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International Journal of Applied Economic Studies Vol. 4, Issue 3 June 2016

Available online at http://sijournals.com/IJAE/

ISSN: 2345-5721

1

Financial Market Development and Economic Growth in Nigeria: Evidence from VECM

Approach

Abubakar Hassan

Department of Economics and Development Studies, Federal University Dutse, Jigawa State, Nigeria.

[email protected]

Omoshola D. Babafemi


[email protected]

Aminu Hassan Jakada


[email protected]

Abstract

There has been a series of intensive debate on whether financial market development has the potential to impact

positively on long run economic growth of an economy. Thus, this study empirically examines the impact of financial

market development on economic growth in Nigeria using annual time series data covering the period of 1981-2014. In

achieving this, the study employed the Vector Error Correction Model (VECM) as the econometric methodology. The

empirical results show that overall there is a positive effect of financial market development on economic growth in

Nigeria. Almost, all the financial markets, namely, stock, capital and money market have been found to have a

significant positive impact with the exception of only foreign exchange market having a negative impact on economic

growth. On the basis of the findings of the study, it was recommended that there is a need for a comprehensive financial

reform to overhaul the entire Nigerian financial system so as to boost business and investment activities in the country.

The study also recommended for the establishment of effective legal framework to complement the regulatory and

supervisory institutions as well as directing the financial reform and credit policy of the apex bank towards improving

credit to private sector. Finally, the study recommended a more flexible foreign exchange rate policy and

diversification of the export base of the country to make the forex market a positive contributor to the nation’s real

GDP.

Keywords: Financial markets, Economic growth, Cointegration, Error Correction Mechanism (ECM).

JEL Classification: C50, D53, G20, O43

1. Introduction

There have been a series of intensive debate among various economists, policymakers, academicians and even among

theoretical thinkers about the key roles of financial market in the growth process of an economy. Majority of the

researchers have considered financial market development as an integral part of economic growth. According to

Wachtel (2001), there are four medium through which financial market can support economic growth. First, financial

markets allocate funds from the surplus sectors to the deficit sectors of the economy ; second, they provide incentives

and innovative mechanisms for mobilising savings; third, they reduce the costs associated with evaluation and

implementation of projects through large scale economies and enhance monitoring through corporate governance; and

lastly, they reduce the problems associated with risk in business by ensuring symmetry information , thereby enhance

provision of liquidity and risk sharing.

Financial markets are the markets where stocks, bonds, commodities, foreign exchange and even derivatives are traded

to raise cash for government or businesses, reducing companies’ risks and increasing investors’ wealth (Amadeo, 2013).

The Nigerian financial market comprises the money market, capital market, stock market and foreign exchange market

as well as the institutions and channels that facilitate the smooth intermediation of financial transactions in the

economy. Financial markets are also synonymous with the financial services sector which is made up of the banking

system, other financial institutions, and the securities, insurance and pension sub-sectors (Central Bank of Nigeria,

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Financial Market Development and Economic Growth in Nigeria: Evidence from VECM Approach

Abubakar Hassan, Omoshola D. Babafemi, Aminu Hassan Jakada

2

2009). According to Abiola and Okoduwa (2008), the financial market consists of two major segments, the money

market and the capital market. The money market provides finance on short-term basis to individuals while the capital

market provides finance to businesses, enterprises, corporate bodies, government agencies etc on a medium to long term

basis. They emphasize that money market and capital market plays a key role in the growth of financial system of every

economy and it an important medium of generating funds to finance projects and investments that would lead to

economic growth. Also, Al-faki (2006) contend that the capital market is a network of specialized financial institutions

with series of mechanism, processes and infrastructures that in various ways, facilitate the bringing together of suppliers

and users of medium to long term capital or fund for investment and economic development projects.

Although, several researchers have been conducted studies on the relationship between financial market development

and economic growth in Nigeria; however, the results of the studies are inconclusive in view of the mixed findings

reached, especially on the channels through which financial market development and economic growth are related. The

early study by McKinnon (1973) found that financial market development influences economic growth through a

process of capital accumulation (both domestic and foreign) and technological change, which is aided by incentives

namely, promotion of local saving rate. Berthelemy and Varoudakis (1996) argue that the competitiveness of the

banking sector has a direct effect on the steady state growth rate, through a process of well-functioning educational

system. Meanwhile, Greenwood and Jovanovic (1990) found that the financial system impacts on the economic growth

through the contribution of more productive investments and increased capital allotment.

It is on the basis of these inconclusive results of previous studies that this study is carried out. Therefore, the study seeks

to investigate empirically the role of financial market development on economic growth in Nigeria between the period

1981-2014, with focus on four major types of financial market, namely, capital, stock, money and forex market. The

remaining part of this paper is divided into four sections as follows. Section 2; examined existing literatures in line with

the research topic. Section 3; discusses the methodology to be employed, Section 4; deals with the analyses of the data

obtained and section 5; concludes the paper and provides policy recommendations for the study.

2. Literature Review

Theoretically, economists agreed that financial market development plays a very vital role in economic growth and

development. However, the ongoing empirical research works concerning financial market development, its measures

and impact on economic growth have not reached any consolidative consensus (agreement). Levine et al (2000)

employed instrumental variable procedures and dynamic panel techniques to evaluate whether the exogenous

component of financial intermediary development influences economic growth and whether cross-country differences

in legal and accounting systems explain differences in the level of financial development of a sample of seventy four

(74) countries. Real per capita GDP was used to proxy economic growth and financial intermediaries’ indicators include

liquid liabilities, commercial – central bank and private credit. Using pure cross-sectional data covering 1960-1995,

they found that the exogenous component of financial intermediary development is positively associated with economic

growth. Also, their findings show that cross-country differences in legal and accounting systems account for differences

in financial development. They argued that countries with laws that give a high priority to secured creditors are getting

the full present value of their claims against firms; legal systems that rigorously enforce contracts including government

contracts, and accounting standards that produce high-quality comprehensive and comparable corporate financial

statements tend to have better developed financial intermediaries. Overall, their findings suggest that legal and

accounting reforms that strengthen creditor rights, contract enforcement, and accounting practices can boost financial

development and accelerate economic growth.

Another group of researchers, Beck et al (2000) examine the relationship between financial development and economic

growth and the sources of growth in terms of private saving rates, physical capital accumulation, and total factor

productivity using a pure cross-country instrumental variable procedure and a dynamic panel technique. The primary

measure of financial intermediary development employed was private credit, which measures the value of credits by

financial intermediaries to the private sector divided by GDP, and alternative measures used are liquid liabilities and

commercial-central Bank. The outcome of their study shows that financial intermediaries exert a large and positive

impact on total factor productivity, which translate to overall GDP growth and that the long-run links between financial

intermediary development and both physical capital growth and private savings rates are very weak. They concluded

that higher levels of financial development lead to higher rates of economic growth, and total factor productivity.

However, Dimitris and Efthymios (2004) investigate the long run relationship between financial depth and economic

growth, by employing panel unit root tests and panel cointegration analysis using data from 10 developing countries.

They estimated the long run relationship using fully modified Ordinary Least Square (FMOLS) technique. The

empirical results show that there is a single equilibrium relationship between financial depth, growth and ancillary

variables (investment share and inflation), and that the only causality relation implies unidirectional causality from

International Journal of Applied Economic Studies Vol. 4, Issue 2 April 2016

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financial depth to growth. The empirical results further suggest that there is no short run causality between financial

deepening and economic growth (proxied by real output), thus, the effect is a fairly strong long run relationship between

financial depth and real output. They recommended that to promote growth, attention should be focused on long run

policies such as; the creation of modern financial institutions in the banking sector and stock markets. In conclusion

they state that long run causality runs from financial development to growth and the relationship is significant, and that

there is no evidence of bi-directional causality.

In contrast, Erdal et al (2007) empirically examines the relationship between financial development and economic

growth in Northern Cyprus by using Ordinary Least Square (OLS) Estimation Method. Annual growth rate of GDP was

used as proxy for economic growth and the financial development variables used are; the ratio of domestic investments

to GDP and ratio of deposit to GDP. Employing time series data from 1986-2004, the study found a negligible positive

relationship between financial development and economic growth in Northern Cyprus. Although, Granger causality test

showed that financial development does not cause economic growth, on the other hand there is evidence of causality

from economic growth to the development of financial intermediaries. Their empirical finding shows that there is a

causal relationship between annual growth rate of GDP and both the ratio of domestic investments to GDP and the ratio

of loan to GDP. They concluded that, there is no evidence to support the view that financial development promotes

economic growth in Northern Cyprus. By implication, financial development does not cause economic growth, rather,

economic growth causes financial development.

Recently, Victor and Samuel (2014) assessed the implication of financial sector development on the economic growth

in Nigeria using a time series data from 1990-2011. The variables used in their assessment include Real Gross Domestic

Product which proxies economic growth, and financial development variables- financial deepening which is given as a

ratio of money supply to Gross Domestic Product, liquidity ratio, interest rate and credit to the private sector. By

applying a cointegration technique they found that, on aggregate, financial sector development has significantly

improved the level of economic growth in Nigeria, although, credit to private sector did not play a significant role. They

concluded that further development of the financial sector should be targeted towards private sector credit by making

more funds available to the private sector through reduced interest rate on loans and to remove stringent collateral

conditions on credit facilities. While, Kolapo and Adaramola (2012) applied Johansen cointegration and Granger

causality test to examine the impact of the capital market on the economic growth in Nigeria between 1990-2010.

Economic growth was proxied by Gross Domestic Product (GDP) while the capital market variables considered

include; Market Capitalization (MCAP), Total New Issues (TNI), Value of Transactions (VLT), and Total Listed

Equities and Government Stocks (LEGS). They found that the activities in the capital market impact positively on the

Nigeria economy. They recommended that regulatory authority should formulate policies that would encourage more

private limited liability companies and informal sector operators to access the market for fresh capital and to remove

trading impediments such as high transaction costs to encourage more active trading in stocks.

Finally, a study by Emeka and Aham (2013) examines the financial sector development-economic growth nexus in

Nigeria using data from 1980- 2009. They used ratio of broad money stock to GDP, private sector credit to GDP,

market capitalization to GDP, banks deposit liability to GDP and prime interest rate as proxies for financial sector

development, while real gross domestic product proxy economic growth. They employed cointegration and Error

Correction Mechanism (ECM) and found that there is a positive effect of financial sector development on economic

growth in Nigeria. The financial sector development indicators; stock market capitalization-GDP ratio, interest rate and

broad money stock-GDP ratio are found to stimulate economic growth, however, credits to private sector and financial

sector depth variables are ineffective and fail to accelerate economic growth. They recommended that, to sustain and

enhance the existing relationship between financial sector development and economic growth in Nigeria, there is a need

to adequately deepen the financial system through innovations, adequate and effective regulation and supervision, a

sound and efficient legal system, efficient mobilization of funds and making such funds available for productive

investment, and improved services.

3. Methodology

The study employs the conventional econometric techniques to critically scrutinize the relationship between financial

market development and economic growth in Nigeria within the framework of Vector Error Correction Model (VECM).

The study incorporates various measures of capital market, stock market, money market and foreign exchange market

as proxies for the financial market development and real GDP as a proxy for the economic growth.

Data Sources and Description

This study employs annual time series data from 1981-2014 (33 observations), and the data are sourced from the

Nigerian Stock Exchange (NSE) , Security and Stock Exchange Commission (SEC) Market Bulletins, the Central Bank

of Nigeria Statistical Bulletins, 2014 and World Development Indicators, 2015. The data used include the measure of



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economic growth and financial market development variables which were pulled out from the framework of financial

market development depicted in figure 1.0 below:

Fig. no. 1: A Framework of Financial Market Development

The figure 1.0 above shows that the primary role of financial market is to serve as an intermediary between buyers and

sellers of financial services ( assets & securities) which include equities, bonds, currencies and derivatives. Although,

there are several types of financial market, but in the case of Nigeria we identify four major segments of financial

market as shown in the figure 1.0 above. They include capital, stock, money and forex market and from these markets

we come up with the financial market development variables as indicated in the figure above.

The dependent variable is the growth rate of the real GDP (RGDP) which serves as a proxy of economic growth. The

financial market development variables are classified into four groups-Total Market Capitalizations as percentage of

Financial Intermediaries

Financial Services

Financial Markets

Capital Market

(MCAP)

Stock Market

(SVT)

Capital Formation

Economic Growth

Money Market

(MQM, CPS)

Forex Market

(TR)

External Inflence

(FDI)


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GDP (MCAP) measures the size of the capital market; Stock Value Traded as percentage of GDP (SVT) measures the

liquidity of the stock market; Money and quasi money growth – annual percentage (MQM) and Credit to Private Sector

(CPS) as a ratio of nominal GDP measure the size and outreach of the banking and other related sectors in the economy

(i.e. financial deepening) ; and finally, Total Reserves as a percentage of total external debt (TR) measure the adequacy

of the Foreign Exchange (Forex) Market. These classifications allows us to capture all the four major segments of the

financial market and by so doing, makes our study unique and more comprehensive in comparison with some of the

other previous studies which classified the financial markets into only two i.e. stock/equity market and financial

intermediaries or credit market (King and Levine, 1993; Levine and Zervos, 1998; Levine et al, 2000).

Model Specification

From the forgoing, this study specified the following functional form of the relationship between financial market

development and economic growth by incorporating various proxies which reflect the financial market development as

the explanatory variables and real GDP as the proxy for economic growth to serve as dependent variable:

RGDP= f (MCAP, SVT, MQM, CPS, TR) (1)

where:

RGDP – index of Gross Domestic Product (Real GDP) expressed in constant term

MCAP – Total Market Capitalization as a ratio of nominal GDP

SVT – Stock Value Traded as a ratio of nominal GDP

MQM – Money and quasi money growth – annual percentage

CPS – Credit to Private Sector as a ratio of nominal GDP

TR – Total Reserves as a percentage of total external debt

The equation (1) above can be further transformed into a mathematical model as follows:

RGDP = α + β1MCAP + β2SVT+ β3MQM + β4CPS + β6TR (2)

Equation (2) above shows the mathematical form of the relationship between RGDP (as the dependent variable) and

MCAP, SVT, MQM, CPS, and TR as the independent variables. The theoretical expectation of the model is that all the

coefficients are expected to be positive: β1 >0, β2 >0, β3 > 0, β4 >0, and β5 >0, Furthermore, the equation (2) can be

transformed into an econometric model as follows:

RGDP = α+ β1MCAP + β2SVT+ β3MQM + β4CPS + β6TR + εt (3)

The above equation is the econometric model where the error term (εt) is added to account for the effect of all the

omitted variables not included in the model as well as the influence of any measurement error that might affect the

dependent variable. The error term is assumed to be normally, independently and identically distributed around zero

mean and constant variance [ i.e. εt~ NIID [ (0,1)].

A visual inspection of the time series plots of the variables (see Appendix B) revealed that all the variables are trending

over time, most especially RGDP which exhibits some great elements of random walks with some extreme outliers.

This is because only RGDP is recorded in Naira not as a ratio of nominal GDP or annual percentage as such the natural

log of RGDP is taken in order to secure normality and homoskedasticity. Thus, equation (3) becomes log-linear model

through log transformation as follows:

InRGDP = α + β1MCAP + β2SVT+ β3MQM + β4CPS + β6TR + εt (4)

Estimation Techniques

In order to analyze the econometric model specified above, unit root test based on the Augmented Dickey-Fuller (ADF)

and Philips-Perron (PP) test will be carried out first in order to find out whether the time series variables are stationary

or not. If the time series variables are stationary, this will prevent spurious result and problem of autocorrelation.

However, in most cases time series variables are non-stationary in nature; and thus running a regression analysis on

non-stationary variables will result in spurious results which in turn will lead to a wrong inference by establishing that

the variables are correlated when in reality they are not. Therefore, as would be expected if the variables of concern are

non-stationary at level but found to be stationary (of the same order) after taking first or second difference then a

cointegration test using Johansen Multivariate Cointegration would be applied accordingly. The purpose of the

cointegration test is to check the presence of a long-term equilibrium relationship among the variables in the model. In

other words, if the variables are cointegrated, there is said to be a long-term equilibrium relationship between the



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variables. Therefore, if the variables are integrated of the same order such as I(1) and they are cointegrated based on the

Johansen Multivariate Cointegration test, then Vector Error Correction Model (VECM) specified by Engle and Granger

(1987) will be applied to investigate the relationship between financial market development and economic growth.

The long run model can be formulated into an error correction model (ECM) which integrates short- and long- run

dynamics of the model. An ECM takes the following form:

ttt

p

i

t ECTYY

11

1 (5)

Where is the first difference operator, Yt is a p X 1 vector of variables that are integrated of order one, Yt-1 is one

period lag of the integrated variables, ECTt-1 is one period lag of the residual term (disequilibrium) from the long run

relationship, and ɛt is white noise error term. While α, β and π are the coefficients of VECM, with α representing the

intercept, β represents short run coefficients and π is the long-run coefficient of the one period lag of the disequilibrium

term. Equation (4) can be estimated by the usual Ordinary Least Square (OLS) method since all its terms are I (1) and

therefore standard hypothesis testing using t-ratios and related diagnostic tests can be conducted on the error term.

Theoretically, the coefficient of the one period lag of the disequilibrium term should be negative (i.e. π < 0) and

significant if the disequilibrium is to be corrected in subsequent period and long run equilibrium restored. In this light,

the coefficient of the error term represents the speed of adjustment to the long run equilibrium i.e. it shows by how

much any deviation from the long run relationship is corrected in each period.

4. Empirical Results & Discussion

In this section, the empirical results of the study will be presented and discussed. The empirical results include unit root

test results, cointegration test results and estimated VECM results. The study critically analyses and discusses these

results as well as compares and contrasts them with the previous empirical evidences reviewed in section two.

Unit Root Test Results

In this subsection the stationarity properties of all the variables of interest are examined using time series plot,

Augmented Dickey Fuller (ADF) test and Phillips-Perron (PP) test. From the visual results of the time series plot

contains in Appendix B, it appears that all the variables exhibit clear patterns which suggest presence of non-stationary.

To confirm this suspicion, the two popular conventional unit root (ADF and PP) tests are conducted and their results are

presented in Table 5.1 and Table 5.2, respectively.

Table 5.1: Augmented Dickey Fuller (ADF) Unit Root Test

Va

ria

ble

s

LEVEL

Rem

ark

FIRST DIFFERENCE

Rem

ark

Intercept Trend & Intercept Intercept Trend & Intercept

InRGDP

MCAP

SVT

MQM

CPS

TR

-0.195698

-2.043010

-1.784207

-3.458387

-1.917512

-1.441885

-2.119408

-2.465859

-2.274088

-3.312924

-2.685399

-2.555985

NS^

NS^

NS^

NS^

NS^

NS^

-5.337394*

-5.341846*

-5.241936*

-4.413320*

-5.722589*

-4.367743*

-5.239323*

-5.252580*

-5.194522*

-4.554634*

-5.658288*

-4.278171*

S^

S^

S^

S^

S^

S^

Source: Author’s computation using Eviews9

Note: * denotes significance at 1% level, and S stands for ‘Stationary’, NS stands for ‘Non stationary’, (^) indicates test conducted

with intercept and trend & intercept. The rejection of null hypothesis (series is non stationary) is based on the Mackinnon critical

values (1991).


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Table 5.2: Phillips-Perron (PP) Unit Root Test

Va

ria

ble

s

LEVEL

Rem

ark

FIRST DIFFERENCE

Rem

ark

Intercept Trend & Intercept Intercept Trend & Intercept

InRGDP

MCAP

SVT

MQM

CPS

TR

-0.182272

-2.062496

-1.781755

-2.277499

-1.823547

-1.618013

-2.229381

-2.504381

-2.173600

-1.992160

-2.557302

-2.257261

NS^

NS^

NS^

NS^

NS^

NS^

-5.348326*

-5.833423*

-5.723716*

-6.272375*

-8.960685*

-4.178196*

-5.237902*

-5.717418*

-6.367932*

-8.828720*

-9.950748*

-4.050078*

S^

S^

S^

S^

S^

S^


Note: * denotes significance at 1% level, and S stands for ‘Stationary’, NS stands for ‘Non stationary’, (^) indicates test conducted

with drift and linear trend. The rejection of null hypothesis (series is non stationary) is based on the Mackinnon critical values (1991).

From the tables above, both the ADF and PP test revealed that all the variables are non stationary in level but found to

be stationary at first difference. This is because in level none of the null hypothesis was rejected for all the variables of

interest but at the first difference all the null hypotheses for all the variables were rejected at 1% significant level.

Therefore, this suggest that all the variables are integrated of order one i.e. they are all I(1s). This outcome satisfies the

condition for conducting cointegration test which requires that all the variables must be integrated of the same order

either at first difference or higher difference. Hence, the next sub-section present the results for the cointegration test.

Cointegration Test Results

After identifying the order of integration in levels and at first difference using both ADF and PP test, the results from

the two unit root tests suggested that the long run relationship among the variables may exist. Therefore, it is very

appealing to investigate if the individual variables of interest can actually converge in the long run. To investigate this,

the study employed Johansen Multivariate Cointegration technique. The results of the cointegration test are presented in

table 5.3 and table 5.4 for the Trace criterion and the Maximum Eigenvalue criterion, respectively.

Table 5.3: Johansen Multivariate Cointegration Test (Trace)

Hypotheses (𝝀𝒕𝒓𝒂𝒄𝒆) 0.05

H0 H1 Eigenvalue Statistic Critical Value Prob.*

r=0 r>0 0.866996 132.6436* 95.75366 0.0000

r≤1 r>1 0.649960 68.08766 69.81889 0.0682

r≤2 r>2 0.388673 34.49704 47.85613 0.4748

r≤3 r>3 0.293524 18.74912 29.79707 0.5111

r≤4 r>4 0.128969 7.630216 15.49471 0.5057

r≤5 r>5 0.095495 3.211745 3.841466 0.0731

Trace test indicates 1 cointegrating eqn(s) at the 0.01 level

* denotes rejection of the hypothesis at the 0.01 level

**MacKinnon-Haug-Michelis (1999) p-values



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Table 5.4: Johansen Multivariate Cointegration Test (Maximum Eigenvalue)

Hypotheses (𝝀𝒕𝒓𝒂𝒄𝒆) 0.05 H0 H1 Eigenvalue Statistic Critical Value Prob.*

Hypotheses (𝝀𝒕𝒓𝒂𝒄𝒆) 0.05

r=0 r>0 0.866996 64.55594* 40.07757 0.0000

r≤1 r>1 0.649960 33.59061 33.87687 0.0540

r≤2 r>2 0.388673 15.74792 27.58434 0.6871

r≤3 r>3 0.293524 11.11890 21.13162 0.6354

r≤4 r>4 0.128969 4.418471 14.26460 0.8127

r≤5 r>5 0.095495 3.211745 3.841466 0.0731

Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.01 level

* denotes rejection of the hypothesis at the 0.01 level

**MacKinnon-Haug-Michelis (1999) p-values

From the above tables, it can be observed that both the Trace test and Maximum Eigenvalue test rejected the first null

hypothesis at 1% level of significance, implying presence of one cointegrating equation among the variables.

Specifically, the trace test statistics indicates the existence of one cointegrating equation, and likewise the maximum

Eigenvalue statistics reveals the same at 1% level of significance in both cases. Therefore, we can conclude that there is

long run relationship among the variables. Note that the outcome of our cointegration test is similar to one obtained by

Victor and Samuel (2014) who discovered the existence of cointegration between financial development variables

(including financial deepening, liquidity ratio, interest rate and credit to the private sector) and real GDP in Nigeria

from 1990 to 2011. The next sub-section will therefore present the long run relationship for the variables.

The long-run relationship

From the Johansen Multivariate Cointegration technique, the normalized cointegrating equation is obtained which

shows the long run relationship between real GDP and financial market development variables. The table below

contains the coefficients of the first normalized cointegrating equation.

Table 5.5: Normalized Cointegrating Coefficients

INRGDP MCAP SVT MQM CPS TR

1 0.305733 1.589539 0.054630 -1.133393 -0.009625

Standard Errors

Test Statistics

(0.08348)

[ 3.66234]

(0.14813)

[ 10.7308]

(0.01899)

[ 2.87702]

(0.11561)

[-9.80392]

(0.00458)

[-2.10032]


The table 5.5 above shows the coefficients of the first normalized cointegrating equation with the standard error in

brackets and test statistics in parenthesis. The test statistics (or t-values) are computed by taking the ratio of the

coefficient of each variable by its respective standard error. From the above table, we can observe that all the variables

are highly statistically significant. Using the normalized cointegrating coefficients and their t-values we can now

construct the long run equation as follows:

InRGDP = -3.596 + 0.306MCAP + 1.589SVT + 0.054MQM ̶ 1.133CPS ̶ 0.009TS (6)

(3.662) (10.731) (2.877) (-9.804) (-2.100)

Equation (6) above shows the estimated long run relationship that exists among the variables of interest. As expected,

there is highly statistically significant positive relationship between total market capitalizations (MCAP), stock value

traded (SVT), money and quasi money (MQM) and economic growth (InRGDP). This implies that the three markets,

namely, capital market, stock market and money market play a very vital and positive role in the growth process of the

Nigerian economy. In contrast, credit to private sector (CPS) and total reserves (TR) turn out with an unexpected

negative sign which is contrary to our apiriori expectation, but however the two variables are also highly statistically

significant. Given the fact that our model is log-linear model, we can interpret the coefficients of the long run equation

as long run elasticities. Meaning each coefficient of the variables measures the contribution of each financial market to


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real GDP. For instance, a 1% increase in total market capitalization will result in almost 4% increase in real GDP all

things being equal. By this standard, we can regard stock market contribution as the most significant in the development

of financial market followed by capital market while money market takes the third position and with forex market

taking the last as its contributions to real GDP is even negative. Our findings are very well supported empirically by

some of the previous studies such as Abiola and Okoduwa (2008); Al-faki (2006); Beck et al (2000) and Emeka and

Aham (2013) with the exception of Erdal et al (2007) whose findings are contrary to ours.

Our next empirical analysis would therefore involve the estimation of the Vector Error Correction Model, since the just

concluded cointegration test revealed the presence of long run relationship among the variables.

Vector Error Correction Results

Having met the two conditions (i.e. all the variables of interest are integrated of the same order and found to be

cointegrated) for estimating VECM, this study estimates the Vector Error Correction Model which is presented in table

5.5 below:

Table 5.6: Parsimonious Error Correction Estimates/Short Run Dynamics


The table 5.6 above presents the VECM results which include the parsimonious error correction estimates and the short

run dynamics among the variables as well as the statistical and diagnostic test results. Having found cointegration

among the variables, then it follows that the coefficient of the error correction term (ECT) should be negative and

statistically significant for the disequilibrium to be corrected in subsequent period and long run equilibrium restored.

This condition is met by our model as the coefficient of the one period of the error correction term ECT t-1 is negative (-

0.0657 approximately) and it is highly statistically significant at 1 percent level. The negativity of the ECTt-1 signals that

the system is stable enough and is capable of converging to the long run equilibrium after some shocks/disturbances in

the system. The value -0.066 implies that about 6.6% of the disequilibrium is restored within one year. However, this

means that the speed of adjustment is very sluggish as it will take 15 years on average for long run equilibrium to be

fully restored after some major shocks in the financial market. But given the underdeveloped nature of the financial

systems especially in a developing country like Nigeria, the outcomes of our model make some little sense at least.

Apart from the underdeveloped nature of the financial systems, there is also coexistence of a very huge nonbankable

population alongside huge informal sector operating in Nigeria constraining the ability of the financial markets in

playing vital roles in the growth process of the Nigerian economy.

Variables Coefficient Std. Error t-Statistic Prob.

Constant -0.006992 0.062705 -0.111510 0.9121

ΔInRGDP t-1 1.061019* 0.273209 3.883549 0.0007

ΔMCAP t-1 0.079659* 0.023369 3.408758 0.0023

ΔSVT t-1 0.093512* 0.023293 4.014633 0.0005

ΔMQM t-1 0.000961 0.002505 0.383515 0.7047

ΔCPS t-1 -0.045224*** 0.017691 -2.556291 0.0173

ΔTR t-1 -0.000986** 0.000437 -2.255655 0.0335

ECM t-1 -0.065728* 0.014888 -4.414832 0.0002

STATISTICAL TESTS:

R2 0.568291

Adjusted R2 0.442376

Schwarz criterion -0.519630*

F-statistic 4.513291**

DIAGNOSTIC TESTS:

B-G Serial Correlation LM Test 0.339169

ARCH Test 0.373935

B-P-G -Heteroskedasticity Test 0.987693

Jarque-Bera Test 0.830339



10

The short run coefficients are similar to that of the long run coefficients and are all relatively highly statistically

significant with the exception of MQM which is found to be statistically insignificant. Like the long run relationships,

MCAP, SVT and MQM all possess the correct signs and magnitudes while CPS and TR turn out with the incorrect

signs. As can be expected, InRGDP is the most important determinant of the real GDP in the short run, and then

followed by SVT, MCAP and MQM (just like in the long run equation) while CPS and TR still having negative impact

on real GDP. Overall, the short run coefficients are largely statistically and highly significant with a fairly good fit (

judging from the R2 and the adjusted R2 ) and also the model is overall highly statistically significant according to the F-

test. Note that the optimal lag chosen for this study is based on Schwarz information criterion which is more accurate

for a sample size smaller than 120 (Ivanov & Kilian, 2005). The results of our VECM is almost similar to the empirical

findings of Emeka and Aham (2013) who employed the same VECM technique and found that there is a positive effect

of financial sector development on economic growth in Nigeria. In their studies, the financial sector development

indicators; stock market capitalization-GDP ratio, interest rate and broad money stock-GDP ratio are found to stimulate

economic growth, however, credits to private sector and financial sector depth variables are ineffective and fail to

accelerate economic growth. Clearly, their findings are similar to our findings in this study.

Robustness Check

In addition to the individual test of significance and other statistical tests conducted, the model is further evaluated

based on econometric criterion. Generally, the model is econometrically satisfactory as it was found to be statistically

significant (having highly statistically significant coefficients) and theoretically meaningful (possessing correct signs

and magnitudes). Specifically, the model passed all the three major econometric tests, namely autocorrelation test,

heteroskedasticity test and normally test according to the Breusch-Godfrey Serial Correlation test, Breusch Pagan-

Godfrey Heteroskedasticity test and Jarque-Bera Normality test, respectively. Overall, these additional diagnostic tests

provide strong evidence of the robustness of our model and hence assuring valid inferences to be drawn with high level

of confidence.

5. Conclusion

This paper investigated the influence of financial market development on economic growth in Nigeria during the period

of 1981 to 2014 using a Vector Error Correction Model (VECM). As the study involves time series data, the two

popular conventional unit root (ADF and PP) tests were first applied to uncover the true order of integration of the

variables involved. The results of the two unit root tests revealed that all the variables are integrated of the same order at

first difference (i.e. I [1s]). Thereafter, Johasen Multivariate Cointegration test was performed to find the long run

relationship between the financial market development variables and economic growth. The findings from the

cointegration test showed that there exists positive and highly statistically significant long run relationship between the

three financial market development variables (MCAP, SVT and MQM) and real GDP. While the other two financial

market development variables (CPS and TR) are also found to be highly statistically significant but negatively related to

real GDP which is contrary to the apriori expectation of our model.

In the same vein, the results of the Vector Error Correction Model (VECM) showed the same outcomes and in addition

revealed that the coefficient of the Error Correction Term (ECT) is negative and statistically significant. Overall the

results of our empirical analysis are very robust according to all the three methodical criteria employed. That is the

results are theoretically meaningful, statistically significant and econometrically satisfactory.

From the forgoing, the study concluded that the three financial markets namely, capital, stock and money markets play

very important roles in the growth process of the Nigerian economy, while the foreign exchange market although a very

important segment in the financial market has not been developed to serve the Nigerian economy in achieving economic

growth and development. Thus, the study recommended that there is a need for a comprehensive financial reform to

overhaul the entire Nigerian financial system so as to boost business and investment activities in the country. This will

go a long way in strengthening the three financial markets (stock, capital and money market) that were found to have a

positive impact on economic growth and development of the Nigerian economy. In addition to the financial sector

reform, there is also the need to put in place effective legal framework that would complement the functioning of the

existing supervisory and regulatory financial institutions such as the Central Bank of Nigeria (CBN) and National

Deposit Insurance Corporation (NDIC). Also, the financial reform and credit policy of the apex bank should be geared

toward improving the credit to private sector by making more loanble funds available to the domestic investors through

soft loans bearing attractive interest rate and lessen the stringent collateral security requirements on the private sector

credits. For the foreign exchange market, the monetary authority in particular the CBN should embark on more flexible

foreign exchange rates policy and stop frequent interferences with the forex market forces. There is also the need for

government to diversify the export base of the country away from oil to other key areas such as agriculture, mining and

manufacturing. These will indeed increase the foreign exchange earnings of the country and thereby making forex

market becomes a positive contributor to real GDP and hence accelerates the economic growth rate and development of

the country.


11

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362.

APPENDIX A

Time Series Data

YEARS RGDP MCAP SVT MQM CPS TR

1981 94.32502 15.34181 3.286509 5.899972 9.085659 36.41999

1982 101.0112 15.62870 2.969967 9.545308 10.56154 16.06369

1983 110.0640 16.07058 3.179967 14.02163 10.60114 7.122871

1984 116.2722 17.29213 2.494148 11.60280 10.71876 9.413961

1985 134.5856 16.56882 2.600575 8.992736 9.711546 10.14114

1986 134.6033 17.68634 2.005894 1.953095 11.32769 6.076326

1987 193.1262 14.27749 2.174744 22.41116 10.91669 5.160509

1988 263.2945 14.56802 1.709113 32.91320 10.37865 3.149426

1989 382.2615 12.00824 1.098724 12.92800 7.953513 6.776045

1990 472.6487 11.18315 0.719350 32.70103 7.097808 12.34010

1991 545.6724 13.81803 0.604758 37.38021 7.578257 13.95300

1992 875.3425 12.69358 0.365571 63.26025 6.640023 4.127298

1993 1089.680 15.17315 0.330372 53.75797 11.66560 5.343595

1994 1399.703 16.45296 0.228620 34.49514 10.24676 4.983555

1995 2907.358 9.943428 0.110066 19.41171 6.191351 5.012880

1996 4032.300 8.577088 0.074399 16.17816 5.917133 13.78140

1997 4189.250 9.865254 0.066838 16.03900 7.548060 27.33379

1998 3989.450 12.23592 0.067678 22.31776 8.822173 24.07671

1999 4679.212 13.44141 0.051291 33.12106 9.214550 19.41784

2000 6713.575 13.08479 0.031280 48.06752 7.900013 31.19609

2001 6895.198 18.40878 0.120374 26.37680 11.09412 33.88668

2002 7795.758 19.31773 0.162909 18.82110 11.93590 23.80989

2003 9913.518 19.69958 0.254198 13.51137 11.06101 20.19823

2004 11411.07 18.68203 1.560766 20.67703 12.45864 43.25154

2005 14610.88 18.05444 2.501355 22.60363 12.58233 111.1724

2006 18564.59 20.45781 4.864044 36.35072 12.33864 449.0795

2007 20657.32 24.82123 14.40932 64.41681 17.81495 431.4932

2008 24296.33 32.96055 10.53229 53.36007 28.56968 411.4237

2009 24794.24 37.99238 8.190451 14.54323 36.89332 286.9596

2010 54204.80 20.35787 3.577672 9.968683 18.59843 232.7190

2011 63258.58 19.24243 3.794687 13.14230 16.92602 208.2164

2012 71186.53 19.51969 6.216131 17.41589 20.42738 252.7783

2013 80222.13 18.89581 5.555693 12.44962 19.66704 213.9867

2014 89043.62 19.85602 5.890000 5.350532 19.23662 139.6119


13

APPENDIX B

Time Series Plot

0

20,000

40,000

60,000

80,000

100,000

1985 1990 1995 2000 2005 2010

RGDP

8

12

16

20

24

28

32

36

40

1985 1990 1995 2000 2005 2010

MCAP

0.0

2.5

5.0

7.5

10.0

12.5

15.0

1985 1990 1995 2000 2005 2010

SVT

0

10

20

30

40

50

60

70

1985 1990 1995 2000 2005 2010

MQM

0

10

20

30

40

1985 1990 1995 2000 2005 2010

CPS

0

100

200

300

400

500

1985 1990 1995 2000 2005 2010

TR

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