assets in a foreign currency.

US Dollar – South African Rand exchange Rate (LOG_USD_ZAR)

British Pound – South African Rand exchange Rate (LOG_GBP_ZAR)

Japanese Yen – South African Rand exchange Rate (LOG_YEN_ZAR)

Chinese Yuan – South African Rand exchange Rate (LOG_YUAN_ZAR)

Euro – South African Rand exchange Rate (LOG_EURO_ZAR)

-40

-20

0

20

40

60

80

100

120

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Country Risk proxied by EMBI Global stripped spread over U.S. Treasuries Quarterly Change

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Figure 34: Exchange Rates South Africa (1994:Q1-2010:Q2)

Figure 35: Log of Exchange Rates South Africa (1994:Q1-2010:Q2)

3.3.11. Real GDP per capita (LOG_GDP_PER_CAPITA)

The analysis calculated this variable by taking the value of real GDP in a quarter divided by

the population in the year.

The IMF (2004) study used GDP per capita as a measure of economic development as well as

an indication of market size. Abdullah et al. (2010) used this variable as a domestic pull

factor in their study.

0

4

8

12

16

20

24

28

32

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Euro South African Rand exchange rateBritish Pound South African Rand exchange rateUS Dollar South African Rand exchange rateJapanese Yen South Africa Rand exchange rateChinese Yuan South African Rand exchange rate

-1

0

1

2

3

4

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Log of Euro South African Rand exchange rateLog of British Pound South African Rand exchange rateLog of US Dollar South African Rand exchange rateLog of Japanese Yen South Africa Rand exchange rateLog of Chinese Yuan South African Rand exchange rate

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Figure 36: Real GDP per Capita South Africa (1994:Q1 -

2010:Q2)

Figure 37: Log of Real GDP per Capita South Africa (1994:Q1

- 2010:Q2)

3.3.12. Sovereign Credit Ratings

The data transformation step in the analysis involved linearly transforming the sovereign

credit ratings sourced from Aron et al. (2010) into a time series. The study followed the same

methodology employed by Kim and Wu (2008) and Aron et al. (2010).

The transformation inverted the total number of possible ratings for each rating agency to

calculate the increment per step of the rating. Next, the transformation allocated a zero

dummy value for the years where no rating was available. Then the appropriate rating score

given during the quarter that the rating change occurred was held until the next rating change

(Aron et al., 2010). (See Appendix C for more details on transforming Sovereign Credit

ratings into indices.)

Sovereign credit rating for South Africa from Fitch (FITCH)

Sovereign credit rating for South Africa from Moody’s (MOODYS)

Sovereign credit rating for South Africa from Standard and Poor (SP)

2,000

3,000

4,000

5,000

6,000

7,000

8,000

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Real GDP per Capita (US$ per person)

7.8

8.0

8.2

8.4

8.6

8.8

9.0

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Log of Real GDP per Capita

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Figure 38: Sovereign Credit ratings converted to indices South Africa (1994:Q1 - 2010:Q2)

PUSH FACTORS

3.3.13. Interest Rate Differentials

Rates of return are important to foreign investors who seek a favourable interest rate

differential for their capital. Since portfolio investment is typically short term in nature, the

study used the short-term Treasury bill rates between South Africa and four developed

countries. Japan is included as a known supplier of portfolio investment in the carry trade.

Differential between US and SA Treasury Bill rates (LOG_US_TBILL_DIFF)

Differential between UK and SA Treasury Bill rates (LOG_UK_TBILL_DIFF)

Differential between Japan and SA Treasury Bill rates (LOG_JAP_TBILL_DIFF)

Differential between China and SA Treasury Bill rates (LOG_CHI_TBILL_DIFF)

Figure 39: Treasury bill interest rate differentials (1994:Q1 - 2010:Q2)

0

2

4

6

8

10

12

14

16

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Fitch's sovereign credit rating for South AfricaStandard & Poor's Sovereign Credit Rating for South AfricaMoody's Sovereign Credit Rating for South Africa

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Differential SA and China Treasury Bill RateDifferential SA and Japan treasury bill rateDifferential SA and UK treasury bill ratesDifferential between SA and US Treasury bill rate

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Figure 40: Log of Treasury bill interest rate differentials (1994:Q1 - 2010:Q2)

Figure 41: First Difference of Log of US/SA T-bill differentials (1994:Q1 - 2010:Q2)

3.3.14. US Fed Fund rate (LOG_US_FED_RATE)

As per the IMF (2004) study, this research used short-term U.S. interest rates as a proxy for

international interest rates. As international interest rates rise, so the host country’s returns

are less attractive deterring portfolio inflows. Wesso (2001) suggested that if foreign interest

rates were higher than domestic interest rates, foreign portfolio flows into South Africa would

decrease. He added that the most commonly used push factors were US interest rates, which

have found to be significant in explaining capital flows to emerging markets.

0.4

0.8

1.2

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Log of differential between SA and China Treasury bill rate (% per annum)Log of differential between SA and Japan Treasury bill rate (% per annum)Log of differential between SA and UK Treasury bill rate (% per annum)Log of differential between SA and US Treasury bill rate (% per annum)

-.4

-.2

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94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

D_LOG_US_TBILL_DIFF

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Figure 42: US Fed Fund Rate (1994:Q1 - 2010:Q2) Figure 43: Log of US Fed Fund Rate (1994:Q1 - 2010:Q2)

Figure 44: First Difference of Log of US Fed Fund Rate

(1994:Q1 - 2010:Q2)

3.3.15. US Industrial Production Index (LOG_US_IPI)

As per the study by Abdullah et al. (2010), this research used the US Industrial Production

Index (IPI) as a proxy of an industrial country’s’ productivity. An improvement in the index

indicates the ability of industrial countries to accumulate capital to fund economic activities

in developing countries. However, this could lead to inflationary pressure in the US and

increased interest rates to curb the inflation. Higher interest rates in the US would attract

capital flows to the US hence, reducing capital flows to emerging economies (Abdullah et al.,

2010).

Figure 45: US Industrial Production Index (1994:Q1 - 2010:Q2) Figure 46: Log of US Industrial Production Index (1994:Q1 -

2010:Q2)

0

1

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US Fed Fund Rate (average per period)

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Log of US Fed Fund Rate

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D_LOG_US_FED_RATE

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US Industrial Production Index

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Log of US Industrial Production Index

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Figure 47: First Difference of Log of US Industrial Production

Index (1994:Q1 - 2010:Q2)

3.4. Research Instruments

The study used the EViews 6 Student Version software package to perform statistical

analysis techniques on the data. EViews 6 SV allows basic and more advanced econometric

analysis of data at an affordable price for student researchers. All the functionality of the full

version is available in the student version with a limited dataset size (EViews Product

website). However, this was more than adequate for the econometric analysis of the chosen

data series. EViews allowed for unit root testing, cointegration tests, VAR analysis and

Granger causality tests.

3.5. Sampling

The sample of data studied was quarterly time series data from the first quarter of 1994 to the

end of the second quarter of 2010 for each of the variables. The author chose the dates to

correspond with the liberalisation and opening up of the South African capital markets after

its first democratic elections in 1994.

This provided time series data with 66 data points per variable, which was sufficient for

statistical analysis. Some variables had fewer data points depending on when official

measurement of the variable started.

3.6. Research Criteria

Leedy and Ormrod (2010) defined reliability as the probability that measurement of the

variables gives consistent results. Reliability is also a necessary condition for validity.

In terms of this research, since the data is secondary data from reputable online databases, the

reliability comes in the statistical analysis of the data. The software used to analyse the data

-.05

-.04

-.03

-.02

-.01

.00

.01

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94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

D_LOG_US_IPI

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is a respected software package used in other empirical studies. The report describes the

analysis thoroughly allowing third parties to repeat the same research accurately.

Leedy and Ormrod (2010) further defined the validity of a research method as the accuracy,

meaningfulness and credibility of the research project. It is also important that the research

instrument used is suitable for the analysis.

This research project met the criteria of validity by citing referenced academic literature from

reputable sources. Furthermore, the study used accurately measured economic data from

respected institutions such as the IMF’s International Financial Statistics online database and

the I-Net Bridge economic data service. The data collected for analysis was deemed a

suitable representative sample of the population.

Furthermore the software package EViews is a tool commonly used for econometric analysis

and has inbuilt statistical functions that are suitable for this study such as the Augmented

Dicky-Fuller test for series non-stationarity, the Phillips-Peron unit root test, the Johansen

cointegration test, Granger causality tests, and the Vector Autoregression model.

Reliability and validity reflect the degree that measurements have errors. Since the data in

this research was from reputable data sources, any error in measurement was unavoidable and

the study could ignore it for the purposes of statistical analysis.

3.7. Data Analysis

The time series data collected was a collection of data measured at discrete points in time.

Gujarati & Porter (2009) defined time series as random or stochastic processes, which are a

set of random data points ordered over time.

To start, Gujarati and Porter (2009), advised that it is common practice in econometric

analysis to plot the natural logarithm of a time series to observe the growth rate of the series.

If the log of the variable increased over time, it indicated that the mean was also increasing

over time and that the time series was not stationary. Since the analysis was interested in

how the variables were changing over time, the study used the natural log of the data for

many of the independent variables.

As per Koop (2008) and Murray (2006), the percentage change in a variable is

approximately:

(5)

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The conversion of variables to logarithmic data was therefore the first step in the statistical

analysis.

When analysing time series data, the researcher should to be aware of two potential data

issues. Firstly, one time series variable can influence another variable with a time lag and

secondly if the variables are non-stationary then spurious regression may arise (Koop, 2008).

Since spurious regression results are misleading and incorrect, tests for stationarity were

conducted before performing a regression on the data.

Furthermore, Koop (2009) pointed out that a general rule of econometric analysis is that

nonstationary time series variables should not be included in a regression model unless the

variables have been transformed to make them stationary before running the regression. The

exception to this rule is if variables are cointegrated. Therefore following the stationarity

tests, the data was tested for cointegration.

Gujarati and Porter (2009), defined a stationary stochastic process as one where the mean and

variance were constant over time. It is important for a time series to be stationary otherwise

the behaviour observed only applies to the time period studied and it will not be possible to

generalise this behaviour to other time periods.

Most economic time series are difference stationary with at least one unit root present. In

general, a regression involving these I (1) series produces misleading results. In order to

force the difference stationary variables to be stationary, the analysis used the first differences

in subsequent analysis steps.

To explain further, if is a times series with one unit root i.e. non-stationary or

difference stationary, then ( ) is the first difference of and measures the

change or growth in a variable over time. If the natural logarithm of the original variable is

taken to be , then the difference variable or delta Y measures the percentage change in

the original variable over time (Koop, 2009).

A common example of a non-stationary series is the random walk:

(6)

where is a random disturbance term and α is a drift constant. A random walk series means

the value today is the value from yesterday plus an unpredictable error term (Koop, 2008).

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A difference stationary series is integrated and represented as I(x) where x is the order of

integration of the series. The order of integration is the number of unit roots contained in the

series, or the number of differencing operations it takes to make the series stationary. For the

random walk above, there is one unit root, so it is an I(1) series and contains a stochastic

trend. Meanwhile, I(0) denotes a stationary series (Koop, 2009).

If a time series has a unit root, then it is non-stationary so the second step in the data analysis

used the unit root tests provided by EViews to test for stationarity in the data. The popular

Augmented Dickey-Fuller (ADF) method tests for the presence of unit roots. The ADF test

uses the t-stat on the coefficient of the lagged dependent variable. If the t-statistic is greater

than the test’s critical values, then the ADF test rejects the null hypothesis of a unit root, and

the variable is stationary.

The ADF test on EViews uses the number of lagged difference terms (lag length) added to

the test regression. The EViews user guide suggested including a number of lags sufficient to

remove serial correlation in the residuals and EViews provides both automatic and manual

lag length options.

In addition, if the time series data is non-stationary, a cointegration test is required. Gujarati

and Porter (2009), cited Engle and Granger (1987) who observed that a linear combination of

two or more non-stationary series might be stationary and therefore the non-stationary time

series are cointegrated. This linear combination of two or more I(1) series defines a

cointegrating equation with a cointegrating vector of weights identifying the long-term

relationship between the variables. In other words, if series X and series Y have unit roots

however the combination of X and Y is stationary (no unit roots) then X and Y are said to be

cointegrated (Koop, 2009). From an economics point of view, two variables are cointegrated

if they have a long-term equilibrium relationship between them (Gujarati & Porter, 2009).

The test for cointegration, necessary to avoid spurious regression results, was performed after

the ADF unit root tests. EViews provides the Johansen cointegration test, which is only valid

for nonstationary data series. The Johansen test is also a more sophisticated cointegration test

than the basic Engle-Granger test (Koop, 2009).

If there are k time series variables, then there are k-1 cointegrating relationships possible.

The Johansen method tests for the number of cointegrating relationships called the

―cointegrating rank‖. Similar to the ADF test, the Johansen method tests the hypothesis of a

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certain cointegrating rank with the alternate hypothesis being that the cointegrating rank is

greater than the one tested for in the null hypothesis (Koop, 2008).

Regression looks at the dependence of one variable on other variables. However, it does not

necessarily imply causation; neither does high correlation (Gujarati & Porter, 2009; Koop,

2008). Estimating a regression model measures the effect of one or more explanatory

variables on the dependent variable.

In the case of time series data, the effect on the dependent variable may take some time to be

evident. In terms of a regression, this means that the value of the dependent variable depends

not only on the value of the explanatory variables in the same period but also of the values of

the explanatory variables in the past. It is therefore useful to use lagged variables in a

regression model to incorporate these past effects.

In time series data, there is a chance that there are structural changes in the data during the

period. These changes generally result from one-off external forces occurring, for example a

policy change, war or global recession. The structural changes cause the parameters in the

model to change over the period. Gujarati and Porter (2009) advise that accurate regression

results require the relationship between dependent and explanatory variables to remains

relatively unchanged over the period.

The Chow breakpoint test is a formal test for breakpoints offered by EViews to confirm

whether structural breaks exist in the time series data. The Chow test assumes that the

researcher knows the points of structural breaks. The idea behind the Chow test is to test the

regression equation on each subset of the data as well as the whole period and then compare

the estimated regression equations. The Chow breakpoint test compares the sum of squared

residuals (SSR) obtained by fitting an equation to the entire sample with the SSR obtained

when separate equations are fitted to each subsample.

Tests for Granger causality require the variables to be stationary. If the variables are

difference stationary, EViews can estimate an autoregressive model such as a VAR model

using the first differences of the variables. However, if the variables had been non-stationary

and co-integrated, the most suitable model to use is the ECM model (Bezuidenhout &

Mlambo, 2008). Standard practice in VAR analysis is to report the results from Granger-

causality tests, impulse responses, and forecast error variance decompositions. (Stock &

Watson, 2001)

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Econometrics usually uses the VAR model for forecasting systems of interrelated time series

variables and for investigating the impact of random disturbances on the system of variables

(EViews II, 2009). A VAR model is an n-equation, n-variable linear model in which ―each

variable is explained by its own lagged values, plus current and past values of the remaining

n-1 variables‖ (Stock & Watson, 2001, p.1).

The mathematical representation of a VAR is:

(7)2

where is a k vector of endogenous variables, is a vector of exogenous variables,

and B are matrices of coefficients to be estimated, and is a vector of innovations

that may be concurrently correlated but are uncorrelated with their own lagged values and

uncorrelated with all of the right-hand side variables. Endogenous variables are the

equivalent of dependent variables in a single-equation regression model and exogenous

variables are the equivalent of independent variables.

The VAR model in EViews provides a framework for Granger Causality testing between sets

of variables. The Granger-causality statistics test whether lagged values of one variable helps

to predict changes in another variable (Stock & Watson, 2001).

Granger causality only applies to time series data and proposes that a variable x Granger-

causes y if past values of x help explain changes in y. This does not guarantee x causes y

therefore the name ―Granger-causality‖ is used rather than ―causality‖ (Koop, 2009).

There is also the possibility of bi-directional Granger-causality where x Granger-causes y and

y Granger-causes x. The data analysis takes each independent variable and tests for Granger

causality to prove or disprove the research hypotheses.

2 Source: EViews II (2009)

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4. RESEARCH RESULTS

This section summarises the research findings, followed by a detailed description of the

methodology followed in the study. The section concludes with a discussion of the findings

and the research limitations.

4.1. Research Findings

This section identifies the main determinants of foreign portfolio investment to South Africa

between 1994 and 2010 using unrestricted VAR modelling. The choice of independent

variables is in line with the Push and Pull framework prevalent in the capital flow literature.

This study closely followed the methodologies used by Wesso (2001), Çulha (2006), De Vita

and Kyaw (2008), Abdullah et al. (2010) and Aron et al. (2010) who used structural VAR or

ECM models with a selection of push and pull explanatory variables to analyse the

determinants of capital flows to various countries.

The generally accepted equation for the determinants of portfolio flows is:

u (8)

where X is a vector of Pull factors (domestic factors) and Y is a vector of Push factors

(external factors). FPI could be either net FPI or FPI inflows, c is a constant and u is an error

correction term.

In this study

X = function {LOG_REAL_GDP, LOG_INFLATION, LOG_SA_TBILL,

COUNTRY_RISK, LOG_OPENNESS, SP} (9)

and

Y = function {LOG_US_TBILL_DIFF, LOG_US_FED_RATE, LOG_US_IPI} (10)

In terms of the VAR model of lag length 1:

(11)

where X is a vector of endogenous variables containing:

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NET_FPI/FPI_INFLOWS, D_LOG_REAL_GDP, D_LOG_INFLATION, D_LOG_SA_

TBILL, COUNTRY _RISK, D_LOG_OPENNESS, SP, D_LOG_US_TBILL_DIFF,

D_LOG_US_FED_RATE, D_LOG_US_IPI. 3

Unlike the studies by Aron et al. (2010) and Wesso (2001), not all the variables in this study

were found to be difference stationary after conducting ADF unit root tests. The analysis

found the dependent variables of NET_FPI and FPI_INFLOWS to be stationary over the

whole period and two sub-periods. The explanatory variables for country risk and sovereign

credit ratings were also stationary over the period. The rest of the variables were found to be

integrated of order 1 i.e. difference stationary.

A cointegration test did not find a unique cointegrating relationship between the variables.

This was contrary to the findings of Aron et al. (2010) and Wesso (2001) who did find a long

run relationship in their studies of South African capital flows. However, this finding was

consistent with that of De Vita and Kyaw (2008) who found that cointegration depending on

the length of the period under observation. They found that cointegration failed to hold with

longer time spans indicating the effect of shocks across different time horizons, which could

change or even break the long run cointegration relationship (De Vita & Kyaw, 2008). It is

likely there are shocks during the period used in the study that caused a break in the long run

cointegration relationship. The use of dummy variables to nullify the effect of shocks would

have been appropriate.

The entire data period for FPI inflows passed the Chow breakpoint test for structural

breakpoints. As a result, the VAR analysis continued to use the whole data sample and not

subsets of the selected period. The breakpoint test found a structural break between 2000:Q4

and 2001:Q3 in the case of net FPI, which required a dummy variable compensating for the

structural break in the VAR analysis.

The research used the coefficients of the estimated equations resulting from the VAR analysis

for dependent variables NET_FPI and FPI_INFLOWS to draw conclusions about the

explanatory variables’ effects on FPI flows to South Africa in the period.

The significant variables affecting net FPI and FPI inflows included the lagged value of US

Industrial Production Index that had a positive effect and the lagged value of trade openness

3 D_ refers to the first difference of the variable.

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that had a negative effect. The lagged value of the domestic T-bill rate had a positive effect

whereas the lagged value of inflation and real GDP both had a negative effect on net FPI and

FPI inflows.

Finally, the research conducted pairwise Granger causality tests between net FPI, FPI inflows

and the explanatory variables. The tests showed net FPI and FPI inflows were Granger-

caused by the US Fed Fund rate, the US Industrial Production Index, Standard & Poor’s

sovereign credit rating, and real GDP. The tests also showed that NET_FPI and

FPI_INFLOWS Granger-caused Standard & Poor’s sovereign credit rating and real GDP.

The next section describes the process used to analyse the data in more detail.

4.2. Research Analysis

The first step in the study was to collect the data from the online IFS and INET databases.

The sovereign credit ratings were sourced from the study done by Aron et al. (2010), the JP

Morgan EMBIG index was sourced from the JP Morgan website and the US IPI was sourced

from the St Louis Federal Reserve Bank website. Next, the GDP per capita and the treasury

rate differential time series were calculated. Finally, the data transformation step converted

the sovereign credit ratings into numerical indices following the same process as Aron et al.

(2010).

The time series variables were entered into EViews 6 as Series Objects. The next step

created new Series objects in EViews as the natural logarithms of all the variables except

CAB, BUDGET, COUNTRY_RISK and the 3 sovereign credit rating indices.

4.2.1. Descriptive Statistics

Appendix D summarises the descriptive statistics for each variable. All variables used a

sample of greater than 30 observations.

4.2.2. Stationarity and Unit Root Tests

Augmented Dicky-Fuller unit root tests

The next step conducted Augmented Dicky-Fuller unit root tests on the variables using the

ADF test available in EViews to determine the order of integration i.e. number of unit roots

for each variable. The analysis first ran the ADF tests for the Level and then the First

Difference of each variable. The test used the default Automatic lag length setting that

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allows EViews to select the most appropriate lag length. (See Appendix E for the detailed

results for the ADF unit root tests).

If the t-statistic falls in the rejection area i.e. outside of the critical values then the test rejects

the null hypothesis that a unit root exists and proves the alternate hypothesis that no unit root

exists.

In the ADF Level test (Appendix E), NET_FPI, FPI_INFLOWS, COUNTRY_RISK, FITCH,

MOODYS, and SP are stationary and therefore have no unit root. However, the remainder of

the variables had t-statistics that were in the null hypothesis acceptance region meaning they

had at least one unit root. The ADF tests using the First Difference option indicated that all

of the differenced variables with the exception of CAB were stationary at the 1% level of

significance. The CAB variable was stationary at 5% and 10% and not at 1%.

The results of the ADF test were that NET_FPI, FPI_INFLOWS, COUNTRY_RISK, FITCH,

MOODYS, and SP were I(0) or stationary over the sample period whereas the rest of the

variables excluding CAB, were I(1) or difference stationary. A further ADF tested showed

CAB to be stationary at the 1% significance level for a 2nd

Difference test.

Next, the analysis divided the dependent variables into 3 sub-samples to exclude the

structural breaks observed in the data from 2001:Q2 to 2001:Q3 and from 2008:Q3 to

2008:Q4. The analysis repeated the ADF unit root test on the six sub-samples with the

following results:

Table 1: Unit root tests for sub-samples of NET_FPI and FPI_INFLOWS

The test results in Table 1 confirmed that NET_FPI and FPI_INFLOWS are stationary across

the whole period and for the first two subsets of data because the t-statistics were in the

rejection range for the null hypothesis of an existing unit root. The final subset showed the

Variable Name Sample t-statistic

Reject Null

Hypothesis

1% level 5% level 10% level

NET_FPI 9/01/1994 3/01/2001 -4.22626 YES – s tationary -3.69987 -2.97626 -2.62742

NET_FPI 9/01/2001 9/01/2008 -5.93456 YES – s tationary -3.67932 -2.96777 -2.62299

NET_FPI 12/01/2008 6/01/2010 -1.7194 No -4.80349 -3.40331 -2.84182

FPI_INFLOWS 9/01/1994 3/01/2001 -3.75013 YES – s tationary -3.699871 -2.976263 -2.62742

FPI_INFLOWS 9/01/2001 9/01/2008 -4.939109 YES – s tationary -3.679322 -2.967767 -2.622989

FPI_INFLOWS 12/01/2008 6/01/2010 -1.438766 No -4.803492 -3.403313 -2.841819

Critical values

Warning: Probabilities and critical values calculated for 20 observations and may not be

accurate for a sample size of 7

Warning: Probabilities and critical values calculated for 20 observations and may not be

accurate for a sample size of 7

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NET_FPI and FPI_INFLOWS to be difference stationary however due to the small sample

size, these findings were excluded and overall NET_FPI and FPI_INFLOWS was assumed to

be stationary across the period.

Phillips-Perron unit root tests

Next, the analysis conducted PP unit root tests on the level, first difference and second

differences of all the variables as a further test of stationarity. As per the ADF tests, if the t-

statistic fell outside of the critical values, the test rejects the null hypothesis of a unit root

existing. (See Appendix F for the detailed results for the PP unit root tests).

For the variables CAB, FITCH, MOODYS, SP, LOG_US_TBILL_DIFF, LOG_UK_TBILL

_DIFF, LOG_JAP_TBILL_DIFF, LOG_CHI_TBILL_DIFF, LOG_US_ FED_RATE and

LOG_US_IPI: the ADF and PP tests had the same results.

However, the PP unit root tests had contradictory results for NET_FPI, FPI_INFLOWS,

LOG_REAL_GDP, LOG_INFLATION, BUDGET, LOG_SA_TBILL, COUNTRY_RISK,

LOG_USD_ZAR, LOG_GBP_ZAR, LOG_YEN_ZAR, LOG_YUAN_ZAR,

LOG_EURO_ZAR, LOG_GDP_ PER_CAPITA and LOG_OPENNESS. Appendix G shows

a comparison of the results of the ADF and PP unit root tests for each variable.

As described by Fedderke (2003), Perron (1989) showed that if a trend-stationary series with

a structural break, i.e. a series with a permanent change in slope or intercept at some point

was tested for unit roots, then the test would show non-stationarity. However, in reality the

series is stationary; the presence of a structural break in the data misleads the unit root test.

Since the Augmented Dicky-Fuller test is the more widely used unit root test in empirical

studies, the analysis continued using the ADF unit root results.

Although the data was further tested for cointegration, this test was relatively meaningless

given that the two dependent variables were found to be stationary by the ADF tests.

Therefore, any long-run cointegrating relationship was unlikely.

4.2.3. Johansen Cointegration Test

Since the ADF tests found the variables to be mostly non-stationary, a cointegration test was

required before a regression analysis could be conducted on the data. In EViews, the study

performed the Johansen test on a Group object i.e. a set of time series variables. The

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Johansen cointegration test examined the long-term equilibrium relationship that exists

between variables.

At this point in the analysis, on the recommendation of an experienced EViews user, the

number of independent variables was reduced to a more manageable number since the

EViews software was unable to run a cointegration test without errors on the larger group of

variables. Reducing the number of variables allowed EViews to run the Johansen test

without errors and the analysis to continue.

Instead of using three sovereign credit ratings, the researcher excluded the Fitch and Moody’s

ratings from the analysis and retained the Standard and Poor sovereign credit rating as an

explanatory variable. The researcher also excluded the five exchange rate variables as pull

factors.

Next, the researcher excluded the current account balance, budget deficit/surplus, and real

GDP per capita. Finally, the T-bill differential variables were reduced to a single variable:

the differential between the SA and US T-bill rate. This left eleven variables, the two

dependent variables and nine explanatory variables. The study created two Group objects in

EViews: NET_FPI combined with the nine independent variables and FPI_INFLOWS

combined with the nine independent variables.

Reducing the number of variables allowed EViews to run the Johansen cointegration test

without errors on the two Group objects. Since some of the time series were stationary, the

test was run on both Groups with case 4 (Intercept and trend in CE – no intercept in VAR)

selected as suggested by the EViews user manual. With case 4, the test assumes the level

data and the cointegrating equations have linear trends.

Appendix H shows the results of the Johansen cointegration test for FPI_INFLOWS and the

independent variables. The Trace test indicates seven cointegrating equations at the 0.05

level of significance. Meanwhile, the Max-Eigenvalue test for FPI_INFLOWS indicates four

cointegrating equations at the 0.05 level of significance.

Appendix I shows the results of the Johansen cointegration test for NET_FPI and the

independent variables. The Trace test indicates eight cointegrating equations at the 0.05 level

of significance. Meanwhile, the Max-Eigenvalue test indicates four cointegrating equations

at the 0.05 level of significance.

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It was clear from the cointegration test results that no unique cointegrating relationship

between the variables existed. In other words, there was no shared long-run trend between

the variables. This is likely because of a stationary dependent variable and the mix of

stationary and non-stationary variables in the cointegration test.

As stated by (Murray, 2006), in order for a set of series to be cointegrated of order k, each

series in the set must be integrated of the same order k+1. In other words, a set of series

integrated with order k, is cointegrated if there exists a linear combination with non-zero

weights that is integrated of order less than k.

In this study most of the series are integrated of order 1 so in order for the set of series to be

cointegrated of order 0 (i.e. stationary); each series would need to be integrated of order 1. In

this case, they are of mixed integration since some of the series are stationary to begin with.

In order to continue with the regression analysis, the first differences of all the non-stationary

variables were calculated. Regression analysis could be performed on either stationary data

or on cointegrated data. In this case, taking the first differences of the non-stationary

variables converted them into stationary variables, which allowed EViews to perform a

regression analysis on the resulting stationary variables. The methodologies used by Çulha

(2006) and Korap (2010) do not use differenced variables in their SVAR models.

Çulha referred to Sims (1980) who argues against differencing variables in an SVAR analysis

even if the time series data has a unit root. The researchers found that transforming variables

to their stationary form by differencing is unnecessary in VAR analysis. To err on the side of

caution, this study did use differenced variables in the unrestricted VAR model.

4.2.4. Chow test for structural changes

The next step in analysing the data was to test for structural changes in all of the variables

since it was suspected there were structural breaks in 2001 and 2008 from the graphs of the

raw data in Section 3.3.

By default, the Chow breakpoint test offered by EViews tests whether there is a structural

change in all of the equation parameters. The Chow test fits the estimated equation

separately for each subsample on either side of the specified breakpoint and checks whether

there are significant differences in the estimated equations of the subsamples.

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The standard Chow test in EViews failed due to the chosen breakpoints resulting in

subsamples with an insufficient number of observations to calculate the Chow test accurately

so the analysis used the Chow Forecast test instead. The Chow forecast test estimates the

model using a subsample that is comprised of the first set observations before the breakpoint.

The remaining data points are then predicted using the estimated model equation.

Firstly, two equation objects with NET_FPI as the dependent variable and two equations

with FPI_INFLOWS as the dependent variables were created in EViews to estimate the

regression equation over two sub-periods using Least Squares regression. The first two

estimated equations for NET_FPI and FPI_INFLOWS used data between 1994:Q1 and

2007:Q1. EViews ran the Chow forecast test on the estimated equations with breakpoints at

quarterly intervals between 2000:Q4 and 2002:Q4. 4

The second two estimated equations for NET_FPI and FPI_INFLOWS used data between

2002:Q1 and 2010:Q2. EViews ran the Chow forecast test on the estimated equations with

breakpoints at quarterly intervals between 2007:Q1 and 2009:Q3.

The results of the Chow forecast tests for the four estimated equations are in Appendix J.

None of the calculated F-values exceeded the critical F-Value for the dependent value of

FPI_INFLOWS meaning the test could not reject the null hypothesis of no structural breaks.

Since the sample period passed the Chow breakpoint test, this indicated there were no

structural breaks in the FPI_INFLOWS data. This was consistent with the findings of Aron

et al. (2010).

However, there appeared to be a structural break between 2000:Q4 and 2001:Q1 when

NET_FPI was the dependent variable. The second sample period for NET_FPI passed the

structural break test.

The Chow forecast tests indicated that the initial assumption of structural breaks in 2001 and

2008 was false except in the case of NET_FPI which allowed a VAR analysis to be

performed across the whole period 1994:Q1 to 2010:Q2 with a dummy variable

compensating for the structural break in the case of NET_FPI.

4 To have EViews calculate the 5% critical F-value for the Chow test, the following equation in the

command window =@qfdist(0.95,ols_period1.@ncoef,ols_period1.@regobs-2*ols_period1.@ncoef), was used

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4.2.5. VAR analysis

The first step in a VAR analysis was to decide on the maximum lag length for the model

estimation. Too many lagged terms would use up degrees of freedom whereas too few lags

could result in model errors. The analysis chose the VAR estimation that provided the lowest

value of the Akaike or Schwarz criteria, to indicate the most appropriate lag length to use in a

VAR analysis (Gujarati & Porter, 2009).

Unrestricted VAR analyses were run on the two groups of variables across the whole period

for 0 to 4 lags since five lags generated an error in EViews. (See Appendix K for results).

The most appropriate lag length appeared to be four lags as this produced the lowest (most

negative) Akaike information criteria. However, the Schwarz criterion contradicted the

Akaike results and recommended that the VAR analysis should use a lag length of one.

Murray (2006) noted that econometricians differ on which measure is best to determine lag

length. Koop (2008) advised that the Schwarz criterion is the more commonly used in model

selection. In addition, both Korap (2010) and Çulha (2006) used the Schwarz criterion in

their determination of lag length.

Furthermore, since the model already contained a large number of explanatory variables and

lags of these variables would generate even more variables, the VAR model used a lag length

of one rather than four in the next step of the analysis.

As per Çulha (2006), the study models NET_FPI and FPI_INFLOWS as follows:

NET_FPIt = f{utD_LOG_US_FED_RATE

, utD_LOG_US_TBILL_DIFF

, utD_LOG_US_IPI

, , utSP

,

utD_LOG_OPENNESS

, utCOUNTRY_RISK

, utD_LOG_SA_TBILL

, utD_LOG_INFLATION

,

utD_LOG_REAL_GDP

, utNET_FPI

, utDUMMY

} (12)

FPI_INFLOWSt = f{utD_LOG_US_FED_RATE, ut

D_LOG_US_TBILL_DIFF, utD_LOG_US_IPI, , ut

SP, ut

D_LOG_OPENNESS, utCOUNTRY_RISK, ut

D_LOG_SA_TBILL, utD_LOG_INFLATION, ut

D_LOG_REAL_GDP, ut

FPI_OPENNESS } (13)

Following Çulha (2006), the VAR (1) model is:

(14)

where

Yt = (D_LOG_US_FED_RATEt, D_LOG_US_TBILL_DIFFt, D_LOG_US_IPIt, SPt, D_LOG_OPENNESSt, COUNTRY_RISKt, D_LOG_SA_TBILLt, D_LOG_INFLATIONt, D_LOG_REAL_GDPt, DUMMYt, NET_FPIt or

FPI_INFLOWSt)',

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Ut = (utD_LOG_US_FED_RATE, ut

D_LOG_US_TBILL_DIFF, utD_LOG_US_IPI, ut

SP, utD_LOG_OPENNESS, ut

COUNTRY_RISK, ut

D_LOG_SA_TBILL, utD_LOG_INFLATION, ut

D_LOG_REAL_GDP, [utDUMMY], ut

NET_FPI or FPI_INFLOWS)'

and A(L) =

= {aij(L)} as L lag operator. The matrix of long run effects of structural

shocks is {aij(L)}.

A structured VAR analysis involves specifying the long run matrix, which includes

restrictions on variables. However, EViews consistently generated errors during the SVAR

matrix setup so the analysis continued with an unrestricted VAR model instead. The results

of the complete VAR analysis for NET_FPI and FPI_INFLOWS are in Appendix M.

Each column in Table 23 and 24 in Appendix M corresponds to the equation for one

endogenous variable in the VAR. For each right-hand side variable, EViews reports the

standard OLS regression statistics for each equation: a coefficient point estimate, the

estimated coefficient standard error, and the t-statistic. At the bottom of the results are the

summary statistics for the VAR model as a whole.

After running the unrestricted VAR analysis for NET_FPI and FPI_INFLOWS, the AR Roots

Graph tested the stability of each VAR model. Appendix L shows the AR Roots Graphs for

the NET_FPI and FPI_INFLOWS models.

A VAR model is stable if all the roots have an absolute value less than one and lie inside the

unit circle. Since there were no roots outside the circles, the tests verified that the VAR

models were stable.

The result of the unrestricted VAR analysis (0 to 1 lags) is an estimated equation for

NET_FPI as follows:

NET_FPI = (-0.048) *c +

(-0.00925) * D_LOG_US_FED_RATE (-1) + (-0.03274) * D_LOG_US_TBILL_DIFF (-1) + (0.67277) * D_LOG_US_IPI (-1) + (0.06969) * D_LOG_SA_TBILL (-1) +

(-0.41281) * D_LOG_INFLATION (-1) + (-0.05754) * D_LOG_REAL_GDP (-1) + (-0.06444) * D_LOG_OPENNESS (-1) + (0.00376) * SP (-1) + (-0.00005) * COUNTRY_RISK (-1) + (-0.0067) * DUMMY (-1) + (-0.1495) * NET_FPI (-1) (15)

and for FPI_INFLOWS:

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FPI_INFLOWS = (-0.004063) *c +

(-0.002801) * D_LOG_US_FED_RATE (-1) + (-0.026143) * D_LOG_US_TBILL_DIFF (-1) + (0.433476) * D_LOG_US_IPI (-1) + (0.041712) * D_LOG_SA_TBILL (-1) +

(-0.245173) * D_LOG_INFLATION (-1) + (-0.07127) * D_LOG_REAL_GDP (-1) + (-0.08376) * D_LOG_OPENNESS (-1) + (0.001003) * SP (-1) + (-0.00003) * COUNTRY_RISK (-1) + (0.16622) * FPI_INFLOWS (-1) (16)

From the signs, magnitude and significance of the coefficients above, the following

conclusions were drawn:

The lagged US Fed Fund Rate and US T-Bill rate differential negatively affect NET_FPI

and FPI_INFLOWS. The magnitude of the coefficient indicates a weak relationship.

The lagged US Industrial Production Index positively affects NET_FPI and

FPI_INFLOWS. The magnitude of the coefficient indicates a strong relationship.

The value of the South African T-bill rate positively affects NET_FPI and

FPI_INFLOWS. The magnitude of the coefficient indicates a relatively strong

relationship.

The lagged value of inflation negatively affects NET_FPI and FPI_INFLOWS. The

magnitude of the coefficient indicates a strong relationship.

The lagged value of real GDP negatively affects NET_FPI and FPI_INFLOWS. The

magnitude of the coefficient indicates a relatively strong relationship.

The lagged value of trade openness negatively affects NET_FPI and FPI_INFLOWS.

The magnitude of the coefficient indicates a relatively strong relationship.

The lagged value of sovereign risk positively affects NET_FPI and FPI_INFLOWS. The

magnitude of the coefficient indicates a weak relationship.

The lagged value of the country risk negatively affects NET_FPI and FPI_INFLOWS.

The magnitude of the coefficient indicates a weak relationship.

NET_FPI is negatively affected by the lagged value of itself whereas FPI_INFLOWS is

positively affected by the lagged value of itself. The magnitude of the coefficient

indicates a strong relationship for both dependent variables.

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Since the study used a sample of more than 30 observations, a t-statistic greater than 2 (or

less than -2) indicated the coefficient was significant with more than 95% confidence. In

Appendix M, the significant coefficients are highlighted in blue in Table 23 and 24.

For the estimated equation for NET_FPI the significant coefficients are for the constant term,

the log of US Industrial Production Index, the log of the SA T-bill rate, the log of real GDP,

the log of trade openness, and the log of the S&P sovereign credit rating.

For the estimated equation for FPI_INFLOWS, the significant coefficients are for the log of

US Industrial Production Index, the log of real GDP, and the log of trade openness.

Table 2: Summary statistics from unrestricted VAR analysis

Table 2 shows the summary statistics of the VAR estimation for NET_FPI and

FPI_INFLOWS.

The R-squared (or coefficient of determination) indicates the overall fit of the regression

equation. This statistic is equal to one if fit is perfect, and to zero when the explanatory

variables have no explanatory power whatsoever. The R-Square statistic shows the

percentage of the total variation in the dependent variable explained by the independent

variables (Lind, Marchal, & Wathen, 2010). In this case, both R-squared values are below

0.5, in other words the chosen explanatory variables explain less than half of the variation in

the dependent variables NET_FPI and FPI_INFLOWS. This suggests that there are further

explanatory variables that could be included in the study.

4.2.6. Pairwise Granger Causality Results

Finally, the analysis ran Granger causality tests on the two sets of variables used in the VAR

model. The Granger test rejects the null hypothesis of no Granger causality if the probability

value is greater than the F-Statistic. (See Appendix N for the pairwise Granger causality tests

results).

NET_FPI FPI_INFLOWS

R-squared 0.485828 0.43169

Adj. R-squared 0.370402 0.31803

Sum sq. resids 0.006069 0.00562

S.E. equation 0.011129 0.01060

F-statistic 4.208986 3.79806

Log likelihood 194.5174 196.84850

Akaike AIC -5.984176 -6.09339

Schwarz SC -5.568922 -5.71275

Mean dependent 0.003582 0.00856

S.D. dependent 0.014025 0.01284

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The Granger tests found NET_FPI and FPI_INFLOWS to be Granger-caused by the US Fed

Fund rate, US Industrial Production Index, Standard & Poor’s sovereign credit rating, and

real GDP.

The tests also found that NET_FPI and FPI_INFLOWS Granger-caused US IPI, Standard &

Poor’s sovereign credit rating, inflation, and real GDP. The analysis rejected the result that

US IPI is Granger-caused by NET_FPI and FPI_INFLOWS based on knowledge of how the

US Industrial Production Index is constructed.

Appendix N shows that the most significant Granger-causes of NET_FPI with a 95%

certainty are the US T-bill differential, the US Industrial Production Index, Real GDP, Trade

Openness and the Standard & Poor sovereign credit rating. For FPI_INFLOWS, the same

Granger-causes are significant except for the Standard & Poor sovereign credit rating and the

US T-bill differential.

Granger causality measures precedence and information content and is not the same as

causality; rather it provides more information about the extent to which variables precede

each other (Levent & Ozgur, 2009). Brooks (2008) advised that causality tests in a VAR

model help explain which variables in the model have statistically significant impacts on the

future values of each variable in the model. However, the causality tests do not indicate

whether the impact is positive or negative.

4.3. Discussion

The objective of this study was to examine the determinants of portfolio investment to South

Africa in the context of pull and push factors. The pull factors were represented by domestic

factors: the SA T-bill rate, inflation, real GDP representing domestic growth, trade openness,

the sovereign credit rating and country risk measuring the riskiness of investing in South

Africa. Meanwhile the push factors were represented by the US fed fund rate, the differential

between the US and SA T-bill rates and the productive performance of industrial countries as

represented by the US Industrial Production Index.

Empirical results showed that some of the variables under study were integrated of order one

while others were stationary. Furthermore, the analysis did not find the variables to be

cointegrated in the long-term, which is contrary to previous empirical studies of capital flows

to South Africa. The suspected reasons for this were discussed in a previous section.

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The unrestricted VAR estimated equation further indicated that net FPI and FPI inflows to

South Africa were more responsive to changes in the US Industrial Production Index, SA T-

bill rate, inflation, real GDP and trade openness compared to the US Fed Fund rate and risk

measures.

Most of the estimated coefficient signs were consistent with prior expectations. As expected

a rise in the lagged value of US Fed Fund Rate and US T-Bill rate negatively affected net FPI

and FPI inflows, as investors realised better returns in their home country and had no need to

look abroad to achieve returns. However, these coefficients were not significant in the

estimated equation.

As expected, a rise in the lagged value of US Industrial Production Index had a positive effect

on net FPI and FPI inflows since higher productivity in developed countries implied more

capital being available to invest in emerging markets. The analysis found the coefficients of

US IPI to be significant for both net FPI and FPI inflows.

Also expected was a rise in the lagged value of South African T-bill rate having a positive

effect on net FPI and FPI inflows, since this means higher returns for foreign investors if they

invested in South African securities. The analysis found the coefficient of the SA T-bill rate

to be significant for net FPI only.

The research expected the lagged value of inflation to have a negative effect on net FPI and

FPI inflows since foreign investors see growing inflation as a sign of macroeconomic

instability. This analysis confirmed this expectation with the coefficient of inflation being

significant in the estimated equations for net FPI and FPI inflows.

The lagged value of real GDP having a negative effect on net FPI and FPI inflows was

unexpected since the research assumed that real GDP as a proxy for domestic growth would

positively influence FPI. The study did not find this to be the case. In addition, the

coefficient of real GDP was significant for both net FPI and FPI inflows. Since a change in

real GDP would result in an immediate effect on FPI flows, using a variable that was not

lagged may have been more accurate and resulted in a positive coefficient.

Next, a rise in trade openness had a negative effect on net FPI and FPI inflows, which the

research did not anticipate. The research expected increasing trade openness, seen as a sign

of increasing liberalisation, to attract FPI. However, the analysis did not find this to be the

case. The coefficient of trade openness was significant for both net FPI and FPI inflows. A

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different measure of trade openness or even a lagged measure of trade openness may have

been more accurate.

An improving sovereign credit rating positively influenced FPI flows whereas an increasing

country risk variable negatively influenced FPI flows. The research had expected increasing

risk (in the form of decreasing sovereign credit ratings or increasing country risk) to deter

FPI. However, the estimation equation found only the coefficient for the S&P sovereign

credit rating to be significant coefficient and only in the case of net FPI.

The findings from the Granger causality test supported the importance of the US Fed Fund

rate and US Industrial Production Index affecting the short-term movement of net FPI to

South Africa. The Granger test also found the Standard & Poor’s sovereign credit rating and

real GDP to be influential in the short term.

In line with the findings of Abdullah et al. (2010), Çulha (2006), and De Vita and Kyaw

(2008) who found that pull factors are more influential in explaining the inflows of capital,

the empirical findings in this study also indicated that pull factors are important in explaining

FPI flows to South Africa. The VAR model indicated that the most important pull factors

were real GDP, the SA T-bill rate (in the case of net FPI), trade openness and the sovereign

credit rating (in the case of net FPI).

The analysis also found the US Industrial Production Index to be the most influential push

factor affecting the flows of portfolio investment to South Africa, which is consistent with the

findings of Abdullah et al. (2010).

The study proposed six hypotheses before analysis of the data began. After conducting the

analysis, the following conclusions were drawn.

Test 1: Foreign interest rates and FPI flows

H0: Foreign interest rates have an effect on FPI flows to South Africa

H1: Foreign interest rates have no effect on FPI flows to South Africa

Conclusion: The study showed the original hypothesis to be true with foreign interest rates

having a slight negative effect on net FPI flows.

Test 2: Domestic interest rates and FPI flows

H0: Domestic interest rates has an effect on FPI flows to South Africa

H1: Domestic interest rates has no effect on FPI flows to South Africa

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Conclusion: The study showed the original hypothesis of domestic interest rates represented

by the South African T-bill rate affecting net FPI flows to be true.

Test 3: Country risk and FPI flows

H0: Country risk ratings have an effect on FPI flows to South Africa

H1: Country risk ratings have no effect on FPI flows to South Africa

Conclusion: The study had inconclusive results with regards country risk ratings. Using risk

represented by the inflation rate proved the original hypothesis of risk affecting net FPI

flows. However using country risk as a measure of risk did not yield a conclusive result due

to the insignificant coefficient.

Test 4: Domestic economic growth and FPI flows

H0: Domestic economic growth has an effect on FPI flows to South Africa

H1: Domestic economic growth has no effect on FPI flows to South Africa

Conclusion: The study disproved the original hypothesis of domestic growth represented by

the lagged value of real GDP affecting net FPI flows.

Test 5: Sovereign credit ratings and FPI flows

H0: Sovereign credit ratings have an effect on FPI flows to South Africa

H1: Sovereign credit ratings have no effect on FPI flows to South Africa

Conclusion: The study had inconclusive results with regards sovereign credit rating changes

affecting net FPI flows. Only the S&P risk rating proved the original hypothesis of an

improving credit rating positively affecting net FPI flows.

Test 6: Trade openness and FPI flows

H0: Trade openness has an effect on FPI flows to South Africa

H1: Trade openness has no effect on FPI flows to South Africa

Conclusion: The study proved the original hypothesis of trade openness affecting net FPI

flows although it was a negative effect instead of the expected positive effect.

4.4. Limitations

The research project was constrained to analysing a single period (1994-2010) of economic

data for South Africa. Furthermore, the analysis confined itself to only studying those

economic variables deemed to have an effect on FPI flows to South Africa as directed by

previous empirical studies.

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The data analysed was restricted to quarterly economic data freely available on publicly

accessible databases (excluding the JP Morgan EMBIG Index), which limited the number of

parameters available for analysis.

Since the South African economy has only been liberalised for a relatively short period, the

data sample was not particularly large. Indices such as the JP Morgan EMBIG only began in

1995 while the sovereign credit ratings began in the last quarter of 1994. A longer period of

data would have been more effective in determining whether a long-term cointegrating

relationship existed between the variables.

Furthermore, the capabilities of the EViews software limited the number of explanatory

variables that could be analysed using a VAR model.

5. CONCLUSION

South Africa’s attractiveness for FPI has increased in recent years due to improved credit

ratings since 1994, macroeconomic stability and relatively high interest rates (CUTS, 2003).

However, South Africa’s economy remains vulnerable to volatile portfolio investment. This

study investigated the role of some of the internal pull factors and external push factors that

could attract or deter FPI to South Africa.

Foreign investment flows are vital for South Africa to finance its future economic growth

given the low domestic savings rate and large current account deficit that currently exists.

Foreign capital flows have previously played an important role in financial crises with

volatile portfolio investment labelled as the culprit. It is therefore crucial to understand the

behaviour and determinants of portfolio investment to mitigate the effect of future financial

crises.

Jenkins and Thomas (2002) proposed that low incomes, fluctuating inflation rates and poor

financial intervention caused low saving rates in a country. They added that it was unlikely

that poorer countries would be able to increase domestic savings to a point that covered

investment requirements. This makes foreign investment essential for economic growth in

emerging and developing countries such as South Africa.

This research used the structural VAR and ECM models in the studies by Wesso (2001),

Çulha (2006), De Vita and Kyaw (2008), Abdullah et al. (2010) and Aron et al. (2010) as the

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foundation for the study. The analysis investigated the determinants of net FPI and FPI

inflows to South Africa using a set of explanatory economic variables, both domestic and

foreign. The previous empirical studies directed the choice of push and pull factors used in

this study.

The unrestricted VAR model used in this study analysed the external push factors of the

US/SA Treasury Bill differential, the US Fed Fund Rate and the US Industrial Production

Index - a proxy for growth in developed countries. The South African internal pull factors

investigated were real GDP, domestic inflation, the SA Treasury bill rate, Standard and

Poor’s sovereign credit rating, country risk measured by the EMBIG stripped yield and trade

openness.

The analysis first tested all the variables for stationarity using the ADF and PP tests provided

by EViews followed by cointegration tests using the Johansen method. The dependent data

series of net FPI and FPI inflows were also tested for structural breaks using the Chow

forecast test and a structural break was found between 2000:Q4 and 2001:Q3 for net FPI.

The analysis calculated first differences of nonstationary variables before an unrestricted

VAR model was run on the stationary variables. The analysis used an appropriate lag length

of one for the VAR models after testing for the minimum Schwarz criterion. Next, EViews

tested the stability of the VAR models using AR Graphs and found the models to be stable.

The estimated equations of the unrestricted VAR models indicated that the internal pull

factors of domestic interest rates (SA T-bill), real GDP, domestic inflation, and trade

openness were the main determinants of FPI (net and inflows) to South Africa. The

coefficients in the estimated equations indicated FPI flows had weaker relationships with

sovereign credit ratings, country risk and the lagged values of FPI (net and inflows). The

analysis also found the most significant push factor affecting FPI to South Africa to be the

US Industrial Production Index.

The study addresses the following research questions.

1. Are external push factors the dominant factors to consider when assessing FPI flows (net

and inflows) to South Africa from 1994 to 2010?

2. Are internal pull factors the dominant factors to consider when assessing FPI flows (net

and inflows) to South Africa from 1994 to 2010?

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Overall, the results of this study indicated that domestic variables or pull factors were more

significant in explaining FPI flows to South Africa answering the proposed research

questions.

This research was in agreement with the findings of Çulha (2006) and Abdullah et al. (2010).

Çulha (2006) found that domestic pull factors , namely real interest rates, budget balance and

current account balance, appeared to be the most significant factors explaining capital inflows

to Turkey between 1992:Q1 and 2005:Q4. Similarly, Abdullah et al. (2010) found that the

domestic pull factors of real GDP, domestic T-bill rate, budget balance, current account

balance and US production Granger-caused capital inflows to Malaysia between 1985:Q1 and

2006:Q4.

The study also agreed with the findings of Ahmed et al. (2005) who found that the US T-bill

rate had a negative effect for total portfolio investment. However, the study disagreed with

Ahmed’s findings that GDP growth positively affected total portfolio investment. This study

found inflation to have a significant effect on FPI whereas Ahmed et al. found inflation

volatility and exchange rate volatility were not significant in explaining total portfolio

investment, equity investment and debt investment.

The study agreed with the findings of Wesso (2001) who found that increased (relative)

returns positively affected capital flows whereas higher relative inflation negatively affected

capital flows. The study also agreed with Fedderke and Liu (2002) who found that political

risk had an impact in attracting capital inflows.

Amaya G and Rowland (2004) pointed out that that the relevance of domestic pull factors

should be less for portfolio flows as contagion; herding behaviour and investor risk appetites

play a large role in explaining the dynamics of portfolio investment. However, the study did

not find this to be the case for South Africa.

To date, South Africa has attempted to encourage FDI flows through restoring and

maintaining macroeconomic stability after the democratic elections in 1994. Lewis (2001)

warned that improved fundamentals promote short-term foreign portfolio investment

however, may not be sufficient to encourage the preferred long-term direct investment.

Rather, South Africa needs to make substantial progress with the concerns identified by both

domestic and international investors. These concerns included levels of crime, skills

shortages, labour relations and the HIV/AIDS epidemic among others (Lewis, 2001).

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Since South Africa is a small open economy, it is critical to maintain a stable domestic

macroeconomic environment in order to attract more foreign capital. Sensible fiscal

management ensures the attractiveness and competitiveness of South Africa as a destination

for foreign investment. Understanding which domestic pull factors are key determinants in

attracting foreign investment helps policy makers make successful decisions.

Frankel, Smit, and Sturzenegger (2008) stated that capital flows were often pro-cyclical in

that global investors invested in developing countries when their home economies were

booming however developed cold feet when problems arose.

Due to the global recession, portfolio investment flows to emerging markets have declined

since 2007. In addition, it is likely that investors will be more risk-averse than in the past.

However, the lack of data since the global recession in 2007 makes it difficult to confirm this.

Moreover, South Africa’s increasingly open financial markets, the dominance of FPI flows,

and the shrinking of FDI and foreign aid, point to present day South Africa being more

vulnerable to FPI surges and reversals than in the past. It is therefore important to understand

the factors that drive FPI to South Africa as well as its volatility so that South African policy

makers can maximise the benefits of capital inflows while managing the attached risks (Aron,

Leape, & Thomas, 2010; Bakardzhieva, Naceur, & Kamar, 2010;Deléchat, Kovanen, &

Wakeman-Linn, 2008).

In conclusion, the challenge for the South African government is to avoid sudden reversals of

foreign investment. Therefore promoting good macroeconomic policies is still important to

encourage stable capital flows. Furthermore, sound fundamentals can help a country absorb

sudden reversals of portfolio investment more effectively than unstable economies.

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6. FUTURE RESEARCH

This study considered a subset of possible macro economic factors that could be determinants

of FPI to South Africa. The RSquared values from the VAR models were less than 0.5

indicating that future research could include more explanatory variables to the model to help

explain the variation in net FPI or FPI inflows.

Furthermore, the study analysed only net FPI and FPI inflows. Future research should

consider the determinants of portfolio inflows versus outflows as well as the division of the

portfolio flows into portfolio equity and portfolio bond flows while considering a larger set of

variables, particularly domestic pull factors, than studied here.

Explanatory factors in future research could include measures of exchange rate over or under-

valuation as per Fedderke and Liu (2002) or stock market indices as a measure of domestic

equity performance as per Aron et al. (2010).

Capital flow studies are reasonably new with investigations mostly carried out in the last two

decades. This leaves scope for future research to build on previous empirical studies.

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7. REFERENCES

7.1. Journals

Abdullah, M. A., Mansor, S. A., & Puah, C.-H. (2010). Determinants of International Capital Flows : The Case

of Malaysia. Global Economy and Finance Journal, 3(1), 31-43.

Adelegan, O. J. (2009). The Derivatives Market in South Africa: Lessons for sub-Saharan African Countries

(IMF Working Paper 09/196). Washington, DC: IMF. Retrieved October 12, 2010, from

http://www.imf.org/external/pubs

Ahlquist, J. S. (2006). Economic Policy, Institutions, and Capital Flows: Portfolio and Direct Investment Flows

in Developing Countries. International Studies Quarterly, 50, 681-704.

Ahmed, F., Arezki, R., & Funke, N. (2005). The Composition of Capital Flows: Is South Africa Different? (IMF

Working Paper 05/40). Washington, DC: IMF. Retrieved October 12, 2010, from


Alfaro, L., Kalemli-Ozcan, S., & Volosovych, V. (2008). Why Doesn’t Capital Flow from Rich to Poor

Countries? An Empirical Investigation. The Review of Economics and Statistics, 90(2), 347-368. doi:

10.1162/rest.90.2.347.

Al-Sakka, R., Gwilym, O. ap. (2009). Heterogeneity of sovereign rating migrations in emerging countries.

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Lind, D. A., Marchal, W. G., & Wathen, S. A. (2010). Statistical Techniques in Business & Economics.

New York: McGraw-Hill/Irwin.

Mohr, P. J., Botha, Z. C., Ingss, E. J., & van Der Merwe, C. (1999). The practical guide to South African

Economic Indicators. Sandton, Johannesburg: Lexicon Publishers (Pty) Ltd.

Murray, M. P. (2006). Econometrics: A Modern Introduction. Boston: Pearson Education, Inc.

Van Der Merwe, E., & Mollentze, S. (2010). Monetary Economics in South Africa. Cape Town: Oxford

University Press Southern Africa (Pty) Ltd.

http://www.unctad.org/sections/dite_fdistat/docs/%20wid_cp_za_en.pdf

http://www.icbs.co.za/internet/Publication.nsf/

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7.3. Websites

EViews Product website (n.d.). EViews 6 Student Version. Retrieved August 14, 2010, from

http://www.EViews.com/EViews6/EViews6s/evstud6.html

Federal Reserve Bank of St Louis Economic Research (n.d.). Federal Reserve Economic Data. Retrieved

November 5, 2010, from http://research.stlouisfed.org/fred2/

Moody’s Product website (n.d.). Ratings Definitions. Retrieved November 22, 2010, from

http://v3.moodys.com/ratings-process/Ratings-Definitions/002002

Standard & Poor’s Product website (n.d.). Credit Ratings Definitions & FAQs. Retrieved November 22, 2010,

from http://www.standardandpoors.com/ratings/definitions-and-faqs/en/us

Fitch Ratings Product website (n.d.). Fitch Ratings Definitions. Retrieved November 22, 2010, from

http://www.fitchratings.com/creditdesk/public/ratings_defintions/ index.cfm?rd_file=intro#cr_rtngs

http://www.eviews.com/eviews6/eviews6s/evstud6.html

http://v3.moodys.com/ratings-process/Ratings-Definitions/002002

http://www.standardandpoors.com/ratings/definitions-and-faqs/en/us

http://www.fitchratings.com/creditdesk/public/ratings_defintions/%20index.cfm?rd_file=intro#cr_rtngs

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8. APPENDICES

APPENDIX A: Methodology Comparison

STUDY COUNTRIES METHOD RESULTS DEPENDANT VAR INDEPENDANT VAR INDEPENDENT VAR CONT.

(Mark P Taylor & Sarno, 1997)

9 Latin American and 9 Asian countries (country panel data) 1988-1992

ADF, Johansen cointegration estimation,

Cointegration reveals both domestic and global factors explain US bond and equity flows to developing countries and represent significant long-run determinants of portfolio flows

U.S. portfolio flows (monthly) - net equity flows and gross bond flows

1) country credit rating 2) black market exchange rate premium 3) ST US nominal interest rate T bill rate 4) and LT US nominal interest rate - gov bond yield

(Dasgupta & Ratha, 1999)

19 countries (country panel data) 1978-1997

ADF, Johansen Cointegration test, Standard OLS regression

Private portfolio flows to a country tended to rise in response to an increase in the current account deficit; a rise in foreign direct investment (FDI) flows; higher per capita income and growth performance.

FDI and non-FDI flows

1) World trade/world GDP 2) Net Non-FDI flows/GDP 3) Growth rate of world GDP 4) Growth rate of Developing country GDP 5) LIBOR (3- month, real) 6) Privatization flows/GDP 7) Net official flows/GDP (lagged) 8) Net FDI flows/GDP 9) Dummy for crises

(Wesso, 2001)

South Africa 1991Q1-2000Q4

ADF test for stationarity, unrestricted vector autoregression (VAR) model,Single equation error correction model

The results show that there is a negative relationship between net capital flows and relatively high domestic inflation rates, and the effect of economic growth is positive in the long run. It is also shown that higher exchange-rate-adjusted

net capital flows as a percentage of nominal gross domestic product (GDP)

1) domestic inflation rate relative to foreign inflation 2) real GDP growth rate 3) ratio between exchange-rate-adjusted South African and US government bond rates 4) government deficit 5) X - see next column

X, which could include a group of variables such as government spending as a percentage of nominal GDP, the

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government bond rates relative to those in the US attract foreign capital, but larger government deficits reduce net capital flows into the country

current-account deficit as a percentage of nominal GDP, credit extension, price- earning ratios of shares, and dummy variables for irregular data

(J. Fedderke, 2001)

South Africa 1960-1995

ADF, Phillips-Peron test, Johansen cointegration test, ARDL model, Bewley Regression

all capital flow measures prove to be sensitive to a range of uncertainty measures, as well as to standard measures of the rate of return on assets

4 measures of capital flows

1) Exchange rate adjusted interest differential. 2) Percentage change of gross domestic product 3) Over-under valuatiuon of the exchange rate in terms of PPP 4) Political Rights Index 5) Political Instability Index

(Bekaert, Harvey, & Lumsdaine, 2002)

20 emerging markets (country panel data) 1980-2000

Structural break point analysis, Vector autoregression, Granger causality tests

“push effect” from world interest rates to capital flow; unexpected shocks in equity flows initially increase returns; when capital leaves, it leaves faster than it came in

net equity flows as a proportion of local market capitalization, log returns and the log dividend yield world interest rate

(Baek, 2006)

5 Latin America countries compared to 4 Asian countries (country panel data)

standard time-series cross-sectional regression models

PI in Asia dominantly pushed by investors’ appetite for risk and other external factors while favorable domestic economic conditions negligible in attracting portfolio. PI in Latin American somewhat pulled by strong economic growth, and also pushed by foreign financial factors but not by the market’s risk appetite.

Net portfolio investment total as a % of GDP; or net changes in liabilities of portfolio investment as a % of GDP

Pull factors: 1) real GDP growth rate (GDP), 2) current account balance as a fraction of GDP (CAB), 3) inflation rate (INF) and 4) volatility of the real exchange rates (RXRV)

Push factors: 1) risk appetite index, 2) world income growth rate (IPG), 3) world stock market performance (RUS), 4) USinterest rate (USI)

(Çulha, 2006)

Turkey 1992Q1-2005Q4

structural vector autoregression (SVAR) time series analysis

pull factors are in general dominant over push factors in determining capital flows into Turkey

ST capital inflows - sum of portfolio and short-term capital

Pull Factors: 1) RIR: Real rate of interest on Turkish Treasury bills. 2) ISE: Istanbul Stock Exchange price index. 3) BD: Budget balance.

Push Factors: 1) USINT: Interest rate on 3-month US Treasury bill. 2)

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flows 4) CA: Current account balance. USIPI: US industrial production index.

(Alfaro, Kalemli-Ozcan, & Volosovych, 2008)

23 developed and 75 developing countries (country panel data) 1970-2000

Correlation, cross-country Least Squares Regression, Monte Carlo simulations

institutional quality is the leading causal variable explaining the “Lucas Paradox.”

inflows of direct and portfolio equity investment per capita

1) logarithm of GDP per capita (PPP) 2) logarithm of the average years of total schooling 3) average institutional quality 4) yearly composite index using International Country Risk Guide’s (ICRG) variables 5) restrictions to capital mobility 6) “distantness,” which is the weighted average of the distances from the capital city of the particular country to the capital cities of the other countries

(De Vita & Kyaw, 2008)

Brazil, Korea, Mexico, the Philippines, and South Africa 1976 - 2001

ADF, Phillips-Peron test, Johansen cointegration test, structural vector autoregression (VAR) model

Model supports the hypothesis that shocks to real variables of economic activity such as foreign output and domestic productivity are the most important forces explaining the variations in capital flows to developing countries.

FDI, Portfolio flows

1) Foreign output - US real GDP growth (log) 2) foreign interest rate - US treasury bill rate 3) Domestic productivity or output is given by real GDP growth in developing countries (log) 4) Domestic money is proxied by the sum of currency outside deposit money banks and demand deposits (log)

(Neumann, Penl, & Tanku, 2009)

22 Developing and Developed countries (country panel data) 1981-2000 Regression

Portfolio flows appear to show little response to capital liberalisation while FDI show increases in volatility

FDI, portfolio (equity and debt), other inflows (relative to GDP), Volatility of each flow: std deviation relative to GDP in 5 year overlapping periods

1) financial liberalization, Kaminsky–Schmukler index, 2) world real interest rates (proxied by the US real federal funds rate) 3) changes in world output growth (proxied by the index of industrial production for the industrial countries), 4) changes in domestic output growth (given by growth in real GDP per capita for each economy).

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(Aron, Leape, & Thomas, 2010)

South Africa 1985Q1-2007Q4

single equation error correction models (single equation, reduced-forms of the related VAR system)

positive effects from annual rates of change of real US GDP and of the real US stock market index and, a corresponding negative effect from the annual change in the real JSE index expressed in dollars, a negative effect from the inflation differential relative to the US and for a long-term bond differential with respect to the US, a positive impact from improvements in the government surplus to GDP ratio for South Africa, a negative effect from changes in a US equity market volatility index, VXO, capturing risk aversion, and a positive effect from the change in an index based on the S&P credit rating

Total flows/GDP Equity flows/GDP

1) Inflation differential - Differential between annual change in log of CPI and annual change in log of trade-weighted foreign prices, 2) T bill interest rate differential 3) 10 year bond interest rate differential 4) Annual change in the log of US industrial production, quarternalised, 5) Annual change in the log of real SA GDP (2000=100) 6) Gov.surplus/GDP, 7) Current account balance/GDP

8) Log of real effective exchange rate. 9) Log of nominal effective exchange rate. 10) Log of bilateral exchange rate with the US dollar. 11) Financial rand premium to the bilateral rate. 12) Real US dollar gold price 13) Real US dollar share price 14) Real SA JSE all share price index, deflating with CPI, 15) Openness, 16) Asset swaps, 17) Forward book CBSO VXO S&P credit rating,

(Abdullah, Mansor, & Puah, 2010)

Malaysia 1985Q1-2006Q4

ADF, Johansen Cointegration test, Error Correction Model, Short Run Granger Causality

real GDP, domestic Treasury bill rate, budget balance, current account balance and US production do Granger cause capital inflows into Malaysia in the short run. The empirical findings in this study show that the pull factors especially budget balance and current account are imperative in explaining inflows of capital into Malaysia.

capital inflows - FDI, FPI and other

Pull factors: 1) real GDP (RGDP), 2) 3-month Treasury bills rate (TBR), budget balance (BB) 3) current account balance (CAB)

Push factors: 1) US Industrial Production Index (USIPI) - proxy for industrial country’s productivity performance

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(Brana & Lahet, 2010)

Thailand, the Philippines, Indonesia, Malaysia (country panel data) 1990-2007

Pesaran t-test for unit roots, OLS regression

Tests show that both push and pull factors are significant. Push factors such as carry trade strategies, global liquidity and contagion factors, seem to be the major determinants of capital inflows into South Africa net capital flows

Pull factors: 1) proxied by sovereign rating Moodys&Fitch

Push factors: 1) excess global liquidity , 2) FED funds rate, 3) rate of growth in industrial production, 4) interest differential with Japan 3-month interbank interest

Table 3: Methodology comparison

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APPENDIX B: Research variables

Variable Definition Source Final

VAR?

DEPENDENT VARIABLES

NET_FPI Total net foreign portfolio investment relative to real GDP IFS YES

FPI_INFLOWS Portfolio investment inflows relative to real GDP IFS YES

INDEPENDENT VARIABLES

PULL FACTORS

LOG_REAL_GDP Log of Real GDP in US dollars IFS YES

CAB

Current Account Balance (millions US $) relative to real GDB

in US Dollars IFS NO

LOG_INFLATION Log of Consumer Price Index (Headline CPI) IFS YES

BUDGET Budget Deficit or Surplus relative to GDP (%) INET NO

LOG_SA_TBILL Log of South African Treasury Bill Rate (% per annum) IFS YES

COUNTRY_RISK Country Risk proxied by EMBI Global stripped spread over

U.S. Treasuries Quarterly Change JP MORGAN YES

LOG_USD_ZAR Log of US Dollar South African Rand exchange rate IFS NO

LOG_GBP_ZAR Log of British Pound South African Rand exchange rate IFS NO

LOG_YEN_ZAR Log of Japanese Yen South Africa Rand exchange rate IFS NO

LOG_YUAN_ZAR Log of Chinese Yuan South African Rand exchange rate IFS NO

LOG_EURO_ZAR Log of Euro South African Rand exchange rate IFS NO

LOG_GDP_PER_CAPITA Log of Real GDP per Capita IFS NO

FITCH Fitch's sovereign credit rating for South Africa expressed as

an index (Aron et al., 2010) NO

MOODYS Moody's Sovereign Credit Rating for South Africa expressed

as an index (Aron et al., 2010) NO

SP Standard & Poor's Sovereign Credit Rating for South Africa

expressed as an index (Aron et al., 2010) YES

LOG_OPENNESS Log of Trade Openness measured by the sum of exports

and imports relative to nominal GDP IFS YES

PUSH FACTORS

LOG_US_TBILL_DIFF Log of differential between SA and US Treasury bill rate (%

per annum) IFS YES

LOG_UK_TBILL_DIFF Log of differential between SA and UK Treasury bill rate (%

per annum) IFS NO

LOG_JAP_TBILL_DIFF Log of differential between SA and Japan Treasury bill rate

(% per annum) IFS NO

LOG_CHI_TBILL_DIFF Log of differential between SA and China Treasury bill rate

(% per annum) IFS NO

LOG_US_FED_RATE Log of US Fed Fund Rate INET YES

LOG_US_IPI Log of US Industrial Production Index St Louis Federal

Reserve Bank YES

Table 4: Dependent and Independent variables under consideration

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APPENDIX C: Sovereign Credit Rating index conversion

Date Rating Outlook

Moodys

October 1994 Baa3 Stable

July 1998 Baa3 Negative

October 1998 Baa3 Stable

February 2000 Baa3 Positive

October 2001 Baa3 Positive

November 2001 Baa2 Stable

February 2003 Baa2 Positive

January 2005 Baa1 Stable

June 2007 Baa1 Positive

July 2009 A3 Stable

S&P

October 1994 BB Stable

November 1995 BB+ Positive

March 1998 BB+ Stable

February 2000 BBB- Stable

November 2002 BBB- Positive

May 2003 BBB Stable

August 2005 BBB+ Stable

November 2008 BBB+ Negative

Fitch

September 1994 BB

May 2000 BB+

June 2000 BBB-

September 2000 BBB- Stable

August 2002 BBB- Positive

May 2003 BBB Stable

October 2004 BBB Positive

August 2005 BBB+ Stable

July 2007 BBB+ Positive

June 2008 BBB+ Stable

November 2008 BBB+ Negative Table 5: Sovereign Credit Ratings for South Africa Source:(Aron et al., 2010)

Fitch’s Sovereign Credit Ratings (Source: company website)

The terms "investment grade" and "speculative grade" have established themselves over time as

shorthand to describe the categories 'AAA' to 'BBB' (investment grade) and 'BB' to 'D' (speculative

grade). The terms "investment grade" and "speculative grade" are market conventions, and do not

imply any recommendation or endorsement of a specific security for investment purposes.

"Investment grade" categories indicate relatively low to moderate credit risk, whereas ratings in the

"speculative" categories either signal a higher level of credit risk or that a default has already

occurred.

Standard & Poor’s Sovereign Credit Ratings (Source: company website)

S&P assigns sovereign risk ratings to the countries that issue debt on global markets. These ratings,

from C (lowest) to AAA (highest), assess the probability that a country will default on its debts.

‘AAA’—Extremely strong capacity to meet financial commitments. Highest Rating.

‘AA’—Very strong capacity to meet financial commitments.

‘A’—Strong capacity to meet financial commitments, but somewhat susceptible to adverse economic

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conditions and changes in circumstances.

‘BBB’—Adequate capacity to meet financial commitments, but more subject to adverse economic

conditions.

‘BBB-‘—Considered lowest investment grade by market participants.

‘BB+’—Considered highest speculative grade by market participants.

‘BB’—Less vulnerable in the near-term but faces major ongoing uncertainties to adverse business,

financial and economic conditions.

‘B’—More vulnerable to adverse business, financial and economic conditions but currently has the

capacity to meet financial commitments.

‘CCC’—Currently vulnerable and dependent on favorable business, financial and economic

conditions to meet financial commitments.

‘CC’—Currently highly vulnerable.

‘C’—Currently highly vulnerable obligations and other defined circumstances.

‘D’—Payment default on financial commitments.

Moody’s Sovereign Credit Ratings (Source: company website)

Moody's appends numerical modifiers 1, 2, and 3 to each generic rating classification from Aa

through Caa.

Aaa: Obligations rated Aaa are judged to be of the highest quality, with minimal credit risk.

Aa: Obligations rated Aa are judged to be of high quality and are subject to very low credit risk.

A: Obligations rated A are considered upper-medium grade and are subject to low credit risk.

Baa: Obligations rated Baa are subject to moderate credit risk. They are considered medium grade

and as such may possess certain speculative characteristics.

Ba: Obligations rated Ba are judged to have speculative elements and are subject to substantial credit

risk.

B: Obligations rated B are considered speculative and are subject to high credit risk.

Caa: Obligations rated Caa are judged to be of poor standing and are subject to very high credit risk.

Ca: Obligations rated Ca are highly speculative and are likely in, or very near, default, with some

prospect of recovery of principal and interest.

C: Obligations rated C are the lowest rated class and are typically in default, with little prospect for

recovery of principal or interest.

Note: Moody’s appends numerical modifiers 1, 2, and 3 to each generic rating classification from Aa

through Caa. The modifier 1 indicates that the obligation ranks in the higher end of its generic rating

category; the modifier 2 indicates a mid-range ranking; and the modifier 3 indicates a ranking in the

lower end of that generic rating category.

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Table 6: Conversion of sovereign credit ratings into an index

Long-

term INDEX

Long-

term INDEX

Long-

term INDEX

Aaa 21 AAA 22 AAA 20 Prime

Aa1 20 AA+ 21 AA+ 19

Aa2 19 AA 20 AA 18

Aa3 18 AA- 19 AA- 17

A1 17 A+ 18 A+ 16

A2 16 A 17 A 15

A3 15 A- 16 A- 14

Baa1 14 BBB+ 15 BBB+ 13

Baa2 13 BBB 14 BBB 12

Baa3 12 BBB- 13 BBB- 11

Ba1 11 BB+ 12 BB+ 10 Non-investment grade

Ba2 10 BB 11 BB 9 speculative

Ba3 9 BB- 10 BB- 8

B1 8 B+ 9 B+ 7

B2 7 B 8 B 6

B3 6 B- 7 B- 5

Caa1 5 CCC+ 6 Substantial risks

Caa2 4 CCC 5 Extremely speculative

Caa3 3 CCC- 4 In default with little

CC 3 prospect for recovery

2 C 2 4

C 1 DDD 3

/ DD 2/ 1 D 1

High grade

Upper medium grade

Lower medium grade

Moody's S&P Fitch

Ca

D In default

Highly speculative

CCC

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APPENDIX D: Descriptive statistics

Variable No. Mean Median Standard

Deviation Minimum Maximum

NET_FPI 66 0.003484 0.002944 0.0134483 -0.061323 0.030734

FPI_INFLOWS 66 0.008312 0.006500 0.012384 -0.027100 0.036700

PULL FACTORS

LOG_REAL_GDP 66 12.31876 12.37206 0.231950 11.68282 12.66701

CAB 66 -0.006126 -0.002801 0.007863 -0.028101 0.004811

LOG_INFLATION 66 4.449015 4.471734 0.283484 3.916115 4.936558

BUDGET 66 -2.404545 -2.500000 3.147816 -8.300000 4.300000

LOG_SA_TBILL 67 2.334511 2.322682 0.290263 1.844774 3.001565

COUNTRY_RISK 63 5.003006 -7.218730 35.77373 -37.66169 112.1016

LOG_USD_ZAR 67 1.840504 1.888735 0.308087 1.234890 2.444605

LOG_GBP_ZAR 67 2.340275 2.434890 0.324450 1.631936 2.799286

LOG_YEN_ZAR 67 2.862666 2.819301 0.304562 2.225213 3.443673

LOG_YUAN_ZAR 67 0.237771 0.212070 0.347702 -0.392657 0.928604

LOG_EURO_ZAR 47 2.162935 2.176948 0.204369 1.827433 2.578805

LOG_GDP_PER_CAPITA 66 8.495788 8.477343 0.262082 7.849901 8.965679

LOG_OPENNESS 66 -0.61779 -0.62029 0.147314 -0.92419 -0.25588

FITCH 67 10.79104 11.00000 2.513824 0.000000 13.00000

MOODYS 67 12.59701 13.00000 2.443667 0.000000 15.00000

SP 67 12.97015 13.00000 2.651301 0.000000 15.00000

PUSH FACTORS

LOG_US_TBILL_DIFF 67 1.917771 1.939748 0.428186 0.766862 2.723727

LOG_UK_TBILL_DIFF 66 1.714588 1.827726 0.460063 0.770108 2.564949

LOG_JAP_TBILL_DIFF 67 2.302833 2.292434 0.289210 1.827287 2.992828

LOG_CHI_TBILL_DIFF 67 1.943366 1.981415 0.371875 1.030690 2.572383

LOG_US_FED_RATE 67 0.934345 1.501853 1.123672 -2.353878 1.880228

LOG_US_IPI 66 4.470617 4.496204 0.104050 4.216726 4.609632

Table 7: Descriptive Statistics

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APPENDIX E: Augmented Dicky-Fuller Unit Root Test Results

(Level, 1st Difference)

Table 8: ADF unit root tests Level

Variable Name t-statistic

Reject Null

Hypothesis


INDEPENDENT

NET_FPI -6.18784 YES – stationary -3.53487 -2.90692 -2.59101

FPI_INFLOWS -5.3641 YES – stationary -3.53487 -2.90692 -2.59101

PULL FACTORS

LOG_REAL_GDP -1.86613 NO -3.53659 -2.90766 -2.5914

CAB -1.59398 NO -3.54406 -2.91086 -2.59309

LOG_INFLATION -0.8912 NO -3.53659 -2.90766 -2.5914

BUDGET -2.34897 NO -3.54406 -2.91086 -2.59309

LOG_SA_TBILL -1.54803 NO -3.53487 -2.90692 -2.59101

COUNTRY_RISK -6.34426 YES – stationary -3.5402 -2.90921 -2.59222

LOG_USD_ZAR -1.94879 NO -3.5332 -2.90621 -2.59063

LOG_GBP_ZAR -2.37962 NO -3.5332 -2.90621 -2.59063

LOG_YEN_ZAR -1.56624 NO -3.5332 -2.90621 -2.59063

LOG_YUAN_ZAR -1.75587 NO -3.53487 -2.90692 -2.59101

LOG_EURO_ZAR -2.65127

NO except 10%

level -3.59246 -2.9314 -2.60394

LOG_GDP_PER_CAPITA -1.91586 NO -3.53659 -2.90766 -2.5914

LOG_OPENNESS -2.24695 NO -3.53659 -2.90766 -2.5914

FITCH -5.01121 YES – stationary -3.5332 -2.90621 -2.59063

MOODYS -6.53758 YES – stationary -3.5332 -2.90621 -2.59063

SP -6.14929 YES – stationary -3.5332 -2.90621 -2.59063

PUSH FACTORS

LOG_US_TBILL_DIFF -2.63105

NO except 10%

level -3.53487 -2.90692 -2.59101

LOG_UK_TBILL_DIFF -2.10035 NO -3.53659 -2.90766 -2.5914

LOG_JAP_TBILL_DIFF -1.6277 NO -3.53487 -2.90692 -2.59101

LOG_CHI_TBILL_DIFF -2.22412 NO -3.5332 -2.90621 -2.59063

LOG_US_FED_RATE -2.38836 NO -3.53836 -2.90842 -2.5918

LOG_US_IPI -2.16356 NO -3.53836 -2.90842 -2.5918

Null hypothesis: variable has a unit root (Augmented Dicky-Fuller test with intercept)

Critical values

LEVEL

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Table 9: ADF unit root tests 1st Difference

Variable Name t-statistic

Reject Null

Hypothesis


INDEPENDENT

NET_FPI -9.49147 YES – stationary -3.53836 -2.90842 -2.5918

FPI_INFLOWS -8.8134 YES – stationary -3.53836 -2.90842 -2.5918

PULL FACTORS

LOG_REAL_GDP -6.16783 YES – stationary -3.53659 -2.90766 -2.5914

CAB -3.29733

NO except 5% and

10% level -3.5421 -2.91002 -2.59265

LOG_INFLATION -4.62841 YES – stationary -3.53659 -2.90766 -2.5914

BUDGET -2.72133 -3.54406 -2.91086 -2.59309

LOG_SA_TBILL -5.21989 YES – stationary -3.53487 -2.90692 -2.59101

COUNTRY_RISK -9.60833 YES – stationary -3.54406 -2.91086 -2.59309

LOG_USD_ZAR -6.23597 YES – stationary -3.53487 -2.90692 -2.59101

LOG_GBP_ZAR -6.35221 YES – stationary -3.53487 -2.90692 -2.59101

LOG_YEN_ZAR -7.15769 YES – stationary -3.53487 -2.90692 -2.59101

LOG_YUAN_ZAR -6.10838 YES – stationary -3.53487 -2.90692 -2.59101

LOG_EURO_ZAR -5.2038 YES – stationary -3.58474 -2.92814 -2.60223

LOG_GDP_PER_CAPITA -6.18409 YES – stationary -3.53659 -2.90766 -2.5914

LOG_OPENNESS -7.38588 YES – stationary -3.53659 -2.90766 -2.5914

FITCH -8.0929 YES – stationary -3.53487 -2.90692 -2.59101

MOODYS -8.13103 YES – stationary -3.53487 -2.90692 -2.59101

SP -8.16663 YES – stationary -3.53487 -2.90692 -2.59101

PUSH FACTORS

LOG_US_TBILL_DIFF -4.51102 YES – stationary -3.53487 -2.90692 -2.59101

LOG_UK_TBILL_DIFF -5.3674 YES – stationary -3.53659 -2.90766 -2.5914

LOG_JAP_TBILL_DIFF -5.29392 YES – stationary -3.53487 -2.90692 -2.59101

LOG_CHI_TBILL_DIFF -7.44109 YES – stationary -3.53487 -2.90692 -2.59101

LOG_US_FED_RATE -6.95186 YES – stationary -3.53487 -2.90692 -2.59101

LOG_US_IPI -3.72969 YES – stationary -3.53836 -2.90842 -2.5918

Null hypothesis: variable has a unit root (Augmented Dicky-Fuller test with intercept)

Critical values

1ST DIFFERENCE

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APPENDIX F: Phillips-Perron Unit Root Test Results

(Level, 1st Difference, 2

nd Difference)

Table 10: PP unit root tests Level

Variable Name t-statistic Reject Null Hypothesis


INDEPENDENT

NET_FPI -1.63075 NO -4.80349 -3.40331 -2.84182

FPI_INFLOWS -1.27383 NO -4.80349 -3.40331 -2.84182

PULL FACTORS

LOG_REAL_GDP -1.18406 NO -4.80349 -3.40331 -2.84182

CAB -3.85164 NO -4.80349 -3.40331 -2.84182

LOG_INFLATION -1.02793 NO -4.80349 -3.40331 -2.84182

BUDGET -1.46654 NO -4.80349 -3.40331 -2.84182

LOG_SA_TBILL -2.02327 NO -4.80349 -3.40331 -2.84182

COUNTRY_RISK -1.70204 NO -4.80349 -3.40331 -2.84182

LOG_USD_ZAR -1.20184 NO -4.80349 -3.40331 -2.84182

LOG_GBP_ZAR 0.841272 NO -4.80349 -3.40331 -2.84182

LOG_YEN_ZAR -2.45727 NO -4.80349 -3.40331 -2.84182

LOG_YUAN_ZAR -1.21301 NO -4.80349 -3.40331 -2.84182

LOG_EURO_ZAR 0.489516 NO -4.80349 -3.40331 -2.84182


LOG_OPENNESS -3.66289 NO -4.80349 -3.40331 -2.84182

FITCH -5.14071 Yes - stationary -3.5332 -2.90621 -2.59063

MOODYS -7.35206 Yes - stationary -3.5332 -2.90621 -2.59063

SP -6.98244 Yes - stationary -3.5332 -2.90621 -2.59063

PUSH FACTORS

LOG_US_TBILL_DIFF -2.16042 NO -3.5332 -2.90621 -2.59063

LOG_UK_TBILL_DIFF -1.89153 NO -3.5332 -2.90621 -2.59063

LOG_JAP_TBILL_DIFF -1.33578 NO -3.5332 -2.90621 -2.59063

LOG_CHI_TBILL_DIFF -2.56124 NO -3.5332 -2.90621 -2.59063

LOG_US_FED_RATE -0.64877 NO -3.5332 -2.90621 -2.59063

LOG_US_IPI -2.62497 NO -3.5332 -2.90621 -2.59063

Null hypothesis: variable has a unit root (Phillips-Perron test with intercept)

Critical values

LEVEL

Copyright UCT


Table 11: PP unit root tests 1st Difference



INDEPENDENT

NET_FPI -4.76851 NO -4.80349 -3.40331 -2.84182

FPI_INFLOWS -3.74693 NO -4.80349 -3.40331 -2.84182

PULL FACTORS

LOG_REAL_GDP -1.63208 NO -4.80349 -3.40331 -2.84182

CAB -7.17234 Yes - stationary -4.80349 -3.40331 -2.84182

LOG_INFLATION -4.29055 NO -4.80349 -3.40331 -2.84182

BUDGET -3.93376 NO -4.80349 -3.40331 -2.84182

LOG_SA_TBILL -1.97778 NO -4.80349 -3.40331 -2.84182

COUNTRY_RISK -2.75914 NO -4.80349 -3.40331 -2.84182

LOG_USD_ZAR -1.68797 NO -4.80349 -3.40331 -2.84182

LOG_GBP_ZAR -3.0519 NO -4.80349 -3.40331 -2.84182

LOG_YEN_ZAR -2.1757 NO -4.80349 -3.40331 -2.84182

LOG_YUAN_ZAR -1.58167 NO -4.80349 -3.40331 -2.84182

LOG_EURO_ZAR -1.95897 NO -4.80349 -3.40331 -2.84182


LOG_OPENNESS -1.36851 NO -4.80349 -3.40331 -2.84182




PUSH FACTORS

LOG_US_TBILL_DIFF -4.5564 Yes - stationary -3.5332 -2.90621 -2.59063

LOG_UK_TBILL_DIFF -5.38622 Yes - stationary -3.5332 -2.90621 -2.59063

LOG_JAP_TBILL_DIFF -5.26238 Yes - stationary -3.5332 -2.90621 -2.59063

LOG_CHI_TBILL_DIFF -7.46128 Yes - stationary -3.5332 -2.90621 -2.59063

LOG_US_FED_RATE -7.20168 Yes - stationary -3.5332 -2.90621 -2.59063

LOG_US_IPI -2.97837 Yes - stationary -3.5332 -2.90621 -2.59063


Critical values

1ST DIFFERENCE

Copyright UCT


Table 12: PP unit root tests 2nd Difference



INDEPENDENT

NET_FPI -7.52595 Yes - stationary -4.80349 -3.40331 -2.84182

FPI_INFLOWS -5.64759 Yes - stationary -4.80349 -3.40331 -2.84182

PULL FACTORS

LOG_REAL_GDP -3.56271 NO -4.80349 -3.40331 -2.84182

CAB -9.61416 Yes - stationary -4.80349 -3.40331 -2.84182

LOG_INFLATION -4.73795 NO -4.80349 -3.40331 -2.84182

BUDGET -4.95078 Yes - stationary -4.80349 -3.40331 -2.84182

LOG_SA_TBILL -1.64227 NO -4.80349 -3.40331 -2.84182

COUNTRY_RISK -5.91211 Yes - stationary -4.80349 -3.40331 -2.84182

LOG_USD_ZAR -3.71669 NO -4.80349 -3.40331 -2.84182

LOG_GBP_ZAR -7.97414 Yes - stationary -4.80349 -3.40331 -2.84182

LOG_YEN_ZAR -4.67134 NO -4.80349 -3.40331 -2.84182

LOG_YUAN_ZAR -3.67835 NO -4.80349 -3.40331 -2.84182

LOG_EURO_ZAR -6.62675 Yes - stationary -4.80349 -3.40331 -2.84182


LOG_OPENNESS -1.6386 NO -4.80349 -3.40331 -2.84182




PUSH FACTORS

LOG_US_TBILL_DIFF -13.1022 Yes - stationary -3.5332 -2.90621 -2.59063

LOG_UK_TBILL_DIFF -18.9406 Yes - stationary -3.5332 -2.90621 -2.59063

LOG_JAP_TBILL_DIFF -22.2445 Yes - stationary -3.5332 -2.90621 -2.59063

LOG_CHI_TBILL_DIFF -33.8418 Yes - stationary -3.5332 -2.90621 -2.59063

LOG_US_FED_RATE -17.8696 Yes - stationary -3.5332 -2.90621 -2.59063

LOG_US_IPI -7.00356 Yes - stationary -3.5332 -2.90621 -2.59063


Critical values

2nd DIFFERENCE

Copyright UCT


APPENDIX G: Comparison of ADF and PP unit root tests

Table 13: Comparison of ADF and PP unit root tests

APPENDIX H: Cointegration test results for FPI_INFLOWS

Trace 0.05 (5% level)

Hypothesized No. of CE(s) Statistic Critical Value

None * 443.125 273.1889

At most 1 * 346.8406 228.2979

At most 2 * 262.251 187.4701

At most 3 * 195.5194 150.5585

At most 4 * 138.3774 117.7082

Variable Name

ADF

level

ADF 1st

Diff

PP

level

PP 1st

Diff

PP 2nd

Diff Compare Results

INDEPENDENT

NET_FPI Y Y N N Y Contradictory Results I(0) or I(2)FPI_INFLOWS Y Y N N Y Contradictory Results I(0) or I(2)

PULL FACTORS

LOG_REAL_GDP N Y N N N Contradictory Results I(1) or I(3)

CAB N Y N Y Y Same Result I(1)

LOG_INFLATION N Y N N N Contradictory Results I(1) or I(3)

BUDGET N Y N N Y Contradictory Results I(1) or I(2)

LOG_SA_TBILL N Y N N N Contradictory Results I(1) or I(3)

COUNTRY_RISK Y Y N N Y Contradictory Results I(1) or I(2)

LOG_USD_ZAR N Y N N N Contradictory Results I(1) or I(3)

LOG_GBP_ZAR N Y N N Y Contradictory Results I(1) or I(2)

LOG_YEN_ZAR N Y N N N Contradictory Results I(1) or I(3)

LOG_YUAN_ZAR N Y N N N Contradictory Results I(1) or I(3)

LOG_EURO_ZAR N Y N N Y Contradictory Results I(1) or I(2)

LOG_GDP_PER_CAPITA N Y N N N Contradictory Results I(1) or I(3)

LOG_OPENNESS N Y N N N Contradictory Results I(1) or I(3)

FITCH Y Y Y Y Y Same Result I(0)

MOODYS Y Y Y Y Y Same Result I(0)SP Y Y Y Y Y Same Result I(0)

PUSH FACTORS

LOG_US_TBILL_DIFF N Y N Y Y Same Result I(1)

LOG_UK_TBILL_DIFF N Y N Y Y Same Result I(1)

LOG_JAP_TBILL_DIFF N Y N Y Y Same Result I(1)

LOG_CHI_TBILL_DIFF N Y N Y Y Same Result I(1)

LOG_US_FED_RATE N Y N Y Y Same Result I(1)LOG_US_IPI N Y N Y Y Same Result I(1)

Date: 11/30/10 Time: 13:18

Sample (adjusted): 1995Q4 2010Q2

Included observations: 59 after adjustments

Lags interval (in first differences): 1 to 2

Series: FPI_INFLOWS LOG_REAL_GDP

LOG_INFLATION LOG_SA_TBILL COUNTRY_RISK SP

LOG_OPENNESS LOG_US_TBILL_DIFF

LOG_US_FED_RATE LOG_US_IPI

Trend assumption: Linear deterministic trend

(restricted)

Copyright UCT

DETERMINANTS OF FPI TO EMERGING MARKETS: SOUTH AFRICA (1994-2010) 107 At most 5 * 101.649 88.8038

At most 6 * 67.90048 63.8761

At most 7 40.3547 42.91525

At most 8 18.83469 25.87211

At most 9 5.28031 12.51798

Table 14: Results of Johansen cointegration Trace test FPI_INFLOWS

Hypothesized Max-Eigen 0.05

No. of CE(s) Eigenvalue Statistic Critical Value

None * 0.80445 96.28441 68.81206

At most 1 * 0.76158 84.58958 62.75215

At most 2 * 0.677304 66.73159 56.70519

At most 3 * 0.620352 57.14207 50.59985

At most 4 0.463407 36.7284 44.4972

At most 5 0.435609 33.74848 38.33101

At most 6 0.373043 27.54578 32.11832

At most 7 0.305627 21.52002 25.82321

At most 8 0.205256 13.55438 19.38704

At most 9 0.085609 5.28031 12.51798

Table 15: Results of cointegration Maximum Eigenvalue test for FPI_INFLOWS

APPENDIX I: Cointegration test results for NET_FPI

Trace 0.05 (5% level)

Hypothesized No. of CE(s) Statistic Critical Value

None * 442.9184 273.1889

At most 1 * 349.6678 228.2979

At most 2 * 257.9828 187.4701

At most 3 * 191.5082 150.5585

At most 4 * 136.7658 117.7082

At most 5 * 102.378 88.8038

At most 6 * 70.53446 63.8761

At most 7 * 43.67826 42.91525

At most 8 20.42353 25.87211

At most 9 5.12968 12.51798

Table 16: Results of Johansen cointegration Trace test NET_FPI

Date: 11/30/10 Time: 13:17

Sample (adjusted): 1995Q4 2010Q2

Included observations: 59 after adjustments

Lags interval (in first differences): 1 to 2

Series: NET_FPI LOG_REAL_GDP LOG_INFLATION

LOG_SA_TBILL COUNTRY_RISK SP LOG_OPENNESS

LOG_US_TBILL_DIFF LOG_US_FED_RATE LOG_US_IPI

Trend assumption: Linear deterministic trend

(restricted)

Copyright UCT


Hypothesized Max-Eigen 0.05

No. of CE(s) Eigenvalue Statistic Critical Value

None * 0.794132 93.2506 68.81206

At most 1 * 0.788596 91.68499 62.75215

At most 2 * 0.675895 66.47464 56.70519

At most 3 * 0.604592 54.74238 50.59985

At most 4 0.441692 34.38781 44.4972

At most 5 0.417089 31.84351 38.33101

At most 6 0.365672 26.8562 32.11832

At most 7 0.325746 23.25474 25.82321

At most 8 0.228345 15.29385 19.38704

At most 9 0.083271 5.12968 12.51798

Table 17: Results of cointegration Maximum Eigenvalue test for NET_FPI

APPENDIX J: Chow Forecast breakpoint tests

Table 18: Chow Forecast test NET_FPI 1994q1 - 2007q1

NET_FPI

Breakpoint F-statistic df

Critical

Value

Structural

break

exists?

Test predictions for

observations

2000q4 3.450876 (27, 13) 2.19935 Y 9/01/2000 to 3/01/2007

2001q1 3.808845 (26, 14) 2.19935 Y 12/01/2000 to 3/01/2007

2001q2 4.244112 (25, 15) 2.19935 Y 3/01/2001 to 3/01/2007

2001q3 4.450011 (24, 16) 2.19935 Y 6/01/2001 to 3/01/2007

2001q4 1.132396 (23, 17) 2.19935 N 9/01/2001 to 3/01/2007

2002q1 0.937341 (22, 18) 2.19935 N 12/01/2001 to 3/01/2007

2002q2 1.036467 (21, 19) 2.19935 N 3/01/2002 to 3/01/2007

2002q3 1.096559 (20, 20) 2.19935 N 6/01/2002 to 3/01/2007

2002q4 1.042695 (19, 21) 2.19935 N 9/01/2002 to 3/01/2007

1994q1 - 2007q1

Copyright UCT


Table 19: Chow Forecast test NET_FPI 2002q1 - 2010q12

Table 20: Chow Forecast test FPI_INFLOWS 1994q1 - 2007q1

NET_FPI


Critical

Value

Structural

break

exists?


observations

2007q1 1.555083 (15, 10) 2.537666 N 12/01/2006 to 6/01/2010

2007q2 1.792294 (14, 11) 2.537666 N 3/01/2007 to 6/01/2010

2007q3 1.510539 (13, 12) 2.537666 N 6/01/2007 to 6/01/2010

2007q4 1.220446 (12, 13) 2.537666 N 9/01/2007 to 6/01/2010

2008q1 1.416126 (11, 14) 2.537666 N 12/01/2007 to 6/01/2010

2008q2 1.401803 (10, 15) 2.537666 N 3/01/2008 to 6/01/2010

2008q3 1.523721 (9, 16) 2.537666 N 6/01/2008 to 6/01/2010

2008q4 1.803582 (8, 17) 2.537666 N 9/01/2008 to 6/01/2010

2009q1 2.010471 (7, 18) 2.537666 N 12/01/2008 to 6/01/2010

2009q2 1.600452 (6, 19) 2.537666 N 3/01/2009 to 6/01/2010

2009q3 0.910531 (5, 20) 2.537666 N 6/01/2009 to 6/01/2010

2002q1 - 2010q2

FPI_INFLOWS


Critical

Value

Structural

break

exists?


observations

2000q4 0.838317 (27, 13) 2.19935 N 9/01/2000 to 3/01/2007

2001q1 0.91824 (26, 14) 2.19935 N 12/01/2000 to 3/01/2007

2001q2 1.020993 (25, 15) 2.19935 N 3/01/2001 to 3/01/2007

2001q3 1.049357 (24, 16) 2.19935 N 6/01/2001 to 3/01/2007

2001q4 0.959536 (23, 17) 2.19935 N 9/01/2001 to 3/01/2007

2002q1 1.013881 (22, 18) 2.19935 N 12/01/2001 to 3/01/2007

2002q2 1.067018 (21, 19) 2.19935 N 3/01/2002 to 3/01/2007

2002q3 1.113548 (20, 20) 2.19935 N 6/01/2002 to 3/01/2007

2002q4 1.175261 (19, 21) 2.19935 N 9/01/2002 to 3/01/2007

2002q1 - 2010q2

Copyright UCT


Table 21: Chow Forecast test FPI_INFLOWS 2002q1 - 2010q2

APPENDIX K: Lag lengths

No. of lags Akaike information criterion Schwarz criterion

NET_FPI as dependent

4 lags -23.5976 -9.0323

3 lags -21.086 -10.17

2 lags -20.0665 -12.736

1 lags -20.9249 -17.1184

0 lags -15.986 -15.643

FPI_INFLOWS as dependent

4 lags -22.9225 -8.3573

3 lags -20.786 -9.8701

2 lags -19.744 -12.4439

1 lags -20.693 -16.8866

0 lags -15.8773 -15.534 Table 22: Unrestricted VAR estimates for lags 0 to 4

FPI_INFLOWS


Critical

Value

Structural

break

exists?


observations

2007q1 0.865323 (15, 10) 2.537666 N 12/01/2006 to 6/01/2010

2007q2 1.013188 (14, 11) 2.537666 N 3/01/2007 to 6/01/2010

2007q3 0.797852 (13, 12) 2.537666 N 6/01/2007 to 6/01/2010

2007q4 0.547753 (12, 13) 2.537666 N 9/01/2007 to 6/01/2010

2008q1 0.617124 (11, 14) 2.537666 N 12/01/2007 to 6/01/2010

2008q2 0.644272 (10, 15) 2.537666 N 3/01/2008 to 6/01/2010

2008q3 0.705965 (9, 16) 2.537666 N 6/01/2008 to 6/01/2010

2008q4 0.836139 (8, 17) 2.537666 N 9/01/2008 to 6/01/2010

2009q1 0.942185 (7, 18) 2.537666 N 12/01/2008 to 6/01/2010

2009q2 1.160265 (6, 19) 2.537666 N 3/01/2009 to 6/01/2010

2009q3 0.823325 (5, 20) 2.537666 N 6/01/2009 to 6/01/2010

2002q1 - 2010q2

Copyright UCT


APPENDIX L: AR Graphs

Figure 48: Results of AR Roots Graph for NET_FPI unrestricted VAR model

Figure 49: Results of AR Roots Graph for FPI_INFLOWS unrestricted VAR model

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Inverse Roots of AR Characteristic Polynomial

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Inverse Roots of AR Characteristic Polynomial

Copyright UCT


APPENDIX M: VAR analysis

Results of NET_FPI unrestricted VAR analysis

Vector Autoregression Estimates Date: 12/08/10 Time:

21:08 Sample (adjusted): 6/01/1995 6/01/2010

Included observations: 61 after adjustments Standard errors in ( ) & t-statistics in [ ]

D_LOG_US_FED_RATE

D_LOG_US_TBILL_DIFF

D_LOG_US_IPI

D_LOG_SA_TBILL

D_LOG_INFLATION

D_LOG_REAL_GDP

D_LOG_OPENNESS SP

COUNTRY_RISK DUMMY NET_FPI

D_LOG_US_FED_RATE(-1) -0.262006 0.068086 -0.013228 0.069625 -0.001986 -0.07433 0.063876 0.170584 3.318813 0.117787 -0.009254

-0.10445 -0.06795 -0.00351 -0.04408 -0.00464 -0.03501 -0.0289 -0.1283 -18.1669 -0.09143 -0.0057

[-2.50850] [ 1.00202] [-3.76690] [ 1.57946] [-0.42798] [-2.12323] [ 2.21048] [ 1.32953] [ 0.18268] [ 1.28824] [-1.62452]

D_LOG_US_TBILL_DIFF(-1) -1.058051 0.68107 -0.005996 0.078904 0.01327 -0.029335 0.081536 0.011813 -20.75062 0.033191 -0.032737

-0.36215 -0.2356 -0.01218 -0.15284 -0.01609 -0.12138 -0.1002 -0.44487 -62.9908 -0.31703 -0.01975

[-2.92156] [ 2.89078] [-0.49243] [ 0.51624] [ 0.82472] [-0.24167] [ 0.81377] [ 0.02655] [-0.32942] [ 0.10470] [-1.65735]

D_LOG_US_IPI(-1) 5.960578 -1.879541 0.822644 -0.380063 -0.012325 2.290707 0.109088 -8.877181 -670.2629 -3.003982 0.672766

-3.18684 -2.07322 -0.10715 -1.34499 -0.14159 -1.06815 -0.88169 -3.91476 -554.301 -2.78974 -0.17382

[ 1.87037] [-0.90658] [ 7.67761] [-0.28258] [-0.08705] [ 2.14456] [ 0.12373] [-2.26762] [-1.20920] [-1.07680] [ 3.87057]

D_LOG_SA_TBILL(-1) 2.096909 -0.630033 0.031378 0.068872 -0.008414 0.173312 -0.14898 -0.108396 55.14441 -0.101384 0.069692

-0.64616 -0.42036 -0.02173 -0.27271 -0.02871 -0.21658 -0.17877 -0.79375 -112.389 -0.56564 -0.03524

[ 3.24519] [-1.49879] [ 1.44431] [ 0.25255] [-0.29306] [ 0.80024] [-0.83336] [-0.13656] [ 0.49066] [-0.17924] [ 1.97749]

D_LOG_INFLATION(-1) -2.81457 3.517748 -0.391266 1.998378 0.375488 0.267478 0.278799 -2.032586 63.6661 2.347463 -0.412807

-3.76321 -2.44818 -0.12653 -1.58824 -0.1672 -1.26133 -1.04115 -4.62278 -654.551 -3.29429 -0.20525

[-0.74792] [ 1.43689] [-3.09235] [ 1.25823] [ 2.24570] [ 0.21206] [ 0.26778] [-0.43969] [ 0.09727] [ 0.71259] [-2.01122]

D_LOG_REAL_GDP(-1) 0.961377 -0.141012 -0.044043 -0.225749 -0.035183 0.146195 -0.24527 -0.218789 145.6924 0.092795 -0.057542

Copyright UCT

DETERMINANTS OF FPI TO EMERGING MARKETS: SOUTH AFRICA (1994-2010) 113 -0.50184 -0.32648 -0.01687 -0.2118 -0.0223 -0.1682 -0.13884 -0.61647 -87.2877 -0.43931 -0.02737

[ 1.91569] [-0.43192] [-2.61025] [-1.06586] [-1.57791] [ 0.86915] [-1.76653] [-0.35491] [ 1.66911] [ 0.21123] [-2.10228]

D_LOG_OPENNESS(-1) 1.110048 0.026601 -0.058586 0.088182 -0.002276 -0.042631 -0.150729 0.284657 86.70506 0.381949 -0.064437

-0.55799 -0.36301 -0.01876 -0.2355 -0.02479 -0.18702 -0.15438 -0.68545 -97.054 -0.48846 -0.03043

[ 1.98936] [ 0.07328] [-3.12275] [ 0.37445] [-0.09182] [-0.22794] [-0.97636] [ 0.41529] [ 0.89337] [ 0.78194] [-2.11728]

SP(-1) -0.026964 -0.000845 -0.001202 0.002789 -2.12E-06 0.013845 -2.61E-05 0.933012 -1.160689 -0.008872 0.003762

-0.02495 -0.01623 -0.00084 -0.01053 -0.00111 -0.00836 -0.0069 -0.03065 -4.34035 -0.02184 -0.00136

[-1.08054] [-0.05206] [-1.43232] [ 0.26479] [-0.00191] [ 1.65534] [-0.00379] [ 30.4370] [-0.26742] [-0.40615] [ 2.76384]

COUNTRY_RISK(-1) -0.001091 0.000492 -4.44E-05 -0.000278 -1.91E-05 -0.000514 -2.40E-05 -0.00181 0.284519 -1.08E-03 -0.00005

-0.00093 -0.00061 -3.10E-05 -0.00039 -4.10E-05 -0.00031 -0.00026 -0.00114 -0.16206 -8.20E-04 -5.10E-05

[-1.17110] [ 0.81152] [-1.41737] [-0.70741] [-0.46064] [-1.64546] [-0.09312] [-1.58101] [ 1.75562] [-1.32795] [-0.94389]

DUMMY(-1) 0.134729 0.139179 -0.001067 0.004863 -0.005045 -0.052122 0.042126 -0.182988 -4.700185 0.668059 -0.00669

-0.12628 -0.08215 -0.00425 -0.0533 -0.00561 -0.04233 -0.03494 -0.15512 -21.9643 -0.11054 -0.00689

[ 1.06691] [ 1.69417] [-0.25135] [ 0.09124] [-0.89922] [-1.23145] [ 1.20575] [-1.17963] [-0.21399] [ 6.04338] [-0.97132]

NET_FPI(-1) 9.130686 5.060029 0.092831 1.868259 0.109344 -1.586189 2.276352 5.059162 595.5929 -1.224053 -0.149461

-2.74225 -1.78399 -0.0922 -1.15735 -0.12184 -0.91913 -0.75869 -3.36863 -476.972 -2.40055 -0.14957

[ 3.32963] [ 2.83636] [ 1.00684] [ 1.61425] [ 0.89743] [-1.72575] [ 3.00037] [ 1.50185] [ 1.24870] [-0.50990] [-0.99929]

C 0.288954 -0.067497 0.022521 -0.079265 0.009195 -0.19441 -0.009506 1.043483 20.16186 0.132311 -0.042117

-0.35746 -0.23255 -0.01202 -0.15086 -0.01588 -0.11981 -0.0989 -0.43911 -62.1741 -0.31292 -0.0195

[ 0.80836] [-0.29025] [ 1.87386] [-0.52541] [ 0.57898] [-1.62264] [-0.09613] [ 2.37638] [ 0.32428] [ 0.42283] [-2.16026]

R-squared 0.687007 0.429865 0.800337 0.276029 0.325655 0.312855 0.28228 0.968312 0.192112 0.58175 0.485828

Adj. R-squared 0.616743 0.301876 0.755515 0.113505 0.174271 0.158597 0.121159 0.961199 0.01075 0.487858 0.370402

Sum sq. resids 2.040017 0.863379 0.002306 0.363371 0.004027 0.229179 0.156151 3.07839 61716.76 1.563293 0.006069

S.E. equation 0.204042 0.13274 0.00686 0.086115 0.009066 0.068389 0.056451 0.250648 35.4898 0.178617 0.011129

F-statistic 9.777543 3.358602 17.85578 1.698391 2.151191 2.028137 1.751979 136.1216 1.059273 6.195904 4.208986

Log likelihood 17.08118 43.30689 224.028 69.70251 207.0241 83.7606 95.46274 4.532 -297.5981 25.19916 194.5174

Akaike AIC -0.166596 -1.026455 -6.951738 -1.891886 -6.394233 -2.352807 -2.736483 0.244852 10.15076 -0.43276 -5.984176

Schwarz SC 0.248658 -6.11E-01 -6.536484 -1.476632 -5.978979 -1.937553 -2.32123 0.660106 10.56601 -0.017506 -5.568922

Mean dependent -0.059408 -1.60E-03 0.00447 -0.011019 0.015172 -0.004377 0.003395 13.45902 4.829061 0.065574 0.003582

S.D. dependent 0.32959 0.158868 0.013875 0.091462 0.009977 0.074557 0.060217 1.272449 35.6821 0.24959 0.014025

Table 23: Results of NET_FPI unrestricted VAR analysis

Copyright UCT


Results of FPI_INFLOWS unrestricted VAR analysis

Vector Autoregression Estimates Date: 12/01/10 Time:

12:18 Sample (adjusted): 6/01/1995 6/01/2010

Included observations: 61 after adjustments Standard errors in ( ) & t-statistics in [ ]

D_LOG_US_FED_RATE

D_LOG_US_TBILL_DIFF

D_LOG_US_IPI

D_LOG_SA_TBILL

D_LOG_INFLATION

D_LOG_REAL_GDP

D_LOG_OPENNESS SP COUNTRY_RISK FPI_INFLOWS

D_LOG_US_FED_RATE(-1) -0.31506 0.05554 -0.01381 0.06360 -0.00265 -0.07454 0.05628 0.13081 4.65849 -0.00280

-0.11248 -0.06954 -0.00343 -0.04270 -0.00453 -0.03375 -0.02947 -0.12809 -16.20610 -0.00532

[-2.80108] [ 0.79860] [-4.02006] [ 1.48955] [-0.58533] [-2.20835] [ 1.90938] [ 1.02122] [ 0.28745] [-0.52621]

D_LOG_US_TBILL_DIFF(-1) -1.21993 0.74523 -0.00853 0.08025 0.00954 -0.08553 0.08951 -0.22353 5.08158 -0.02614

-0.37869 -0.23413 -0.01156 -0.14375 -0.01525 -0.11363 -0.09923 -0.43125 -54.56220 -0.01792

[-3.22147] [ 3.18291] [-0.73764] [ 0.55827] [ 0.62550] [-0.75266] [ 0.90208] [-0.51833] [ 0.09313] [-1.45869]

D_LOG_US_IPI(-1) 8.85949 -2.01690 0.86061 -0.20564 0.03827 2.74769 0.27516 -5.76893 -915.34180 0.43348

-3.09654 -1.91453 -0.09455 -1.17541 -0.12467 -0.92919 -0.81140 -3.52632 -446.15700 -0.14655

[ 2.86109] [-1.05347] [ 9.10228] [-0.17495] [ 0.30695] [ 2.95708] [ 0.33911] [-1.63596] [-2.05162] [ 2.95782]

D_LOG_SA_TBILL(-1) 2.38666 -0.61886 0.03554 0.10101 -0.00220 0.20371 -0.12168 0.22700 43.59936 0.04171

-0.68662 -0.42452 -0.02097 -0.26063 -0.02764 -0.20604 -0.17992 -0.78192 -98.93020 -0.03250

[ 3.47594] [-1.45777] [ 1.69503] [ 0.38756] [-0.07945] [ 0.98872] [-0.67629] [ 0.29031] [ 0.44071] [ 1.28358]

D_LOG_INFLATION(-1) -2.83211 3.02385 -0.37815 2.07539 0.41098 0.50207 0.18202 -0.58381 94.28096 -0.24517

-4.04208 -2.49913 -0.12342 -1.53433 -0.16274 -1.21292 -1.05916 -4.60309 -582.39100 -0.19130

[-0.70066] [ 1.20996] [-3.06390] [ 1.35264] [ 2.52534] [ 0.41394] [ 0.17185] [-0.12683] [ 0.16189] [-1.28160]

D_LOG_REAL_GDP(-1) 1.39159 0.08003 -0.04059 -0.15992 -0.03274 0.08424 -0.14925 -0.06082 155.48420 -0.07127

-0.53271 -0.32936 -0.01627 -0.20221 -0.02145 -0.15985 -0.13959 -0.60665 -76.75420 -0.02521

Copyright UCT


[ 2.61229] [ 0.24299] [-2.49519] [-0.79085] [-1.52641] [ 0.52701] [-1.06921] [-0.10025] [ 2.02574] [-2.82684]

D_LOG_OPENNESS(-1) 1.45282 0.19312 -0.05709 0.11478 -0.00399 -0.08250 -0.08472 0.29419 73.89415 -0.08376

-0.60188 -0.37213 -0.01838 -0.22847 -0.02423 -0.18061 -0.15771 -0.68542 -86.72040 -0.02849

[ 2.41381] [ 0.51895] [-3.10668] [ 0.50237] [-0.16444] [-0.45676] [-0.53720] [ 0.42921] [ 0.85210] [-2.94041]

SP(-1) -0.01036 0.00415 -0.00092 0.00692 0.00051 0.01297 0.00332 0.95508 0.22734 0.00100

-0.02523 -0.01560 -0.00077 -0.00958 -0.00102 -0.00757 -0.00661 -0.02873 -3.63558 -0.00119

[-0.41051] [ 0.26618] [-1.18804] [ 0.72289] [ 0.50470] [ 1.71225] [ 0.50260] [ 33.2377] [ 0.06253] [ 0.83970]

COUNTRY_RISK(-1) -0.00138 0.00022 -0.00004 -0.00033 -0.00002 -0.00041 -0.00012 -0.00167 0.26668 -0.00003

-0.00101 -0.00062 -0.00003 -0.00038 -0.00004 -0.00030 -0.00026 -0.00115 -0.14527 -0.00005

[-1.36562] [ 0.34882] [-1.44899] [-0.85214] [-0.37763] [-1.34567] [-0.45491] [-1.44985] [ 1.83579] [-0.54990]

FPI_INFLOWS(-1) 2.35748 3.89208 0.05275 1.84768 0.12302 -1.96672 1.64808 3.01774 1373.49100 0.16622

-2.85179 -1.76320 -0.08708 -1.08251 -0.11482 -0.85574 -0.74726 -3.24759 -410.89200 -0.13497

[ 0.82667] [ 2.20740] [ 0.60575] [ 1.70685] [ 1.07146] [-2.29825] [ 2.20548] [ 0.92922] [ 3.34271] [ 1.23153]

C 0.07586 -0.13176 0.01813 -0.14520 0.00055 -0.18075 -0.05658 0.69253 -7.61313 -0.00406

-0.35681 -0.22061 -0.01089 -0.13544 -0.01437 -0.10707 -0.09350 -0.40633 -51.40980 -0.01689

[ 0.21262] [-0.59726] [ 1.66438] [-1.07203] [ 0.03824] [-1.68813] [-0.60514] [ 1.70435] [-0.14809] [-0.24058]

R-squared 0.61491 0.36641 0.79740 0.27945 0.31872 0.32237 0.20788 0.96649 0.31792 0.43169

Adj. R-squared 0.53789 0.23969 0.75688 0.13534 0.18246 0.18684 0.04946 0.95979 0.18151 0.31803

Sum sq. resids 2.50995 0.95947 0.00234 0.36165 0.00407 0.22601 0.17234 3.25502 52105.67000 0.00562

S.E. equation 0.22405 0.13853 0.00684 0.08505 0.00902 0.06723 0.05871 0.25515 32.28178 0.01060

F-statistic 7.98389 2.89153 19.67925 1.93915 2.33908 2.37866 1.31221 144.22730 2.33056 3.79806

Log likelihood 10.75845 40.08821 223.58270 69.84700 206.71180 84.18590 92.45458 2.83036 -292.43500 196.84850

Akaike AIC 0.00792 -0.95371 -6.96993 -1.92941 -6.41678 -2.39954 -2.67064 0.26786 9.94869 -6.09339

Schwarz SC 0.38857 -0.57306 -6.58928 -1.54876 -6.03613 -2.01889 -2.28999 0.64851 10.32934 -5.71275

Mean dependent -0.05941 -0.00160 0.00447 -0.01102 0.01517 -0.00438 0.00340 13.45902 4.82906 0.00856

S.D. dependent 0.32959 0.15887 0.01388 0.09146 0.00998 0.07456 0.06022 1.27245 35.68210 0.01284

Table 24: Results of FPI_INFLOWS unrestricted VAR analysis

Copyright UCT


APPENDIX N: Pairwise Granger Causality Tests

Figure 50: Granger Causality tests for NET_FPI

Pairwise Granger Causal i ty Tests

Date: 12/01/10 Time: 17:04

Null Hypothesis: Obs F-Statistic Prob.

Reject

Hypothesis

NET_FPI does not Granger Cause D_LOG_US_FED_RATE 64 51.8521 0.0000 N

D_LOG_US_FED_RATE does not Granger Cause NET_FPI 0.1095 0.7418 Y

Result D_LOG_US_FED_RATE does Granger Cause NET_FPI

NET_FPI does not Granger Cause D_LOG_US_TBILL_DIFF 64 4.0872 0.0476 N

D_LOG_US_TBILL_DIFF does not Granger Cause NET_FPI 3.7924 0.0561 N

No Granger Causality

NET_FPI does not Granger Cause D_LOG_US_IPI 64 0.0567 0.8125 Y

D_LOG_US_IPI does not Granger Cause NET_FPI 7.8156 0.0069 N

Result NET_FPI does Granger Cause D_LOG_US_IPI

NET_FPI does not Granger Cause D_LOG_SA_TBILL 64 3.4257 0.0690 N

D_LOG_SA_TBILL does not Granger Cause NET_FPI 1.6920 0.1982 N


NET_FPI does not Granger Cause D_LOG_INFLATION 64 0.0454 0.8320 Y

D_LOG_INFLATION does not Granger Cause NET_FPI 5.7307 0.0198 N

Result NET_FPI does Granger Cause D_LOG_INFLATION

NET_FPI does not Granger Cause D_LOG_REAL_GDP 64 0.0153 0.9020 Y

D_LOG_REAL_GDP does not Granger Cause NET_FPI 0.0172 0.8960 Y

Result bi-directional Granger causality between NET_FPI and

D_LOG_REAL_GDP

NET_FPI does not Granger Cause D_LOG_OPENNESS 64 8.4720 0.0050 N

D_LOG_OPENNESS does not Granger Cause NET_FPI 2.4416 0.1233 N


NET_FPI does not Granger Cause SP 65 0.1095 0.7419 Y

SP does not Granger Cause NET_FPI 0.2679 0.6066 Y

Result bi-directional Granger causality between NET_FPI and

SP

NET_FPI does not Granger Cause COUNTRY_RISK 61 3.8563 0.0544 N

COUNTRY_RISK does not Granger Cause NET_FPI 1.9909 0.1636 N


NET_FPI does not Granger Cause DUMMY 65 0.6725 0.4153 N

DUMMY does not Granger Cause NET_FPI 4.2226 0.0441 N


Sample: 3/01/1994 9/01/2010

Lags : 1

Copyright UCT


Figure 51: Granger Causality tests for FPI _INFLOWS

Pairwise Granger Causal i ty Tests

Date: 12/01/10 Time: 17:04

Null Hypothesis: Obs F-Statistic Prob.

Reject

Hypothesis

FPI_INFLOWS does not Granger Cause D_LOG_US_FED_RATE 64 15.3162 0.0002 N

D_LOG_US_FED_RATE does not Granger Cause FPI_INFLOWS 0.01085 0.9174 Y

Result D_LOG_US_FED_RATE does Granger Cause FPI_INFLOWS

FPI_INFLOWS does not Granger Cause D_LOG_US_TBILL_DIFF 64 3.35176 0.072 N

D_LOG_US_TBILL_DIFF does not Granger Cause FPI_INFLOWS 3.89936 0.0528 N


FPI_INFLOWS does not Granger Cause D_LOG_US_IPI 64 0.26348 0.6096 Y

D_LOG_US_IPI does not Granger Cause FPI_INFLOWS 5.11449 0.0273 N

Result FPI_INFLOWS does Granger Cause D_LOG_US_IPI

FPI_INFLOWS does not Granger Cause D_LOG_SA_TBILL 64 3.92982 0.0519 N

D_LOG_SA_TBILL does not Granger Cause FPI_INFLOWS 2.16693 0.1461 N


FPI_INFLOWS does not Granger Cause D_LOG_INFLATION 64 0.21904 0.6414 Y

D_LOG_INFLATION does not Granger Cause FPI_INFLOWS 3.91213 0.0525 N

Result FPI_INFLOWS does Granger Cause D_LOG_INFLATION

FPI_INFLOWS does not Granger Cause D_LOG_REAL_GDP 64 1.72916 0.1934 N

D_LOG_REAL_GDP does not Granger Cause FPI_INFLOWS 0.31229 0.5783 Y

Result bi-directional Granger causality between FPI_INFLOWS and

D_LOG_REAL_GDP

FPI_INFLOWS does not Granger Cause D_LOG_OPENNESS 64 6.88681 0.011 N

D_LOG_OPENNESS does not Granger Cause FPI_INFLOWS 4.24984 0.0435 N


FPI_INFLOWS does not Granger Cause SP 65 0.23539 0.6293 Y

SP does not Granger Cause FPI_INFLOWS 0.01719 0.8961 Y

Result bi-directional Granger causality between FPI_INFLOWS and SP

FPI_INFLOWS does not Granger Cause COUNTRY_RISK 61 9.68629 0.0029 N

COUNTRY_RISK does not Granger Cause FPI_INFLOWS 1.01731 0.3173 N


Sample: 3/01/1994 9/01/2010

Lags : 1

Copyright UCT


Figure 52: VAR Granger Causality/Block Exogeneity Walk tests

Date: 12/01/10 Time: 17:05 Date: 12/01/10 Time: 17:05

Sample: 3/01/1994 9/01/2010 Sample: 3/01/1994 9/01/2010

Included observations: 61 Included observations: 61

Dependent variable: NET_FPI Dependent variable: FPI_INFLOWS

Excluded Chi-sq df Prob. Excluded Chi-sq df Prob.

D_LOG_US_FED_RATE 2.639076 1 0.1043 D_LOG_US_FED_RATE 0.2769 1 0.5987

D_LOG_US_TBILL_DIFF 2.746797 1 0.0974 D_LOG_US_TBILL_DIFF 2.127775 1 0.1447

D_LOG_US_IPI 14.98129 1 0.0001 D_LOG_US_IPI 8.748709 1 0.0031

D_LOG_SA_TBILL 3.910467 1 0.048 D_LOG_SA_TBILL 1.647572 1 0.1993

D_LOG_INFLATION 4.045008 1 0.0443 D_LOG_INFLATION 1.642495 1 0.2

D_LOG_REAL_GDP 4.419597 1 0.0355 D_LOG_REAL_GDP 7.991031 1 0.0047

D_LOG_OPENNESS 4.482878 1 0.0342 D_LOG_OPENNESS 8.645987 1 0.0033

SP 7.638833 1 0.0057 SP 0.705102 1 0.4011

COUNTRY_RISK 0.890932 1 0.3452 COUNTRY_RISK 0.302387 1 0.5824

DUMMY 0.943466 1 0.3314

All 40.90491 10 0 All 25.83762 9 0.0022

VAR Granger Causality/Block Exogeneity VAR Granger Causality/Block Exogeneity

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