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ISSN: 2167-0269

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Research Article - (2016) Volume 5, Issue 5

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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.

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

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**).

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.

Impact of oil prices on the stock exchange of Nigeria.

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.

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**).

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.

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).

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.

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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