Journal of Tourism & Hospitality

Journal of Tourism & Hospitality
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Research Article - (2016) Volume 5, Issue 5

An Empirical Study on the Dynamic Relationship between Crude Oil Prices and Nigeria Stock Market

Rabia Najaf* and Khakan Najaf
Department of Accounting and Finance, Islamabad Campus, Pakistan
*Corresponding Author: Rabia Najaf, Department of Accounting and Finance, University of Lahore, Islamabad Campus, Pakistan, Tel: +92 (0)42 111-865-865 Email:

Abstract

In this paper, we have examined the crude oil price on the performance of Nigerian stock exchange and exchange rate act as the plausible countercyclical tool. We have applied the different models and collected the results that crude oil prices have direct impact on the stock exchange of Nigeria. The Nigeria stock exchange is regulated by the Securities and Exchange Commission. Nigeria stock exchange has the automated trading system. The basic facility of Nigeria trading system is (ATS), it is helpful to remote trading system. Consequently, most of the investors do trade with the method of ATS. This study is also proving that Nigeria stock exchange has influenced on the performance of the economy. Impact of oil crisis on the Nigeria stock exchange. Impact of crude oil crisis on the development of country. Effect of exchange rate policy on the performance of Nigeria stock exchange.

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Keywords: Nigerian stock exchange, Exchange commission, ATS, Crude oil

Introduction

Robust of the studies have been done about the fluctuations effect of crude oil on the stock exchange of Malaysia. Financial market is known as the crucial way to analysis the impact of decrease crude oil prices on the stock exchange stock exchange of Malaysia. According to Kumar oil crisis has impact worst on the performance of stock exchange. In this paper, discussed the two main dimestions1) impact of oil crisis on the importing country 2) impact of oil prices on the exporting countries. Soytas, 2006 have analyzed the impact of oil crisis on the Nigeria stock exchange, for this purpose they have utilized the VAR model. It is very effective model to analysis the impact of crude oil prices on the stock exchange of all the stock exchange. This model is also affected to analysis the response of dependent variable on the logged values of the independent variables.

History of Nigeria stock exchange

First time the Nigeria stock exchange was established in 1960 with the name of Lagos stock exchange. After the sometimes, its name was changes now it is known as the Nigerian stock exchange. In 2016, there are listed near about 181 listed companies with the market capitalization of about N10.17 trillion. Nigeria stock exchanges is known as the third largest stock exchange of Africa. The Nigeria stock exchange is regulated by the Securities and Exchange Commission. Nigeria stock exchange has the automated trading system. The basic facility of Nigeria trading system is (ATS), it is helpful to remote trading system. Consequently, most of the investors do trade with the method of ATS. Every business day the trade has started from 9.30 am and close to 2.30 PM (Figure 1).

tourism-hospitality-in-2009-world-production

Figure 1: In 2009 world production shares.

Objective of the Study

1) Impact of oil crisis on the Nigeria stock exchange.

2) Impact of crude oil crisis on the development of country.

3) Effect of exchange rate policy on the performance of Nigeria stock exchange.

Problem Statement

Impact of oil prices on the stock exchange of Nigeria.

Impact of International Crude Oil on the Different Stock Market

1) High profitability can be created with the lower cost of energy.

2) There is inverse relationship between crude oil and exchange rate.

3) In the different domestic market the demand of lower energy is very high.

Hypothesis Study

HO: There is relationship between oil prices and stock exchange of Nigeria.

HA: There is no relationship between oil prices and stock prices of Nigeria (Figure 2).

tourism-hospitality-theoretical-framework

Figure 2: Theoretical framework.

Literature Review

Arouri, Lahiani and Nguyen observed that impact of crude oil by the various sectors of stock exchange of India. For this purpose, they had taken the data from 2002 to 2012 and applied the VECM model and proved that there is no positive relationship between oil prices and stock exchange of India [1].

Bollerslev, Engle and Wooldridge examined that impact of crude oil by the various sectors of stock exchange of France. For this purpose, they had taken the data from 2003 to 2013 and applied the VAR model and proved that there is no positive relationship between oil prices and stock exchange of France [2].

Cappiello, Engle and Sheppard analyzed that impact of crude oil by the various sectors of stock exchange of China. For this purpose, they had taken the data from 2004 to 2014 and applied the VECM model and proved that there is no positive relationship between oil prices and stock exchange of China [3].

Dhaoui and Khraief viewed that impact of crude oil by the various sectors of stock exchange of Japan. For this purpose, they had taken the data from 2001 to 2011 and applied the multi regression model and proved that there is no positive relationship between oil prices and stock exchange of Japan [4].

Guesmi, Fattoum and Ftiti observed that impact of crude oil by the various sectors of stock exchange of Pakistan. For this purpose, they had taken the data from 2001 to 2011 and applied the VAR model and proved that there is no positive relationship between oil prices and stock exchange of Pakistan [5].

Hung, Lee and Liu viewed that impact of crude oil by the various sectors of stock exchange of Jordan. For this purpose, they had taken the data from 2001 to 2011 and applied the GARCH model and proved that there is no positive relationship between oil prices and stock exchange of Jordan [6].

Dhaoui and Khraief analyzed that impact of crude oil by the various sectors of stock exchange of Nigeria. For this purpose, they had taken the data from 2005 to 2015 and applied the ARCH model and proved that there is no positive relationship between oil prices and stock exchange of Nigeria [5,7-9].

Hung, Lee and Liu examined that impact of crude oil by the various sectors of stock exchange of Asian countries. For this purpose, they had taken the data from 2003 to 2013 and applied the VAR model and proved that there is no positive relationship between oil prices and stock exchange of Asian countries [10-15].

Felipe and Diranzo analyzed that impact of crude oil by the various sectors of stock exchange of UK. For this purpose, they had taken the data from 2004 to 2014 and applied the VECM model and proved that there is no positive relationship between oil prices and stock exchange of UK [16-22].

Engle viewed that impact of crude oil by the various sectors of stock exchange of USA. For this purpose, they had taken the data from 2001 to 2011 and applied the VAR model and proved that there is no positive relationship between oil prices and stock exchange of USA [23].

Gaps in literature

1) In the last studies, nobody had discussed about the alternative of oil.

2) In the past studies nobody has explained impact of crude oil on the economy condition.

3) From the last studies nobody has major reason of increasing inflation rate day by day.

Methodology

In this paper, we have adopted the econometric data, it is based on the empirical facts. We have derived the hypotheses from here. We have showed the associations between dependent and independent variables.

Model specification

The following models of the capital market indicators were specified for this study:

Stock Price model, represented as SP=f(OP, GDP, EXR, INV, MPR); and its regression model is stated as ;

SP=a0+a1OP+a2 GDP+a3EXR+a4INV+a5MPR+μ1

Where,

SP=Stock Price (representing the stock market performance)

OP=Oil price

GDP=Gross Domestic Product

EXR=Exchange Rate

INV=Investment

MPR=Monetary Policy Rate

μ1=Stochastic Error term

Dependenvariable:sp

Method: Leastsquare

Included observations: 31 (Tables 1-12).

Variable Coefficient Std. Error t-Statistic Prob.
C 0.323688 0.294622 1.098655 0.2829
OP 0.011081 0.004125 2.686375 0.0128
GDP -0.00466 0.001688 -2.76104 0.0108
EXR -0.00111 0.001208 -0.92126 0.3662
INV 0.116203 0.032207 3.608137 0.0015
MPR -0.01714 0.008975 -1.90946 0.0683
R-squared 0.800318 Mean dependent var   0.330334
Adjusted R-squared 0.758719 S.D. dependent var   0.412992
S.E. of regression 0.202864 Akaike info criterion   -0.17572
Sum squared resid 0.987684 Schwarz criterion   0.104526
Log likelihood 8.635712 Hannan-Quinn criter.   -0.08606
F-statistic 19.23832 Durbin-Watson stat   0.999702
Prob(F-statistic) 0      

Table 1: Model Specification.

Variable Coefficient Std. Error t-Statistic Prob.
C -12.8386 12.68815 -1.01186 0.3218
OP 0.404382 0.177624 2.276628 0.0321
GDP 0.225637 0.072646 3.105998 0.0049
EXR -0.0261 0.051999 -0.50189 0.6204
INV -1.88583 1.386968 -1.35968 0.1867
MPR 0.144252 0.386456 0.373268 0.7123
R-squared 0.811832 Mean dependent var   11.552
Adjusted R-squared 0.772628 S.D. dependent var   18.32188
S.E. of regression 8.736495 Akaike info criterion   7.349752
Sum squared resid 1831.833 Schwarz criterion   7.629992
Log likelihood -104.246 Hannan-Quinn criter.   7.439403
F-statistic 20.708 Durbin-Watson stat   2.115538
Prob(F-statistic) 0      

Dependent Variable: MC
Method: Least Squares
Sample: 1981 2008
Included observations: 31.

Table 2: The MC Equation.

Variable Coefficient Std. Error t-Statistic Prob.
C -12.8386 12.68815 -1.01186 0.3218
OP 0.404382 0.177624 2.276628 0.0321
GDP 0.225637 0.072646 3.105998 0.0049
EXR -0.0261 0.051999 -0.50189 0.6204
INV -1.88583 1.386968 -1.35968 0.1867
MPR 0.144252 0.386456 0.373268 0.7123
R-squared 0.811832 Mean dependent var   11.552
Adjusted R-squared 0.772628 S.D. dependent var   18.32188
S.E. of regression 8.736495 Akaike info criterion   7.349752
Sum squared resid 1831.833 Schwarz criterion   7.629992
Log likelihood -104.246 Hannan-Quinn criter.   7.439403
F-statistic 20.708 Durbin-Watson stat   2.115538
Prob(F-statistic) 0      

Dependent Variable: MC
Method: Least Squares
Sample: 1981 2008
Included observations: 31.

Table 3: The NLC Equation.

Variable Coefficient Std. Error t-Statistic Prob.
C 69.19435 26.29365 2.631602 0.0147
OP -1.25007 0.368089 -3.39612 0.0025
GDP 0.656899 0.150544 4.363524 0.0003
EXR 0.630309 0.107757 5.849388 0
INV -2.34707 2.874212 -0.8166 0.4223
MPR 2.168548 0.800851 2.707807 0.0124
R-squared 0.893414 Mean dependent var 149.4334  
Adjusted R-squared 0.871209 S.D. dependent var 50.44813  
S.E. of regression 18.10465 Akaike info criterion 8.807071  
Sum squared resid 7866.669 Schwarz criterion 9.087309  
Log likelihood -126.106 Hannan-Quinn criter. 8.896721  
F-statistic 40.23379 Durbin-Watson stat 1.732026  
Prob(F-statistic) 0      
      t-Statistic Prob.*
Augmented Dickey-Fuller test statistic     -3.41698 0.0186
         
Test critical values: 1% level   -3.67932  
  5% level   -2.96777  
  10% level   -2.62299  

0.0025, 0.0003, 0.0000 and 0.0124 respectively.
Null Hypothesis: SP has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=0).
For the MC equation.

Table 4: Augmented Dickey-Fuller Unit Root Test on SP.

Variable Coefficient Std. Error t-Statistic Prob.
SP(-1) -0.18569 0.054344 -3.41698 0.0021
C 0.015356 0.028921 0.530956 0.5999
R-squared 0.301888 Mean dependent var   -0.04759
Adjusted R-squared 0.276034 S.D. dependent var   0.141108
S.E. of regression 0.120066 Akaike info criterion   -1.3351
Sum squared resid 0.389218 Schwarz criterion   -1.24081
Log likelihood 21.35898 Hannan-Quinn criter.   -1.30557
F-statistic 11.67578 Durbin-Watson stat   1.977997
Prob (F-statistic) 0.002022      

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(SP)
Method: Least Squares
Sample (adjusted): 1981 2009
Included observations: 29 after adjustments.

Table 5: MacKinnon (1996) one-sided p-values.

    t-Statistic Prob.*
Augmented Dickey-Fuller test statistic   -1.58265 0.4785
Test critical values: 1% level -3.67932  
  5% level -2.96777  
  10% level -2.62299  

Null Hypothesis: MC has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=7).

Table 6: Augmented Dickey-Fuller Unit Root Test on MC.

Variable Coefficient Std. Error t-Statistic Prob.
MC(-1) -0.1898 0.119864 -1.58265 0.1252
C 3.135381 2.498507 1.254903 0.2204
R-squared 0.084895 Mean dependentvar   1.090346
Adjusted R-squared 0.051002 S.D. dependentvar   11.82116
S.E. of regression 11.51577 Akaike infocriterion   7.791783
Sum squaredresid 3580.544 Schwarz criterion   7.886079
Log likelihood -110.981 Hannan-Quinncriter.   7.821315
F-statistic 2.504777 Durbin-Watsonstat   1.599451
Prob(F-statistic) 0.125147      

Exogenous: Constant,
Lag Length: 0 (Automatic - based on SIC, maxlag=7).

Table 7: Null Hypothesis: NLC has a unit root.

    t-Statistic Prob.*
    -0.95816 0.7554
Test critical values: 1% level -3.67932  
  5% level -2.96777  
  10% level -2.62299  

*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(NLC)
Method: Least Squares
Date: 04/27/10 Time: 15:33
Sample (adjusted): 1981 2009
Included observations: 29 after adjustments.

Table 8: Augmented Dickey-Fuller test statistic.

Variable Coefficient Std. Error t-Statistic Prob.
NLC(-1) -0.04987 0.052048 -0.95816 0.3466
C 12.06895 8.079408 1.493791 0.1469
R-squared 0.032885 Mean dependent var   4.724139
Adjusted R-squared -0.00294 S.D. dependent var   13.72667
S.E. of regression 13.74678 Akaike info criterion   8.145958
Sum squared resid 5102.303 Schwarz criterion   8.240257
Log likelihood -116.117 Hannan-Quinn criter.   8.175493
F-statistic 0.918068 Durbin-Watson stat   2.439743
Prob(F-statistic) 0.346486      

Included observations: 28 after adjustments,
Standard errors in ( ) and t-statistics.

Table 9: Sample (adjusted): 1982 2009.

  SP MC NLC
SP(-1) 0.814218 4.154087 8.769695
  -0.21807 -17.3693 -17.4485
[ 3.73395] [ 0.23917] [ 0.50261]  
SP(-2) -0.06341 -3.10081 -7.81972
  -0.19834 -15.7976 -15.8697
  [-0.31971] [-0.19628] [-0.49276]
MC(-1) 0.000981 0.286044 -0.14246
  -0.00363 -0.28832 -0.28964
  [ 0.27078] [ 0.99213] [-0.49188]
MC(-2) -0.00265 -0.14072 0.0786
  -0.00286 -0.22681 -0.22884
  [-0.93108] [-0.62045] [ 0.34555]
NLC(-1) 0.001178 0.159615 0.546679
  -0.00198 -0.15868 -0.15949
  [ 0.59086] [ 1.00598] [ 3.42927]
NLC(-2) -0.00136 -0.07794 0.349386
  -0.00194 -0.15406 -0.15486
  [-0.70482] [-0.50593] [ 2.25770]
OP 0.001418 0.580219 0.127034
  -0.00249 -0.19724 -0.19823
  [ 0.57237] [ 2.94188] [ 0.64212]
C 0.022623 -19.8763 20.64314
  -0.16345 -13.0187 -13.0881
  [ 0.13842] [-1.526762 [ 1.58838]
R-squared 0.870554 0.795904 0.96494
Adj. R-squared 0.825247 0.724468 0.951419
Sum sq. resids 0.307576 1951.486 1969.459
S.E. equation 0.124012 9.877968 9.923101
F-statistic 19.21477 11.14181 76.47657
Log likelihood 23.42708 -99.1483 -99.3759
Akaike AIC -1.10194 7.653448 7.672564
Schwarz SC -0.72131 8.034078 8.044194
Mean dependent 0.252144 12.18358 156.0814
S.D. dependent 0.296653 18.81838 44.97583
Determinant resid covariance (dof adj.)   126.7048  
Determinant resid covariance   46.17525  
Log likelihood   -172.845  
Akaike information criterion   14.06037  

Unrestricted Cointegration Rank Test (Trace)
Sample (adjusted): 1983 2009
Included observations: 27 after adjustments
Trend assumption: Linear deterministic trend
Series: SP MC NLC
Lags interval (in first differences): 1 to 2.

Table 10: Schwarz criterion.

Hypothesized Eigenvalue Trace 0.05 Prob.**
No. of CE(s) Statistic Critical Value
None * 0.568427 32.58108 29.79708 0.0234
At most 1 0.306707 9.892559 15.49472 0.2892
At most 2 8.65E-06 0.002336 3.841467 0.9595

Trace test indicates.
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
*denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values.

Table 11: Trace test.

Hypothesized Eigenvalue Max-Eigen 0.05 Prob.**
No. of CE(s) Statistic Critical Value
None * 0.568427 22.68855 21.13163 0.04
At most 1 0.306709 9.890223 14.26461 0.3192
At most 2 8.65E-06 0.002336 3.841467 0.8594

Table 12: Hypothesized.

In the Table 1 is showing the equation of sp and op and predictor variables are significant at 0.128, 0.0109 and 0.0015 respectively all values have less than 0.05.

In the Table 2 is showing the MC equation and showing that GDP are significant with the values of 0.0321 and 0.0049 respectively.

In the NLC equation there is not the investment is significant 0.4223. All other variables are significant with the values of 0.0025, 0.0003, 0.0000 and 0.0124 respectively.

The ADF statistic value is -3.418 and p value is 0.0186. The critical value is 2%, 5% and 10% level.

All the values are showing that these are stationarity.

The ADF statistic value is -1.584 and p value is 0.479. The critical value is 1%, 5% and 10% respectively. The value of MC is showing that there is no stationary.

Null Hypothesis: NLC has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=7).

Conclusion

Robust of the studies have done about oil prices and it is proved that oil is known as the key indicator of all the developing and under developing countries. Now a day the demands of oil prices are high and it has impacted on the prices of subsidies. According to setpen there is inverse relationship between oil prices and stock exchange. It is seen that oil prices have impacted on the transport. Therefore, our paper is trying to prove that increase in the prices of oil prices is main cause of inflation. It is not wrong saying that oil prices up and downs of oil prices have good and bad impact on the all the sort of stock exchange. Oil prices are also known as the uncontrolled variable.

Recommendation

1) There is need of proper policy to take decisions in the lower prices of oil.

2) Government should keep alternative in the worst situations.

3) How many improve the development of the economy after oil crisis.

4) How can improve inflation rate such types of conditions.

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Citation: Najaf R, Najaf K (2016) An Empirical Study on the Dynamic Relationship between Crude Oil Prices and Nigeria Stock Market. J Tourism Hospit 5:245.

Copyright: © 2016 Najaf R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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