TX_1~AT/TX_2~AT International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021100 International Journal of Energy Economics and Policy ISSN: 2146-4553 available at http: www.econjournals.com International Journal of Energy Economics and Policy, 2021, 11(3), 100-109. The Influence of Oil Price Fluctuations on Stock Market of Developing Economies: A Focus on Nigeria Chuke Nwude1, Damilola Felix Eluyela2, Elias Igwebuike Agbo1, Francis O. Iyoha3* 1Department of Banking and Finance, Faculty of Business Administration, University of Nigeria, Nigeria, 2Department of Accounting and Finance, Landmark University, Nigeria, 3Department of Accounting, College of Business and Social Sciences, Covenant University, Nigeria. *Email: iyoha.francis@covenantuniversity.edu.ng Received: 22 June 2020 Accepted: 28 December 2020 DOI: https://doi.org/10.32479/ijeep.10140 ABSTRACT The inconclusiveness of findings from various studies on Nigeria on the effect of crude oil price fluctuation on the stock market has led to an argument in literature, thus necessitating further exploration of the subject. This study examines the effect of variations in the price of crude oil on selected stock market performance variables in Nigeria using monthly frequency data covering January 1997-December 2016. Variance decomposition, impulse response analysis, and VAR estimations were employed for the study. The results reveal that oil price variations are slowly transmitted in some stock market performance variables. The findings indicate that the effect of crude oil price fluctuations in the Nigerian stock market is greatly minimized and does not sufficiently account for market activities. Keywords: Emerging Economy, Nigeria, Oil Price Shocks, Stock Market, Vector Autoregressive JEL Classifications: C25, Q47, F4 1. INTRODUCTION This study aims to examine whether fluctuations in crude oil price impact on stock market performance in developing economy from January 1997 to December 2016. Nigeria is used as a proxy for developing economies because she is the sixth largest member of OPEC and the largest net exporter of crude oil in Africa but also a highly promising economy for international portfolio diversification. In many industrialized economics, the production process uses crude oil as an essential raw material. Because of this, its demand is highly presumed to correlate with the growth of industrial production of many economies. From the economic perspective, higher demand for any commodity without marching increase in its supply paves the way for its price appreciation. Similarly, the cash flow of producing firms will be affected by an increase in raw material required in the production process. Nigeria exports crude oil and imports refined crude oil from international markets. It is assumed that any apparent movements in the international oil market will affect some macroeconomic variables which can affect the performance of the stock market. Considering the producer (exporter) and consumer (importer) nature of Nigeria, an increase in oil prices will likely affect the cash flow of companies and individuals. Corporate earnings will be subdued, which may lead to falling investors’ appetite towards investing in the capital market. Therefore, investigating the effect of oil price movements on the stock market performance is a study worth engaging. In Nigeria, the increase in crude oil price at international markets usually attracts more money into the federation account. As a result, more money is released to government tiers, which will put pressure on the inflation rate and exchange rate. The question here is; does this reflect in the performance of the stock market? Various studies on the effect of crude oil price fluctuation on the stock market in Nigeria show mixed results. For instance, Omisakin et al. (2009), Mordi et al. (2010), Abbas and Terfa (2010), Adebiyi et al. This Journal is licensed under a Creative Commons Attribution 4.0 International License Nwude, et al.: The Influence of Oil Price Fluctuations on Stock Market of Developing Economies: A Focus on Nigeria International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021 101 (2010), Akomolafe and Danladi (2014), Akinlo (2014), Iheanacho (2016), Lawal et al. (2018), Soyemi et al. (2017), Ojikutu et al. (2017), Obi et al. (2018) observe a positive effect of oil price shock on the stock price. On the contrary, studies like Adaramola (2012) and Effiong (2014) reported an inverse correlation between the price of oil movements and returns from stocks. For Okany (2014), the two constructs do not react to each other. However, Babatunde et al. (2013) and Effiong (2014) recorded a very weak relationship oil price shock and stock price in Nigeria. This inconclusiveness of findings has created much doubt in literature. This study is an effort aimed at providing further insight into the subject. The study’s significance lies in its ability to generate results that will improve the forecasting accuracy of stock market behavior from crude oil price variations, which will aid investors and policymakers in decision making. This study’s latest year with valid, accurate data was 2016 while the commencement year 1997 was the period the journey to stable leadership in Nigeria started. The stability of leadership of any nation says much about the functioning of various organs of the economy which includes the stock market. The remainder of the study is presented; thus, section 2 presents the literature review, while section 3 indicates the material and methods adopted in the study. Section 4 reports the empirical results and discussion, while section 5 is the conclusion. 2. LITERATURE REVIEW Conceptually, oil price shock or fluctuation refers to unanticipated changes in the prices of oil. In the wake of the oil price shocks of the 19,970, there emerged a body of literature that started growing and interrogating the effect oil prices changes have on the real economic activity. Among the early researchers that probed the oil price and aggregate economy nexus is Hamilton (1983) who emerged with the finding that fluctuations in the price of oil precipitated ten out of the eleven post-war recessions in the United States up to 1983. This motivated many scholars to carry out similar investigations. Oil price shocks usually cause some increases in the general price levels and a significant decrease in productivity. Thus, fluctuation in oil price is seen as a key ingredient for forecasting the capital market activities. Still, research has provided conflicting results, and several authors have disagreed with their findings on the nature of the nexus between oil price and the stock market. The conflicts in results have left doubt which this study intends to investigate in an emerging market economy. While crude oil is considered universally as the life-wire of every nation, stock markets are generally regarded as an engine of economic growth (Uwubanmwen and Omorokunwa, 2015). Results of some empirical inquiries on the oil price movements and stock market connection are highlighted below. Kilian and Park (2009) observe that returns on stock in the USA react to movement in oil price whether as a result of supply or demand shocks. The authors further opine that shocks in oil prices impact stock returns. Papapetrou (2001) argues that true economic activity, jobs and stock prices are a substantial reaction to changes in oil prices. Others like Jones and Kaul (1996), Sadorsky (1999), Basher et al. (2012) and Cunado and Perez de Gracia (2003) find a negative relationship, although Faff and Brailsford (1999) observe a positive link. A study on the effects of changes to oil prices on the Australian Paper and Packaging and Transportation industries was carried out by Faff and Brailsford (1989). The relationship between oil price and industries was significantly negative. Jones and Kaul (1996), conducted a similar study with a cash flow assessment model in the developed countries of the United States, Canada, Great Britain and Japan. The result showed an inverse connection between oil and stock prices. Sadorsky (1999) studied the link between oil price volatility in the USA between 1947 and 1996 using VAR and GARCH modeling and established a strong correlation between oil price volatility and inventory return. The relationships between the fluctuation in oil prices and stock market between 13 European nations and the USA have been studied by Park and Rati (2008). The result showed a strong negative effect of oil price shock on the oil-importing countries and positive effect on the oil-exporting countries. Magyereh et al. (2016) found no relationship between the stock market index returns of developing countries and oil price shocks and applying unrestricted vector autoregressive (VAR) approach on daily oil future returns and the daily US returns. It has also been observed that spot oil returns do lead some individual oil company stock returns (Huang et al., 1996). Still, general market indices are not much impacted by oil future returns. Zhang (2017), Nandha and Faff (2008) confirm that large oil shocks occasionally contribute a big way to stock markets. 3. MATERIALS AND METHODS 3.1. Materials This study adopted an expo facto research design. The stock market data for the study were obtained from the Nigeria Stock Exchange (NSE) and Central Bank of Nigeria (CBN) Statistical Bulletins. The data frequency is monthly from January 1st 1997 to December 31st 2016 and contains Naira dominated value-weighted stock market indices. The stock market variables which form our dependent variables consist of market capitalization, All-Share Index, the market value of shares traded, the market volume of shares traded, average closing price and several deals. Market capitalization is the monthly sum of all the listed firms on the NSE as documented by the NSE. All-Share Index is the barometer that measures the strength of the stock market in terms of share price appreciations and depreciation in the market. The market value of shares traded is the product of the number of shares traded on each stock multiply by the market price per share. Market volume of shares traded presents the number of shares traded on the NSE for all the listed firms. The average closing price is the monthly mean market price per share of each stock for all the listed firms. Several deals are the monthly sum of individual transactions on all the listed stocks. In all the above-mentioned stock market variables were collected from the NSE and Central Bank of Nigeria statistical bulletins. The crude oil price data were sourced from US Energy Information Administration data stream (2018), and this encompasses spot historical prices of Brent crude oil from January 1997 to December 2016. This variable was employed as our independent variable to measure oil price shocks’ effect on some selected stock market variables. We choose to use the Brent spot Nwude, et al.: The Influence of Oil Price Fluctuations on Stock Market of Developing Economies: A Focus on Nigeria International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021102 crude oil price indices rather than other local oil price or other oil prices such as West Texas Intermediate and Dubai-serve for several reasons. First, Brent spot crude oil price was expressed in U.S. $/barrel. Second, Brent spot crude oil price measures the spot price of various oil barrels, which are quoted in the global oil market. Thirdly, Brent oil serves as a benchmark in the crude oil market. However, consistent with convention, all data used in this study were transformed by taking the raw data’s natural logarithm. The control variables that captured and factored Nigerian economic moods in this study are the exchange rates and the inflation rates, which are quite high compared to developed economies. 3.2. Methods The study employed Vector Autoregressive (VAR) model to estimate the effect of oil price shocks on selected stock market variables. This enables the endogeneity of all remaining variables tested when oil price shocks are introduced as exogenous variables. The appropriate diagnostic tests were used to ascertain the linear or non-linear effects of crude oil price shocks on some selected stock market variables. We conducted Unit Root based on Augmented Dickey-Fuller and Phillips and Perron to verify the order of integration of the variables. Extant literature is on the position that VAR modelling employs a series of unit root tests to ensure our variables are integrated on the order of one 1(1). We employed the Akaike Information Criterion (AIC), and Schwarz Bayesian Criterion (SBC) to determine the appropriate number of lag length of the VAR model. However, the study employed the variance decomposition and impulse response functions to analyze the variables’ short-run dynamics. 4. EMPIRICAL RESULTS AND DISCUSSION 4.1. Descriptive Statistics Table 1 demonstrates that all the variables selected for the study have positive mean values. The standard deviation of the all-share index (9.171) is the highest among the variables, implying that it is the riskiest and most highly volatile period of study. The positive mean monthly oil price changes indicate an upward trend during the study period. The mean value of the all-share index is 99544.04 points for the 240 months and the highest. Market capitalization and several deals equally exhibited high variability during the period. Probably, the innovation in these selected stock market variables in Nigeria has been fueled by the unstable money supply regimes and the frequent movements in the international oil price. According to this summary statistics, the average monthly closing price fluctuated rather slowly during the period. The negative value of skewness for our data set revealed that the data points are clustered to the left side of the mean, except lnAPPA with a positive cluster which implies that data points are skewed to the right of the data average. The variables indicated that the data are not normally distributed as a result of sets of data not balanced normal distribution (skewness of zero), except for lnNOD that the data are normally distributed. Confirming the above analytics, Kurtosis results in Table 1 showed that the variables are not normally distributed which revealed symmetric distribution with no well-behaved tails excluding lnAPPB, lnNOD and VOPPB with more than the expected value of 3 indicating that symmetric distribution is well-behaved. Although kurtosis confirmed that all the variables are heavily-tailed distribution with positive expected values, though, Jarque-Bera test statistic of our dataset exceeds the critical value of 5% significance level, resulting in the conclusion that the adopted variables follow a normal distribution. 4.2. Tests for Stationarity To determine the stationarity of the employed variables, the result of unit root tests in Table 2 shows the order of integration (does not have unit root). Traditionally, the null hypothesis assumes that variables have a unit root. The outcomes for the unit root test are based on the assumption of Augmented Dickey-Fuller (ADF) and Phillips and Perron (PP) are attained at 5% level of significance. However, the decision rule for the position to accept the null hypothesis that the variable has a unit root or does not support the outcome of the two statistical tests. The outcomes from Table 2 above revealed that the employed variables attained stationarity (does not have unit root), but these were obtained at the first difference. Based on the above outcomes, the study rejects the null hypothesis assumption of Augmented Dickey-Fuller (ADF) and Phillips and Perron (PP). It concludes that our employed variables do not have a unit root. Nevertheless, stationarity was attained at 1(1), but none of the variables attained stationarity at 1(2). On this note, the overall outcomes satisfy the condition for Johansen Cointegration test since all the variables attained stationarity after first differencing. The outcomes of the Johansen cointegration test was subject to satisfying the precondition for running cointegration model, which states that variables must be non- stationary at the level. Still, when the variables are converted into the first difference, then they become stationary. This position was Table 1: Descriptive statistics Variable Mean Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera LNOPPB 3.828 4.897 2.282 0.683 −0.318 2.052 11.352 %∆OPPB 0.802 25.080 −26.910 9.171 −0.340 3.115 4.146 LNAPPB 3.257 4.993 2.283 0.3754 0.629 5.957 89.914 LNMC 7.819 9.549 5.375 1.422 −0.433 1.614 23.279 LNMVALUE 23.316 26.369 19.755 1.601 −0.538 2.066 17.677 LNMVOL 21.049 23.723 17.332 1.528 −0.388 2.013 13.718 LNNOD 10.838 12.885 2.493 1.478 −2.323 11.396 801.933 LNNSEASI 9.854 11.051 8.495 0.694 −0.522 2.105 16.490 Source: Researcher’s Estimation using E-View. lnAPPB: Natural log of Average closing oil price per barrel in US$, lnOPPB: Natural log of Oil Price per barrel in US$ at month-end, %∆OPPB: Percentage change in Oil Price per barrel in US$, lnMC: Natural log of Market capitalization in Billion Naira, lnNSEASI: Natural log of Nigerian Stock Exchange All-Share Index, lnMVOL: Natural log of the Market volume of trade, lnMVAL: Natural log of Market value of trade in Naira, lnNOD: Natural log of Number of deals or trades. Nwude, et al.: The Influence of Oil Price Fluctuations on Stock Market of Developing Economies: A Focus on Nigeria International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021 103 highlighted in the previous section, where employed variables attained stationarity after first difference. The outcomes for the trace test and the max-eigen value test both indicate the existence of no cointegration at 5% level. This implies that the variables of the study have no long-run equilibrium relationships among themselves. However, the Johansen cointegration test’s outcome led to the employment of the unrestricted VAR model in favour of vector error correction model (VECM). 4.3. VAR Model Estimation 4.3.1. VAR model estimates using oil price per barrel The outcome from Table 3 indicates a significant influence of crude oil price on itself which implies that the variable is strongly endogenous but has a strongly exogenous influence on other employed variables, that is, the crude oil price has a weak influence on dependent variables. Although, exceptional among the variables is NSE all-share index that appeared to be least exogenous, which implies that crude oil price has a strong influence on NSE all-share index. The result of VAR estimation showed that average closing oil price per barrel has strong endogeneity since the variable has a significant influence on itself. The influence of average closing Table 3: VAR model estimates using Oil Price per barrel lnOPPB lnAPPB lnMC lnMVALUE lnMVOL lnNOD lnNSEASI lnOPPBt−1 1.136 (14.625) 0.006 (0.030) 0.380 (1.803) −0.241 (−0.371) 0.153 (0.201) −0.082 (−0.089) 0.151 (1.989) lnOPPBt−2 −0.216 (−2.781) 0.019 (0.090) −0.262 (−1.243) 0.399 (0.614) −0.078 (−0.103) 0.167 (0.180) −0.121 (−1.602) lnAPPBt−1 −0.012 (−0.439) 0.466 (6.113) 0.038 (0.501) −0.022 (−0.094) 0.199 (0.717) −0.045 (−0.133) 0.056 (2.017) lnAPPBt−2 −0.040 (−1.371) 0.381 (4.791) 0.007 (0.089) 0.116 (0.467) 0.153 (0.527) −0.803 (−2.281) −0.025 (−0.885) lnMCt−1 0.037 (1.186) 0.053 (0.626) 0.276 (3.250) −0.076 (−0.291) −0.215 (−0.701) −0.142 (−0.380) 0.046 (1.499) lnMCt−2 −0.018 (−0.579) −0.073 (−0.852) 0.654 (7.571) −0.052 (−0.194) 0.174 (0.555) 0.079 (0.206) −0.057 (−1.823) lnMVALUEt−1 0.005 (0.477) 0.003 (0.108) 0.012 (0.418) 0.375 (4.148) 0.202 (1.907) 0.074 (0.578) 0.012 (1.123) lnMVALUEt−2 0.005 (0.418) 0.020 (0.662) 0.006 (0.211) 0.292 (3.183) 0.279 (2.599) −0.144 (−1.097) 0.006 (0.543) lnMVOLt−1 0.005 (0.569) −0.002 (−0.080) −0.007 (−0.301) 0.146 (1.972) 0.322 (3.708) −0.003 (−0.032) −0.004 (−0.431) lnMVOLt−2 −0.005 (−0.602) −0.005 (−0.202) 0.020 (0.819) −0.023 (−0.314) −0.009 (−0.109) 0.028 (0.266) 0.006 (0.647) lnNODt−1 −0.005 (−0.775) −0.002 (−0.144) 0.050 (2.858) 0.027 (0.498) −0.035 (−0.557) 0.036 (0.465) 0.010 (1.538) lnNODt−2 −0.002 (−0.320) −0.020 (−1.127) −0.004 (−0.238) 0.054 (0.986) 0.109* (1.699) 0.257* (3.299) −0.002 (−0.254) lnNSEASIt−1 0.127 (1.455) −0.095229 (−0.40547) 0.457221 (1.93784) −0.327279 (−0.44842) −1.826236 (−2.1384) 2.690449 (2.58740) 0.759094 (8.92865) lnNSEASIt−2 −0.107 (−1.271) 0.136 (0.600) −0.579 (−2.543) 0.774 (1.100) 1.966 (2.386) −1.501 (−1.496) 0.174 (2.123) C −0.056 (−0.191) 0.487 (0.619) −1.913 (−2.465) 7.509 (3.169) 9.049 (3.270) −2.196 (−0.630) −0.283 (−0.996) R-squared 0.984 0.624 0.973 0.783 0.675 0.540 0.985 Adj. R-squared 0.982 0.591 0.970 0.764 0.647 0.500 0.984 Sum sq. resids 1.232 8.992 9.075 86.837 118.897 176.262 1.178 S.E. equation 0.091 0.245 0.246 0.761 0.890 1.084 0.089 F-statistic 691.532 19.126 412.563 41.674 24.003 13.534 772.771 Log likelihood 168.358 5.385 4.629 −180.567 −206.334 −238.619 172.029 Akaike AIC −1.882 0.105 0.114 2.373 2.687 3.081 −1.927 Schwarz SC −1.618 0.370 0.379 2.637 2.952 3.345 −1.663 Mean dependent 3.827 3.265 7.861 23.346 21.078 10.862 9.875 S.D. dependent 0.679 0.383 1.431 1.567 1.499 1.533 0.701 Table 3 is Oil Price per barrel. The underlying cointegrated VAR model is of order 2, contains unrestricted intercepts, and lag order was selected using Akaike information criterion (AIC). Standard errors generated from none replications and factorization is based on Cholesky Decomposition. We do capture the out of sample dynamics in the subsequent impulse responses. Table 2: Stationarity results Variable aAugmented Dickey‑Fuller (ADF) aPhillips and Perron (PP) Order of Integration lnOPPB −19.32647*** −18.99767*** 1 (1) %∆OPPB −11.36989*** −94.20518*** 1 (1) lnAPPB −14.42972*** −30.30106*** 1 (1) lnMC −12.70675*** −28.63905*** 1 (1) lnMVALUE −12.44347*** −38.51012*** 1 (1) lnMVOL −13.98515*** −84.53476*** 1 (1) lnNOD −11.13418*** −100.0612*** 1 (1) lnNSEASI −15.98697*** −15.89726*** 1 (1) Source: Researcher’s Estimation using E-View. lnAPPB: Natural log of Average closing oil price per barrel in US$, lnOPPB: Natural log of Oil Price per barrel in US$ at month-end, %∆OPPB: Percentage change in Oil Price per barrel in US$, lnMC: Natural log of Market capitalization in Billion Naira, lnNSEASI: Natural log of Nigerian Stock Exchange All-Share Index, lnMVOL: Natural log of the Market volume of trade, lnMVAL: Natural log of Market value of trade in Naira, lnNOD: Natural log of Number of deals or trades Nwude, et al.: The Influence of Oil Price Fluctuations on Stock Market of Developing Economies: A Focus on Nigeria International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021104 oil price per barrel on other variables recorded weak influence, which implies that the variable is strongly exogenous. Our observation for market capitalization revealed that this variable is weakly endogenous and least exogenous, that is, natural log of market capitalization has a weak influence on itself and strong influence from NSE all-share index and crude oil price. The outcome for the natural log of the market value of trade was the same as that of market capitalization. Similarly, we also observed that natural log of market volume of trade and the natural log of deals traded are weakly endogenous, least exogenous and strongly endogenous to other variables. NSE all-share index has a significant influence on itself which implies that this variable is strongly endogenous but has a strongly exogenous on other employed variables, that is, NSE all-share index has a weak influence on dependent variables. The results align with existing literature. In the diagnostic tests conducted, it was observed that most of the variables used are not normally distributed and heteroscedastic. 4.3.2. VAR model estimates using percentage change in oil price per barrel The VAR estimation, as revealed in Table 4, depicted significant outcomes. We observed that percentage variation in the price per barrel of oil recorded a weak influence on itself on lag 1 and 2. This is an indication that percentage variation in the price per barrel of oil is weakly endogenous when lagged by 2 periods. The percentage variation in the price per barrel of oil appeared to have strong endogeneity on the average closing oil price per barrel, natural log of market capitalization, and the natural log of NSE all-share index. This implies that it has a strong influence on these highlighted variables but a weak influence on the other variables. For the estimation on the average closing oil price per barrel, we ascertained that this variable is weakly endogenous, which implies that average closing oil price per barrel has a weak influence on itself on the lagged period. The influence of average closing oil price per barrel on the other variables shows that the variable is strongly exogenous, indicating a weak influence on the dependent variables and other variables. Table 4: VAR model estimates using percentage change in oil price per barrel %∆OPPB lnAPPB lnMC lnMVALUE lnMVOL lnNOD lnNSEASI %∆OPPBt−1 0.044* (0.554) −0.002*** (−0.903) 0.003*** (1.370) −0.001*** (−0.183) 0.004*** (0.549) −0.003*** (−0.355) 0.001*** (1.066) %∆OPPBt−2 −0.064* (−0.836) 0.001*** (0.534) 0.002*** (1.021) 0.005*** (0.762) 0.006*** (0.796) 0.012*** (1.224) 0.002*** (2.915) lnAPPBt−1 −2.518 (−0.909) 0.458* (5.909) 0.058* (0.746) −0.065 (−0.276) 0.192 (0.708) 0.0181 (0.053) 0.066** (2.435) lnAPPBt−2 −3.585 (−1.288) 0.357* (4.575) 0.003* (0.038) 0.036 (0.150) 0.073 (0.266) −0.778 (−2.256) −0.027 (−1.007) lnMCt−1 1.453 (0.475) 0.052* (0.610) 0.230* (2.702) 0.087 (0.336) −0.029 (−0.095) −0.217 (−0.574) 0.030** (1.021) lnMCt−2 −2.484 (−0.781) −0.043* (−0.480) 0.620* (7.001) 0.280 (1.037) 0.565 (1.809) 0.037 (0.095) −0.051 (−1.659) lnMVALUEt−1 0.051 (0.048) 0.002** (0.072) 0.030** (1.014) 0.324* (3.590) 0.149 (1.430) 0.098 (0.744) 0.015*** (1.463) lnMVALUEt−2 −0.002 (−0.002) 0.012** (0.401) 0.021** (0.685) 0.211* (2.295) 0.199 (1.872) −0.125 (−0.932) 0.008*** (0.751) lnMVOLt−1 0.571 (0.660) −0.007** (−0.271) 0.003** (0.112) 0.107* (1.454) 0.269* (3.163) −0.003 (−0.024) −0.004*** (−0.509) lnMVOLt−2 −0.286 (−0.33) −0.005** (−0.219) 0.033** (1.369) −0.050* (−0.679) −0.053* (−0.632) 0.062 (0.586) 0.010*** (1.234) lnNODt−1 −0.296 (0.479) −0.002** (−0.130) 0.046** (2.684 0.048* (0.909) −0.010* (−0.169) 0.033* (0.427) 0.009*** (1.486) lnNODt−2 −0.622 (−0.992) −0.020** (−1.116) −0.007** (−0.390) 0.064* (1.200) 0.121* (1.965) 0.259* (3.334) −0.001 (−0.205) lnNSEASIt−1 8.122 (0.974) −0.120 (−0.512) 0.505 (2.174) −0.308 (−0.436) −1.794 (−2.191) 2.662 (2.578) 0.757* (9.337) lnNSEASIt−2 −5.594 (−0.657) 0.150 (0.631) −0.429 (−1.808) 0.158 (0.219) 1.112 (1.329) −1.276 (−1.209) 0.212* (2.559) C 7.705 (0.277) 0.372 (0.476) −2.094 (−2.699) 7.147 (3.021) 8.864 (3.238) −2.712 (−0.785) −0.397 (−1.464) R-squared 0.076 0.625 0.974 0.796 0.700 0.543 0.987 Adj. R-squared −0.011 0.590 0.971 0.777 0.671 0.500 0.985 Sum sq. resids 11318.08 8.879 8.776 81.584 109.214 173.739 1.070 S.E. equation 8.7155 0.244 0.243 0.740 0.856 1.080 0.085 F-statistic 0.870 17.757 393.541 41.573 24.781 12.652 777.826 Log likelihood −579.918 6.425 7.379 −175.451 −199.368 −237.436 179.945 Akaike AIC 7.255 0.105 0.093 2.323 2.614 3.078 −2.012 Schwarz SC 7.539 0.388 0.376 2.606 2.898 3.362 −1.728 Mean dependent 1.884 3.269 7.860 23.346 21.072 10.851 9.869 S.D. dependent 8.667 0.381 1.430 1.567 1.493 1.527 0.697 Table 4 is the Percentage change in Oil Price per barrel. The underlying cointegrated VAR model is of order 2, contains unrestricted intercepts, and lag order was selected using Akaike information criterion (AIC). Standard errors generated from none replications and factorization is based on Cholesky Decomposition. We do capture the out of sample dynamics in the subsequent impulse responses Nwude, et al.: The Influence of Oil Price Fluctuations on Stock Market of Developing Economies: A Focus on Nigeria International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021 105 Table 5: Variance decompositions using oil price per barrel Horizon S.E. lnOPPB lnAPPB lnMC lnMVALUE lnMVOL lnNOD lnNSEASI Shock to lnOPPB, explained by innovations in 1 0.091 100.00 0.000 0.000 0.000 0.000 0.000 0.000 4 0.201 94.390 1.720 1.324 1.490 0.037 0.160 0.880 8 0.2684 84.249 7.179 2.570 4.515 0.107 0.134 1.246 16 0.340 68.770 14.885 4.757 8.142 0.304 0.851 2.291 Shock to lnAPPB, explained by innovations in 1 0.245 0.0003 100.00 0.000 0.000 0.000 0.000 0.000 4 0.325 0.031 98.697 0.216 0.316 0.027 0.611 0.102 8 0.376 0.148 96.990 0.303 1.283 0.028 1.162 0.087 16 0.412 0.550 93.902 0.659 3.152 0.024 1.372 0.341 Shock to lnMC, explained by innovations in 1 0.246 0.202 0.032 99.766 0.000 0.000 0.000 0.000 4 0.355 2.823 0.281 89.149 1.121 0.248 4.825 1.554 8 0.456 4.937 0.326 83.216 4.348 0.577 5.441 1.155 16 0.587 9.343 1.858 71.236 9.521 0.929 5.972 1.141 Shock to lnMVALUE, explained by innovations in 1 0.761 0.004 5.599 0.142 94.254 0.000 0.000 0.000 4 0.972 0.175 4.431 0.227 91.769 2.390 0.782 0.226 8 1.085 0.922 3.581 0.278 88.908 2.407 1.453 2.452 16 1.230 3.811 2.805 0.396 79.701 2.025 1.538 9.723 Shock to lnMVOL, explained by innovations in 1 0.890 1.355 0.0002 0.434 16.443 81.768 0.000 0.000 4 1.084 1.204 0.077 1.288 31.896 62.600 0.878 2.056 8 1.170 1.307 0.270 1.119 38.872 54.090 1.420 2.922 16 1.280 3.139 0.539 1.007 40.847 45.362 1.462 7.643 Shock to lnNOD, explained by innovations in 1 1.084 0.080 0.081 0.122 0.013 0.019 99.685 0.000 4 1.183 0.436 2.047 0.387 0.837 0.075 90.685 5.534 8 1.235 0.989 5.424 0.704 0.970 0.086 84.310 7.517 16 1.287 1.651 7.487 0.782 1.853 0.098 78.114 10.015 Shock to lnNSEASI, explained by innovations in 1 0.089 2.172 0.526 5.984 1.551 0.309 0.381 89.077 4 0.163 7.740 2.349 7.802 7.226 0.265 2.063 72.555 8 0.227 9.795 1.938 5.483 16.666 0.234 2.354 63.530 16 0.322 11.922 1.304 2.939 27.047 0.316 2.284 54.188 Table 5 is oil Price per barrel. The underlying cointegrated VAR model is of order 2, contains unrestricted intercepts, and lag order was selected using Akaike information criterion (AIC). Standard errors generated from none replications and factorization is based on Cholesky Decomposition. We do capture the out of sample dynamics in the subsequent impulse responses Market capitalization results, the market value of share traded, the market volume of share traded, number of deals, and the NSE all- shares index are weakly endogenous on themselves for the lagged period, which implies that the variables have weak influence on themselves. However, these variables recorded weak influence on other employed variables which is an indication that the variables are strongly exogenous. Though except for the market volume of share traded and several deals that recorded strong influence on NSE all-shares index, which implies that these variables are strongly endogenous with NSE all-shares index. For the validity of VAR results, the researchers carried out diagnostic tests. Most of the employed variables are not normally distributed, and the result showed the presence of heteroscedasticity. The outcomes for variance decompositions for our first model in both the short and the long horizons showed that price per barrel of oil is a strong predictor of itself but does not predict other variables as the total forecasted values for all the variables in the whole period is less than the predicted value of itself in the first period. This outcome for oil price per barrel is in line with our outcome for VAR estimation where we found oil price per barrel to be strongly endogenous on itself and strongly exogenous on other variables. In the same pattern, average closing oil price per barrel is a strong predictor of itself and does not predict other variables. This outcome did not deviate with our observation on VAR estimation. Market capitalization followed the same pattern; as a result, showed that this variable is a strong predictor of itself and does not predict other variables. This outcome did not deviate with our observation on VAR estimation. However, the outcomes for the remaining employed variables followed the same pattern as we observed that these variables are a strong predictor of themselves, and they do not forecast the outcomes of the other variables. In Table 6 as shown above, in both short and long-run horizon, we ascertained that percentage change in oil price per barrel predict itself and does not forecast the short-run and long-run variation of other employed variables. Also, average closing oil price per barrel predict itself and does not predict variation in other employed variables. In the same pattern, market capitalization, the market value of share traded, the market volume of share traded, number of deals and NSE all-share index predicted the variation of themselves. Still, these variables do not forecast the outcomes Nwude, et al.: The Influence of Oil Price Fluctuations on Stock Market of Developing Economies: A Focus on Nigeria International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021106 Table 6: Variance Decompositions for the percentage change in oil price per barrel Horizon S.E. %∆OPPB lnAPPB lnMC lnMVALUE lnMVOL lnNOD lnNSEASI Shock to %∆OPPB, explained by innovations in 1 8.716 100.00 0.000 0.000 0.000 0.000 0.000 0.000 4 8.938 95.444 2.556 0.579 0.172 0.225 0.485 0.539 8 8.995 94.241 3.674 0.660 0.174 0.222 0.487 0.541 16 9.024 93.643 4.202 0.715 0.190 0.221 0.491 0.538 Shock to lnAPPB, explained by innovations in 1 0.244 0.395 99.605 0.000 0.000 0.000 0.000 0.000 4 0.321 1.073 97.857 0.254 0.043 0.120 0.534 0.119 8 0.362 1.036 97.266 0.348 0.122 0.175 0.952 0.100 16 0.383 0.988 96.853 0.569 0.290 0.178 1.025 0.097 Shock to LNMC, explained by innovations in 1 0.243 0.303 0.044 99.652 0.000 0.000 0.000 0.000 4 0.345 1.857 0.301 84.635 5.268 1.119 4.307 2.513 8 0.452 2.600 0.207 73.754 12.805 1.775 5.364 3.496 16 0.596 3.837 0.367 60.772 19.718 1.999 6.601 6.705 Shock to lnMVALUE, explained by innovations in 1 0.740 0.014 6.692 0.940 92.354 0.000 0.000 0.000 4 0.870 0.258 6.498 3.220 86.519 1.106 2.267 0.131 8 0.930 0.591 6.524 7.408 80.134 1.230 3.786 0.327 16 1.003 1.278 6.519 12.110 72.536 1.427 4.716 1.415 Shock to lnMVOL, explained by innovations in 1 0.856 0.722 5.68E-06 0.008 12.987 86.284 0.000 0.000 4 0.968 0.677 0.112 2.995 18.553 73.039 1.906 2.717 8 1.015 0.700 0.218 7.849 19.070 66.589 3.007 2.567 16 1.072 0.905 0.561 13.149 19.274 59.983 3.722 2.405 Shock to lnNOD, explained by innovations in 1 1.080 0.059 0.042 0.035 0.002 0.049 99.813 0.000 4 1.184 1.613 1.610 0.138 1.233 0.257 89.772 5.378 8 1.240 2.410 4.263 0.261 1.959 0.310 82.786 8.011 16 1.299 3.197 5.272 0.265 3.390 0.311 75.822 11.744 Shock to lnNSEASI, explained by innovations in 1 0.085 0.964 0.472 5.214 2.246 0.345 0.124 90.634 4 0.160 6.825 2.725 5.656 10.613 0.395 1.398 72.387 8 0.226 8.520 2.109 4.599 18.508 0.326 1.859 64.078 16 0.312 9.219 1.399 4.472 23.792 0.321 2.534 58.262 Table 6 is the percentage change in Oil Price per barrel. The underlying cointegrated VAR model is of order 2, contains unrestricted intercepts, and lag order was selected using Akaike information criterion (AIC). Standard errors generated from none replications and factorization is based on Cholesky Decomposition. We do capture the out of sample dynamics in the subsequent impulse responses of the other variables. Though the forecasted values themselves, and that of other variables vary significantly. These highlighted results for variance decompositions are in line with our observation on VAR estimation on the employed variables. The results are consistent with existing literature. The results of variance decomposition analysis and impulse response function provide the same conclusions regardless of the order of decomposition since their estimation is independent of the ordering. 4.4. Implication of the Results Figures 1 and 2 plot the responses of each of the employed variables to a one standard error shock in the other variable. This is presented in the appendix section. The figures show that variations in the price of crude oil in the market are slowly transmitted to some selected stock market variables. The Nigerian stock market responds to the global crude oil price shock some months after the shock. The response to the shock may be attributed to inflation and foreign exchange policy of the nation. These results show the inefficiency of the Nigeria stock market in transmitting shocks in the international crude oil market. The situation is also reflected in the international crude oil market as the outcomes of our VAR estimations and variance decompositions indicate. The insignificant responses of the selected stock market performance variables to price shock in international crude oil market show the weak influence of the selected stock market variables in Nigeria in the international crude oil market. The result is consistent with that of Mordi et al. (2010), Al Hayky and Naim (2016) and Ojikutu et al. (2017). 5. CONCLUSIONS This study examined the effect of oil price shock on selected performance variables in the Nigerian stock market. Vector autoregression (VAR) analysis was carried on monthly data for the period, January 1, 1997, to December 31, 2016. This study utilized variance decomposition and impulse response analysis to compliment VAR estimations for the models. In line with the existing empirical literature, the results from VAR estimation Nwude, et al.: The Influence of Oil Price Fluctuations on Stock Market of Developing Economies: A Focus on Nigeria International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021 107 revealed that international crude oil price is strongly exogenous to Nigerian stock market performance variables, which indicated that the oil price fluctuations in the international crude oil market have weak influence on stock market performance variables in Nigeria. The results from the variance decomposition analysis also indicate a very weak relationship between the crude oil price shocks and stock market variables in Nigeria. In the international crude oil market, the impulse analysis reveal that variation in oil price is slowly transmitted to the Nigeria stock market. It is also established that crude oil price in the Nigerian capital market is greatly minimized, and the effect does not sufficiently account for changes in the stock market activities. REFERENCES Abbas, T., Terfa, W.A. (2010), The Impact of Oil Price Volatility on the Nigerian Stock Market: Evidence from Autoregressive Distributed Lag Model. A Conference Paper Presented at Nasarawa State University. Adaramola, A.O. (2012), Oil price shocks and stock market behavior: The Nigerian experience. Journal of Economics, 3(1), 19-24. Adebiyi, M.A., Adenuga, A.O., Abeng, M.O., Omanukwue, P.N. (2010), Oil Price Shocks, Exchange Rate and Stock Market Behavior: Empirical Review from Nigeria. Available from: http://www.africanetics.org/document/conference09/papers/ adebiyi͜ adenuga͜abeng ͜omanukwue.pdf. Akinlo, O.O. (2014), Oil prices and stock market: Empirical evidence from Nigeria. European Journal of Sustainable Development, 3(2), 33-40. Akomolafe, K.J., Danladi, J.D. (2014), Oil price dynamics and the Nigerian stock market: An industry-level analysis. International Journal of Economics, Finance and Management, 3(6), 1-9. Al Hayky, A., Naim, N. (2016), The relationship between oil price and stock market index: An empirical study from Kuwait. In: Presented at Middle East Economic Association 15th International Conference. Babatunde, M.A., Adenikinju, O., Adenikinju, A.F. (2013), Oil price shocks and stock market behaviour in Nigeria. Journal of Economic Studies, 40(2), 180-202. Basher, S., Haung, A., Sadorsky, P. (2012), Oil prices, exchange rates and emerging stock markets. Energy Economics, 34(1), 227-240. Cunado, J., Perez de Gracia, F. (2003), Do oil price shocks matter? Evidence from some European countries. Energy Economics, 25, 137-154. Effiong, E.L. (2014), Oil Shocks and Nigeria Stock Market: What have we Learned from Crude Oil Market Shocks? Oxford: John Wiley and Sons Ltd., OPEC. p36-38. Faff, R.W., Brailsford, T.J. (1999), Oil price risk and the Australian stock market. Journal of Energy Finance and Development, 4(1), 69-87. Hamilton, J.D. (1983), Oil and the macro-economy since World War II. Journal of Political Economy, 91, 228-248. Huang, R., Musulis, R., Stoll, H. (1996), Energy shocks and financial markets. Journal of Futures Markets, 16(1), 1-27. Iheanacho, E. (2016), The dynamic relationship between crude oil price, exchange rate and stock market performance in Nigeria. International Multidisciplinary Journal, 10(4), 224-240. Jones, C.M., Kaul, G. (1996), Oil and the stock markets. Journal of Finance, 51, 463-491. Kilian, L., Park, C. (2009), The impact of oil price shocks on the US stock market. International Economic Review, 50(4), 1267-1287. Lawal, A.I., Babajide, A.A., Nwanji, T.I., Eluyela, D. (2018), Are oil prices mean reverting? Evidence from unit root tests with sharp and smooth breaks. International Journal of Energy Economics and Policy, 8(6), 292-298. Magyereh, A.I., Awartani, B., Bouri, E. (2016), The directional volatility connectedness between crude oil and equity markets: New evidence from implied volatility indexes. Energy Economics, 57, 78-93. Mordi, C.N.O., Michael, A., Adebiyi, A.M. (2010), The asymmetric effects of oil price shock on output and prices in Nigeria using a structural VAR model. Economic and Financial Review, 481, 1-32. Nandha, M., Faff, R. (2008), Does oil move equity prices? A global view. Energy Economics, 30, 986-997. Obi, B., Oluseyi, A.S., Olaniyi, E. (2018), Impact of oil price shocks on stock market prices volatility in Nigeria: New evidence from a non- linear ARDL co-integration. Journal of Global Economy, 14(3), 1-17. Ojikutu, O.T., Onolemhemhen, R.U., Isehunwa, S.O. (2017), Crude oil price volatility and its impact on Nigeria stock market performance (1985-2014). International Journal of Energy Economics and Policy, 7(5), 302-311. Okany, C.T. (2014), Effect o0f oil price movement on stock prices in the Nigerian equity market. Research Journal of Finance and Accounting, 5(15), 1-15. Omisakin, O., Adeniyi, O., Omojolaibi, A. (2009), A vector error correction modelling of energy price volatility of an oil-dependent economy: The case of Nigeria. Pakistan Journal of Social Sciences, 6(4), 207-213. Papapetrou, E. (2001), Oil price shocks, stock market, economic activity and employment in Greece. Energy Economics, 23(5), 511-532. Park, J., Ratti, R.A. (2008), Oil price shocks, stocks market in the U.S. and 13 European countries. Energy Economics, 30(5), 2587-2068. Sadorsky, P. (1999), Oil price shocks and stock market activity. Energy Economics, 21, 449-469. Soyemi, K.A., Akingunola, R.O., Ogebe, J. (2017), Effects of oil price shock on stock returns of energy firms in Nigeria. Kasetsart Journal of Social Sciences, 30, 1-8. U.S. Energy Administration. (2018), List of Countries by Oil Production. Available from: https://www.en.wikipedia.org/wiki/list-of-counries- by-oil-production. Uwubanmwen, A.E., Omorokunwa, O.G. (2015), Oil price volatility and stock price volatility: Evidence from Nigeria. Academic Journal of Interdisciplinary Studies, 4(1), 253. Zhang, D. (2017), Oil Shocks and stock markets re-visited: Measuring connectedness from a global perspective. Energy Economics, 62, 323-333. Nwude, et al.: The Influence of Oil Price Fluctuations on Stock Market of Developing Economies: A Focus on Nigeria International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021108 APPENDIx Figure 1: Impulse response for model 1 (oil price per barrel) Nwude, et al.: The Influence of Oil Price Fluctuations on Stock Market of Developing Economies: A Focus on Nigeria International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021 109 Figure 2: Impulse response for model 2 (percentage change in oil price per barrel)