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American Finance & Banking Review; Vol. 5, No. 1; 2020 
ISSN 2576-1226    E-ISSN 2576-1234 

Published by Centre for Research on Islamic Banking & Finance and Business, USA 
 

     50 
 

 

Do Monetary Policy Instruments Influence Capital Market Returns in Nigeria? 
 
 

Udochukwu Godfrey Ogbonna PhD 
Senior Lecturer 

Department of Management Science 
Rhema University, Aba, Nigeria 

E-mail: kellyogbo2004@yahoo.com 
 

Chukwu Agwu Ejem PhD 
Senior Lecturer 

Department of Banking and Finance 
Abia State University, Uturu, Nigeria 

E-mail: ecjah71@yahoo.com 
 
Abstract 
This study concisely examined the relationship between monetary policy variables and performance of the Nigerian Capital 
Market, analyzed with appropriate econometric tools. After the analysis, the outputs revealed the following; the entire monetary 
policy variables employed only monetary policy rate has significant relationship with the performance of the capital market in 
Nigeria. It was also found that the previous information about the all share index has the capacity to predict future returns in 
capital market in Nigeria. On this note, the researchers are of the opinion to embark on prompt disclosure of the daily all share 
indexes by regulatory authorities, thereby refurbishing the efficiency of the Nigeria Capital Market. It is also suggested to adopt 
alternative means of disclosure apart from the national television stations and national daily newspapers because of our 
technological know-how in Nigeria. 

Keywords: Capital Market, Monetary Policy, ARDL, Nigeria.  
 
1. Introduction 
In broad terms, beside the service function, the Central Bank of Nigeria (CBN) is saddled with monetary and development 
functions qua CBN Decree No. 24 of 1991. On the monetary function the CBN in conjunction with the Federal Ministry of 
Finance is statutorily obliged to advise the Federal Government of Nigeria on suitable monetary policy. The essence of the 
collaboration is to harmonize monetary and fiscal policy to ensure the stability of the economy. On its development function, 
the CBN is a promoter of national economic development through, not limited to promoting the growth of financial 
institutions; power house of every nation and accelerator of a rapidly industrializing economy. An integral part of the financial 
system is the capital market. The capital market makes long term loans available to economic agents. It can also be recalled from 
foundations of finance that, the capital market comprises a number of institutions and intermediaries that facilitate the flow of 
fund to deficit units from the surplus units of the economy. As such developed capital market should be is sufficiently large and 
liquid, with inherent diverse market capitalization and exhibit enough linkage to the performance of the real economic sector 
(Onoh, 2002; Kamal, 2013).  

The analysis and transmission of monetary policy is aimed at facilitating the design of a suitable macroeconomic policy 
framework that triggers sustainable economic growth, domestic stability and external balance. A good number of instruments are 
employed in the design of macroeconomic policy. An indispensable role of monetary authorities qua central bank is to exercise a 
firm control over money supply, generally considered the nerve centre of the economy. It is worthy to note that good control 
over money supply is only feasible through information obtained from monetary analysis. The central bank must however 
sufficiently equip for monetary data collection, analysis, storage and retrieval for use when and where necessary. For investors, 
keeping abreast of changes in the monetary policy would strengthen their knowledge in measuring the intrinsic value of common 
shares, thereby advert mispricing (Onoh, 2007; Echekoba, Ananwude and Lateef, 2017). 

The global economic meltdown of 2007/2008 was a huge challenge to the CBN, hence had to tighten her seat belt on 
appropriate policy measure(s) to tackle such intractable hydra headed global mayhem. The incidence aroused the consciousness 
of finance, economic and political scholars to ascertain the role CBN played amidst the global economic catastrophes that 
averted the doom days prophesied by many analysts. These interventions were prominent in the money and capital markets. 
Economic and financial literatures are awash with empirical studies on the relationship between central bank monetary policy 

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measures and capital market even before, and after the global crisis. Though these studies presented mixed findings.  Most of the 
scholars are of the view that monetary policy variables have significant impact on stock market performance index (Aliyu, 2009; 
Godwin 2010; Ajie and Nenbee, 2010; Okpara, 2010; Aliyu, 2010; Octavio, Martin and George, 2011; Babaak, Navid, 
Shabriar and Rozar, 2012; Ahmed and Igbinovia, 2013; Aliyu, 2013; Yosino, 2014; Nwakoby and Alajekwu, 2016; Onyeke, 
2016; Adekunle, Alalade and Okulenu, 2016; Nkoro and Uko, 2016; Barakat, Elgazzar and Hanafy, 2016; Bissoon, Seetanah, 
BhattuBabajee, Gopy-Ramdhany and Seetah, 2016). Others disagreed that monetary policy variables have significant impact on 
stock market performance (Hasan and Javed, 2009; Abaenewe and Ndugbu, 2012; Rifat, 2015; Onyeke, 2016). 

In the same vein, investors are also eager to know the happenings in the capital market, as well as the relationship 
between monetary policy and capital market. This has a theoretical underpinning; Fama, in his Efficient Market Hypothesis 
(EMH) awakened the consciousness of investors to know all the necessary or relevant information concerning their investment. 
In addition, issues regarding profit maximization and the macroeconomic variables which might lead to an abnormal profit 
(supernatural earning). This succinctly informed investors that stock price really mirror images of reactions from monetary 
policy variables and other macroeconomic variables (Fama, 1981). 

Despite the plethora of empirical studies found on the subject matter, the relationship between monetary policy and 
capital market cannot easily be delineated in practice. This is due to incessant fluctuations in the prices of stock in the Nigeria 
Capital Market notwithstanding the timely interventions of the regulatory authorities. This actually has incited the researchers to 
empirically add to existing body of knowledge by unearthing if capital market performance responds appropriately to the 
monetary policy variables in Nigeria. However, the subsequent sections of this study are decomposed as follows; section two 
takes care of review of theoretical and empirical literature; section three is all about materials and methods; section four analyses 
and interprets the data, whereas section five addresses conclusion and recommendations for further studies. 
 
2. Literature Review 
2.1 Theoretical Literature 
From fundamental approach, there have been controversy among finance and Economic scholars on how monetary policy 
influence stock price. On one side that restrictive monetary policy, it leads to lower stock prices, while on the side expansionary 
monetary policy, it leads to higher stock prices. Researchers also are of the opinion that changes in monetary policy have the 
capacity to predict direction of the stock market. Taking a clue from Friedman’s money demand function, origin of the 
relationship between money supply, interest rates and stock prices; any increase in interest rates qua contraction or restrictive 
monetary policy offers investors the opportunity to raise funds, the equity market exclusively. In order to enhance the demand 
for investors stock, the price will fall to a level that will be attractive to an investor at least in the short run, perhaps through 
public offers (Friedman, 1956; Waud, 1970; Mbutor 2007). 

An important concept underlying investment analysis is the idea of efficient capital market. From the dimension of 
investor, it is necessary to an efficient capital market to ensure that an investor is involved in a fair game, whereas from economic 
point of view, the efficient capital market is the essential vehicle for optimal allocation of resources. The Efficient Market 
Hypothesis (EMH), also called Random Walk Theory (Kendall, 1953), is the consideration that the equity value of a listed 
firm reflects all data regarding the business value. That means the market is efficient when stock prices instantaneously reflect 
supposed-to-know or available information in the market. “Efficient market” was presented in 1965 by Eugene Fama. He 
suggested that stocks always trade at fair value.  This  makes it impossible  for  investors  to  buy  undervalued stocks  or  to  sell  
stocks  at overestimated prices. A market is efficient if prices adjust rapidly and, on average, without bias to new info. Thus, 
there isn’t a reason to believe that prices are excessively high or low (Fama, 1970; Brealey and Myers, 2003; Fisher and Jordan, 
2005; Ibenta, 2005; Ross, Westerfield, Jaffe and Jodan, 2009; Bhalla, 2011). 
 
2.2 Empirical Literature 
As already identified, numerous empirical literature are available on the issue of monetary policy variables and capital market 
performance both in the developed, developing and emerging markets. Previously noted are the mixed outputs in the empirical 
investigations. For instance, Ajie and Nenbee (2010) applied co-integration and Error correction modeling (ECM) to x-ray if 
there is relationship between monetary policy and stock prices in the Nigerian stock exchange market. Money supply and interest 
rate were used as monetary policy variables on stock prices with time series data from 1986 to 2008. The output showed that 
money supply and interest rate have short run significant effect on stock prices. 

Applying the generalized autoregressive conditional heteroscedasticity (GARCH) model, Aliyu (2010) examined the 
impact of inflation on stock market returns and volatility, evidence from Nigeria and Ghana monthly data from 1998 to 2010. 
The result of the study revealed as follows; that in Nigeria that bad news exerts more adverse effect on stock market volatility 
than good news of the same magnitude; while a strong opposite case holds for Ghana. Again, inflation rate has significant effect 
on stock market volatility in the Nigeria and Ghana.  



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Employing Augmented Dickey Fuller Unit Root Test and Cointegration Test, Vector Error Correction Model and 
the Forecast Error Decomposition Analysis. Okpara (2010) investigated the effect of monetary policy on the Nigerian stock 
market returns between 1985 and 2006 using  the Two Stage Least Squared Method on a set of simultaneous equations on 
stock market return proxied by all share index with monetary policy variables (Treasury bill rate, interest rate and monetary 
policy rate). The study revealed that stock market returns in Nigeria respond significantly on monetary policy. 

With threshold regression model, cointegration test and an error correction model, Chen & Wu (2013) looked at the 
relationship between interest rate and stock prices. Threshold regression model found that before and after a nonlinear 
relationship exists between interest rates and the stock index, also that stock index prices are significantly and positively related 
to the interest rates. The cointegration test and an error correction model revealed that there is a significant cointegration 
relationship before and after central banks cut interest rates. 

Rifat (2015) engaged Johansen Co integration test, Vector Error Correction, and Vector Autoregressive Model to 
examine the relationship between monetary policy tools (inflation, real output, money supply, exchange rate) and stock market 
returns in Bangladesh.  The study showed that there is no significant relationship between monetary policy variables and stock 
market returns. 

Barakat, Elgazzar and Hanafy (2016) used data spanning from January 1998 to January 2014 to investigate the 
relationship between the stock market and macroeconomic factors in two emerging economies of Egypt and Tunisia employing 
ADF, Johansson cointegration, VAR and granger causality tests. The results of the study found a causal relationship in Egypt 
between market index and consumer price index (CPI), exchange rate, money supply, and interest rate. For Tunisia the same 
result was applicable except for CPI that had no causal relationship with the market index. Furthermore, it was found that the 
four macroeconomic variables are co-integrated with the stock market in both countries.  

Employing simple and multiple regressions, Adekunle etal (2016) investigated effect of macroeconomic pricing 
variables (interest rate, inflation rate, and exchange rate) on capital market growth (all share index). The result found that 
interest rates have an unfavorable effect on capital market growth. Also that inflation rate and exchange rate insignificantly exert 
on capital market. A negative relationship was also found between interest rate and All Share Index.  

Onyeke (2016) investigated the impact of monetary policy on stock returns in Nigeria over a monthly time period 
covering January 2003 to June 2014. The explanatory variables employed are consumer price index, inter-bank rate, open buy-
back, Treasury bill rate, and exchange rate while the all share index is the dependent variable. The dynamic interactions among 
the variables are based on variance decompositions and impulse response functions generated from the VAR. The estimated 
results revealed that monetary policy variables did not have a significant impact on the prices of stock in Nigerian equity market. 
Using Johansen co-integration, OLS and granger causality tests, Nwakoby and Alajekwu (2016) tried to unearth the  effect of 
monetary policies(monetary policy rate, Treasury bill rate, lending interest rate, liquidity ratio and deposit rate) on stock market 
Volume performance (All Share Index) in Nigeria from 1986 and 2013. First the result revealed that there is long run 
relationship between monetary policy and stock market performance. Again, monetary policy was found to influence the stock 
market, though causality test found that monetary policy cannot influence stock market performance, instead stock market 
performance has influenced the direction of monetary policy via lending and deposit rates.  

With GARCH (1, 1) models, Nkoro and Uko (2016) examined the relationship between exchange rate and inflation 
volatility and stock prices volatility in Nigeria and found a negative relationship between stock market prices volatility and 
exchange rate and inflation volatility in Nigeria.  

Bissoon etal (2016) studied the impact of monetary policies on stock markets with a panel data from five open 
countries from 2004 to 2014. In this study, interest rate and money supply stood for monetary policy regressed on Mauritius, 
London, Trinidad, Australia and Japan stock markets. The study employed random effect model for the panel regression and 
panel vector error correction model to know if short term and long term relationship exist between the variables. Thereafter, it 
was found that a negative relationship exist between interest rate and stock return and a direct link between money supply and 
stock return. The output showed that both in the short run and long run monetary variables explain changes in stock return.  
Applying mainly the Autoregressive Distributive Lag (ARDL), Echekoba, Ananwude and Lateef (2017) examined effect of 
monetary policy tools on performance of the Nigerian capital market. The monetary policy engaged are monetary policy rate, 
cash reserve ratio, liquidity ratio and loan to deposit ratio on the performance of the Nigerian capital market. The results of the 
analysis revealed that Nigerian capital market performance is not significantly affected by monetary policy announcement by the 
Central Bank of Nigeria instead monetary policy rate that is significantly influenced by performance of the capital market. 

Echekoba, Okaro, Ananwude and Akuesodo (2018) employed Ordinary Least Square (OLS) regression technique and 
causality analysis to investigate the effect of monetary policy on the performance of Nigerian capital market with time series data 
from 1986 to 2016. It was found that monetary policy rate negatively and significantly relate with capital market performance, 
whereas cash reserve ratio has positive and significant impact on performance of the capital market. 

Nwokoye and Otu (2018) used Cointegration and vector error correction modelling (VECM) to examine if monetary 
authorities can stabilize the stock market and reduce its volatility culminating to examination of impact of monetary policy on 



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the development of the stock market in Nigeria. The results found as follows; the cointegration test showed that there exist long 
run relationships among the variables of the model. VECM saw that monetary policy, through the growth rate of money supply 
has impacted positively and significantly on the development of the stock market in Nigeria. Again, prime lending rate has a 
negative impact on the development of the stock market in Nigeria. 

Osakwe and Chukwunulu (2019) used OLS regression technique to unravel if monetary policy (money supply, 
interest rate and exchange rate) influences stock market performance in Nigeria from 1986 to 2015. The results of the study 
indicated that money supply and exchange rate have positive and significant effect on stock market price movement whereas 
Interest rate has insignificant negative effect on stock market price movement. 
 
3. Method of Study 
3.1 Sample Data Collection 
This study obtained annual data from the Central Bank of Nigeria (CBN), Nigerian Stock Exchange (NSE) Annual Report 
Books from 1989 to 2018 for Capital Market return as performance index proxied by All Share Index (ASI) as dependent 
variables, while the independent variable is Monetary Policy variables (Monetary Policy Rates (MPR), Cash Reserve Ratio 
(CRR), Liquidity Ratio (LDR) and Savings Deposit Rate (SDR)).  
 
3.2 Trend Analysis of Data 
This estimation of the model specified in this study started with trend analysis of data. The time series plot of the data is shown 
in figure I below. The figures below indicated that all the variables recorded period of peaks and troughs suggesting non-
stationarity of the variables as expected.  

0

10,000

20,000

30,000

40,000

50,000

1990 1995 2000 2005 2010 2015

ASI

5

10

15

20

25

30

1990 1995 2000 2005 2010 2015

MPR

0

4

8

12

16

20

24

28

1990 1995 2000 2005 2010 2015

CRR

20

30

40

50

60

70

1990 1995 2000 2005 2010 2015

LDR

0

4

8

12

16

20

1990 1995 2000 2005 2010 2015

SDR

 

Figure 1. Trend Analysis of ASI, MPR, CRR, LDR and SDR 



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3.3 Techniques 
To examine presence of multicollinearity, the correlation matrix is engaged in this study and Ordinary Least Square (OLS) 
technique to examine and determine the global utility of the specified model. To determine the stationarity of the data obtained, 
the Augmented Dickey Fuller (ADF) unit root test is employed. To estimate the model, the Autoregressive Distributive Lags 
(ARDL) is engaged. 

3.4 Model Specification 
Commencing with functional specification as seen below;  

Stock Market Performance = f (Monetary Policy Variables)     (1) 

All Share Index= f (Monetary Policy Rates, Cash Reserve Ratio, Liquidity Ratio,         
Savings Deposit Rate)          (2) 

ASI = f (MPR, CRR, LDR, SDR)          (3) 

Next is the explicit form; 

ASI = α0+ α1ASI t-1 + α 2MPR + α 3MPR t-1+ α 4CRR + α 5CRR t-1 + α 6LDR + α 7LDR t-1 + α 8SDR +   

  α 9SDR t-1 + et-1                                         (4) 

ASI = α0+α1nlogASIt-1+α2nlogMPR+α3nlogMPRt-1+α4nlogCRR+α5nlogCRRt-1+α6nlogLDR+  α7nlogLDR t-1 + α8nlogSDR 

+ α 9nlogSDR t-1 + et-1             (5) 

Where et-1 are stochastic terms  

3.5 Operational form (Apriori Expectation) 
α1, α2, α3, and α4 are coefficient of MPR, CRR, LDR, and SDR respectively. It is expected that monetary variables 
influence capital market returns both ways. 

4. Results and Analysis 
Next is descriptive statistical analysis.  
4.1 Description of Variables 
Table 1 below is a summary of statistics that describe the distributional features of all the data.  The capital market has average 
price index of 17208.87, with 13.88%, 9.37%, 40.12% and 6.71% of MPR, CRR, LDR and SDR respectively. ASI, LDR and 
SDR exhibited Kurtosis lower than 3 indicating platykurtic distributions while MPR and CRR showed Kurtosis greater than 3, 
suggesting a leptokurtic distribution. At 5% significant level Jarque-Bera P-value for ASI, CRR and LDR are 0.3937, 0.0531 
and 0.4173 respectively; an evidence of normal distribution, whereas MPR and SDR recorded 0.0319 and 0.0460 indicating 
abnormal distribution.   

           Table 1. Descriptive Statistics for ASI, MPR, CRR, LDR and SDR 
 

 ASI MPR CRR LDR SDR 

 Mean  17208.87  13.88133  9.373333  40.12667  6.711333 

 Median  17702.59  13.50000  8.150000  40.10000  4.140000 

 Maximum  45908.88  26.00000  27.50000  64.10000  18.80000 

 Minimum  325.3000  6.130000  1.000000  25.00000  1.410000 

 Std. Dev.  13692.81  3.865720  6.891621  10.79955  5.318112 

 Skewness  0.459073  0.711373  1.067086  0.425798  1.092443 

 Kurtosis  2.194921  4.866871  3.375811  2.179709  2.613412 

 Jarque-Bera  1.863932  6.886764  5.869901  1.747618  6.153972 

 Probability  0.393779  0.031956  0.053133  0.417359  0.046098 

 Sum  516266.0  416.4400  281.2000  1203.800  201.3400 

 Sum Sq. Dev.  5.44E+09  433.3699  1377.339  3382.279  820.1871 

Authors’ computation output using E-view 10. 



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4.2 Global Utility Examination and Determination 
In finametric analysis, determination of global utility or usefulness of the specified models gives a research confidence to making 
inference that can be referred for policy making. To achieve this, the researchers used correlation matrix and Ordinary Least 
Square (OLS) as shown below. 

4.2.1 Multicolinearity Test 
Table 2 below depicts the correlation matrix of the variables employed. The correlations between ASI, MPR, CRR, LDR and 
SDR are from -0.730474 to 0.635569; suggesting no linear correlation. Hence, multicollinearity is not a concern in this model. 

      Table 2. Correlation Matrix 

Variables ASI MPR CRR LDR SDR 

ASI 1.000000 -0.601275 0.357605 -0.402143 -0.730474 

MPR -0.601275 1.000000 0.072084 0.415718 0.635569 

CRR 0.357605 0.072084 1.000000 -0.177207 -0.376283 

LDR -0.402143 0.415718 -0.177207 1.000000 0.118386 

SDR -0.730474 0.635569 -0.376283 0.118386 1.000000 

 
Authors’ computation output using E-view 10. 

 
4.2.2 Ordinary Least Square (OLS) Method 
Table 3 is an output of the Ordinary Least Square (OLS) estimate for the relationship between monetary policy and capital 
market return. Though other indexes are satisfied both Durbin-Watson statistics is 0.970034, suggesting autocorrelation is 
found. This is an uncomfortable posture for further analysis and policy formulation, therefore ignored and subjected to 
stationarity test to choose an appropriate method for model estimation. 

Table 3.  Ordinary Least Square (OLS) method 

Dependent Variable: LNASI   

Method: Least Squares   

Variable Coefficient Std. Error t-Statistic Prob.   

LNMPR -1.992927 0.793792 -2.510642 0.0189 

LNCRR 0.626251 0.194299 3.223131 0.0035 

LNLDR 0.425481 0.499423 0.851944 0.4023 

LNSDR -1.061547 0.269908 -3.932994 0.0006 

C 13.28905 1.699087 7.821284 0.0000 

R-squared 0.835014 F-statistic 31.63196 

Adjusted R-squared 0.808616     Prob(F-statistic) 0.000000 

 Durbin-Watson stat 0.970034      

 
Authors’ computation output using E-view 10. 

 
4.3 Stationarity/Unit Root Test 
Here, the researchers employed Augmented Dickey Fuller (ADF) unit root test as depicted below; 

Table 4 below shows the stationary test for ASI, MPR, CRR, LDR and SDR variables. The results show MPR, CRR, LDR 
and SDR are difference once to be stationary or integrated at order one, while ASI is stationary at level. The variables have 
different orders of integration, justifying the adoption of ARDL technique. 

         

 

 



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          Table 4. ADF Unit Test   

Variables Lag 
SCI 

ADF Statistic  CRITICAL 
VALUES 

Remarks 

   With Prob. Value 5% 10% Stationarity  

LnASI 7 -3.280648 (0.0253) -2.967767 -2.622989 @1(0) 

LnMPR 7 -6.183067 (0.0000) -2.971853 -2.625121 @1(1) 

LnCRR 7 -4.908116 (0.0005) -2.971853 -2.625121 @1(1) 

LnLDR 7 -5.748665 (0.0001) -2.971853 -2.625121 @1(1) 

LSDR 7 -5.206266 (0.0002) -2.971853 -2.625121 @1(1) 

 
Authors’ computation output using E-view 10. 

 
The researchers having certified adoption Autoregressive Distributive Lag (ARDL) for estimation of the specified 

model then moved to model selection using Akaike Information Criterion (AIC) as shown below in Figure 2 below;  

4.4 Model Selection 
Figure 2 below depicts ARDL model selection based on Akaike Information Criterion (AIC). Information criteria select models 
that minimize their values. From figure 1 below, the best model, according to AIC, is an ARDL (1, 2, 0, 0, 1). This implies that 
a model that includes lagged value of the dependent variables as an additional regressor is the best description of researchers’ 
data. 

.32

.34

.36

.38

.40

.42

.44

.46

.48

AR
DL

(1
, 2

, 0
, 0

, 1
)

AR
DL

(1
, 2

, 1
, 0

, 1
)

AR
DL

(1
, 2

, 0
, 0

, 0
)

AR
DL

(1
, 2

, 0
, 0

, 2
)

AR
DL

(2
, 2

, 0
, 0

, 1
)

AR
DL

(1
, 2

, 0
, 1

, 1
)

AR
DL

(1
, 2

, 1
, 0

, 2
)

AR
DL

(2
, 2

, 1
, 0

, 1
)

AR
DL

(1
, 2

, 2
, 0

, 1
)

AR
DL

(1
, 2

, 1
, 1

, 1
)

AR
DL

(1
, 2

, 1
, 0

, 0
)

AR
DL

(1
, 0

, 0
, 0

, 0
)

AR
DL

(1
, 0

, 0
, 0

, 1
)

AR
DL

(2
, 2

, 0
, 0

, 0
)

AR
DL

(2
, 2

, 0
, 1

, 1
)

AR
DL

(1
, 2

, 0
, 1

, 0
)

AR
DL

(1
, 0

, 0
, 1

, 0
)

AR
DL

(2
, 2

, 0
, 0

, 2
)

AR
DL

(1
, 0

, 0
, 1

, 1
)

AR
DL

(1
, 2

, 0
, 1

, 2
)

Akaike Information Criteria (top 20 models)

 

Figure 2. Model Selection based on AIC 

Authors’ computation output using E-view 10. 

The researchers now commence estimation of the models with ARDL, aimed at proffering dynamic solution to the static 
problem of time series. This is shown in table 5 below. 

4.5 Model Estimation and Results 
Having confirmed the preliminary finametric statistical test, the researchers confidently proceeded to estimating the relationship 
between stock market performance (ASI) and monetary policy variables (MPR, CRR, LDR and SDR) in Nigeria with ARDL 
framework.  

Table 5 below found that ASI has p-value of 0.0000 indicating that ASI reinforces itself or is autoregressive. It is 
statistically confirmed evidence showing that ASI in the past can predict future returns in capital market in Nigeria.  It was 
found that MPR has coefficient of 0.724611 with p-value of 0.0256 at lag 2 indicating that MPR has positive and significant 



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relationship with ASI, while other monetary policy variables have insignificant relationship with ASI. The adjusted R-square is 
0.949880 revealing that the estimated ARDL (1, 2, 0, 0, 1) model is moderately fitted, with the explanatory variable jointly 
accounting for 94.9% of total variation of ASI. The probability of F-Statistic is 0.000000, evidence that the estimated model is 
highly significant. Durbin-Watson Statistics (Dw) is 1.682484 suggesting absence of autocorrelation.  
 

   Table 5.  ARDL Estimation Results 
 

Dependent Variable: LNASI   

Method: ARDL    

Model selection method: Akaike info criterion (AIC) 

Dynamic regressors (2 lags, automatic): LNMPR LNCRR LNLDR LNSDR     

Selected Model: ARDL(1, 2, 0, 0, 1)  

Variable Coefficient Std. Error t-Statistic Prob.*   

LNASI(-1) 0.852353 0.056070 15.20145 0.0000 

LNMPR -0.158215 0.326985 -0.483859 0.6337 

LNMPR(-1) -0.081640 0.329247 -0.247961 0.8067 

LNMPR(-2) 0.724611 0.300541 2.411018 0.0256 

LNCRR 0.035349 0.089174 0.396401 0.6960 

LNLDR 0.143261 0.268741 0.533081 0.5999 

LNSDR 0.144891 0.302112 0.479593 0.6367 

LNSDR(-1) -0.353063 0.221791 -1.591870 0.1271 

 R-squared 0.962874 Durbin-Watson stat 1.682484 

Adjusted R-squared 0.949880   

 
Authors’ computation output using E-view 10. 

 
4.5.1 Test of long run Relationships and cointegration between Monetary Policy Variables and Capital Market 
To examining if there is long run relationship in the model, table 6 summarizes the output for long run effect and cointegration 
of the dependent and independent variables. MPR has p-value of 0.0256, the result confirms that MPR significantly relate with 
ASI both in the short run and long run. Table 6 also shows the ARDL Bound cointegration.. From the bound test, it can be 
seen that the F-Statistics is 4.902550, which is greater than all the critical values at 1(0) and 1(1) bound at 1%, 5% and 10%. 
These reject the null hypothesis of no levels of relationship. With this result the researchers have sufficient evidence to declare a 
cointegration between capital market performance proxied by All Share Index (ASI) and Monetary Policy variables (Monetary 
Policy Rates (MPR), Cash Reserve Ratio (CRR), Liquidity Ratio (LDR) and Savings Deposit Rate (SDR)) in Nigeria within 
the scope of this study. 

  Table 6. ARDL Long Run Form and Bounds Test 
 

Dependent Variable: D(LNASI)   

Selected Model: ARDL(1, 2, 0, 0, 1)  

Long run Test 

Variable Coefficient Std. Error t-Statistic Prob.    

LNASI(-1)* -0.147647 0.056070 -2.633241 0.0159 

LNMPR(-1) 0.484756 0.560427 0.864977 0.3973 

LNCRR** 0.035349 0.089174 0.396401 0.6960 

LNLDR** 0.143261 0.268741 0.533081 0.5999 

LNSDR(-1) -0.208172 0.228098 -0.912642 0.3723 

D(LNMPR) -0.158215 0.326985 -0.483859 0.6337 

D(LNMPR(-1)) -0.724611 0.300541 -2.411018 0.0256 

D(LNSDR) 0.144891 0.302112 0.479593 0.6367 

     
 EC = LNASI - (3.2832*LNMPR + 0.2394*LNCRR + 0.9703*LNLDR  -
1.4099 

        *LNSDR )   



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F-Bounds Test Null Hypothesis: No levels relationship 

 
Test Statistic Value Signif. I(0) I(1) 

F-statistic  4.902550 10%   1.9 3.01 

K 4 5%   2.26 3.48 

  2.5%   2.62 3.9 

  1%   3.07 4.44 

 
4.5.2 Correction Short Run Error Test 
Table 7 below revealed that error correction equation, CointEq(-1) has expected negative sign of -0.147647 and p-value of 
0.0000 suggesting the model is statistically significant. It can also be adduced that 14.7% of errors from the equilibrium can be 
corrected in the next period, and speed of adjustment is 14.7%. 
 

 Table 7. ARDL Error Correction Regression 

ARDL Error Correction Regression  

Dependent Variable: D(LNASI)   

Selected Model: ARDL(1, 2, 0, 0, 1)  

Variable Coefficient Std. Error t-Statistic Prob.    

D(LNMPR) -0.158215 0.198721 -0.796166 0.4353 

D(LNMPR(-1)) -0.724611 0.207990 -3.483876 0.0023 

D(LNSDR) 0.144891 0.181716 0.797348 0.4346 

CointEq(-1)* -0.147647 0.027223 -5.423587 0.0000 

 
Authors’ computation output using E-view 10. 

 
Next is to run some residual diagnostic test; Normality Test, Serial Correlation Test and Heteroscedasticity Test as seen tables 
8, 9 and 10 below; 

4.6 Residual Diagnostic Test 
4.6.1 Normality Test 
From Table 88 below, it is seen that Jarque-Bera Statistic is 0.6348817 with P- value of 0.728033 clear evidence of normal 
distribution. 
 
Table 9. Normality Distribution 

0

1

2

3

4

5

6

7

8

9

-0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5

Series: Residuals

Sample 1991 2018

Observations 28

Mean       0.001765

Median   0.041867

Maximum  0.403183

Minimum -0.496786

Std. Dev.   0.218765

Skewness  -0.366121

Kurtosis   2.910837

Jarque-Bera  0.634817

Probability  0.728033 

 

Authors’ computation output using E-view 10. 



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4.6.2 Serial Correlation Test and Heteroscedasticity Test 
The table 9 below shows that Heteroscedasticity Test: ARCH F-Statistic has P-value of 0.30888, suggesting no presence 
heteroscedasticity in the model. Also, table 10 depicting Breusch-Godfrey Serial Correlation LM Tests with F-Statistic P-value 
of 0.4594, which shows of non-rejection of the null hypothesis, an indication of absence of serial correlation.  
 

Table 9.  Heteroscedasticity 
 

Heteroskedasticity Test: ARCH   

F-statistic 1.079507     Prob. F(1,25) 0.3088 

Obs*R-
squared 

1.117609     Prob. Chi-
Square(1) 

0.2904 

 

Table 10. Serial Correlation Tests 

 

Breusch-Godfrey Serial Correlation LM Test:  

F-statistic 0.570331     Prob. F(1,19) 0.4594 

Obs*R-squared 0.815994     Prob. Chi-
Square(1) 

0.3664 

 
Authors’ computation output using E-view 10. 

 
4.7 Causality Relationship 
From the table 11 below, ASI granger cause MPR (F-statASI = 3.23061; ProbASI= 0.0580, significant at 10%), a unidirectional 
causality between ASI and MPR. Whereas, CRR. LDR and SDR have no traceable causal relationship with ASI since their p-
values are greater than the significant levels of 5% and 10%.  

      Table 11. Pairwise Granger Causality Test 

 Null Hypothesis: Obs F-Statistic Prob.  

 MPR does not Granger Cause ASI  28  0.08726 0.9167 

 ASI does not Granger Cause MPR  3.23061 0.0580 

 CRR does not Granger Cause ASI  28  2.16887 0.1371 

 ASI does not Granger Cause CRR  0.53241 0.5943 

 LDR does not Granger Cause ASI  28  0.11887 0.8885 

 ASI does not Granger Cause LDR  2.16953 0.1370 

 SDR does not Granger Cause ASI  28  2.51592 0.1028 

 ASI does not Granger Cause SDR  0.01519 0.9849 

 
Authors’ computation output using E-view 10. 

 
5. Concluding Remarks  
The concern on whether monetary policy tools could influence the performance of the Nigerian Capital Market with suitable 
statistical tools made the following remarkable findings: that all the monetary policy variables employed in this study; only 
monetary policy rate has significant relationship with the performance of the capital market in Nigeria. The veracity is not in 
doubt because economic and finance scholars have already confirmed that the efficacy of the monetary policy transmission from 
CBN is mainly conspicuous in the money market, mostly deposit money banks (Onoh, 2007). This output collaborate the 
findings of Okpara (2010) and Echekoba etal, 2017, 2018, also with the fundamental hypothesis reviewed in the theoretical 
literature. It was all found that the previous information about the all share index has the capacity to predict future returns in 
capital market in Nigeria. This makes the market efficient by adopting the efficient market hypothesis reviewed in this study. On 
this note, the researchers are of the opinion to embark on prompt disclosure of the daily all share indexes by regulatory 
authorities, thereby refurbishing the efficiency of the Nigeria Capital Market. It is also suggested to adopt alternative means of 
disclosure apart from the national television stations and national daily newspapers because of our technological know-how in 
Nigeria. Again, having confirmed that Monetary Policy Rate has significant relationship with the performance of the capital 
market in Nigeria, the researchers are of the opinion that Central Bank of Nigeria should revisit the issue of MPR rates and 



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grant a special MPR to commercial banks in relation to Margin loans which must be applied mandatorily in the capital market 
to further boost the capital market performance. 

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