Oil price & stock market performance in Nigeria: An empirical analysis

AMERICAN JOURNAL OF SOCIAL AND MANAGEMENT SCIENCES ISSN Print: 2156-1540, ISSN Online: 2151-1559, doi:10.5251/ajsms.2013.4.1.20.41 2013, ScienceHuβ, http://www.scihub.org/ajsms Oil price & stock market performance in Nigeria: An empirical analysis 1 Itotenaan Henry Ogiri, 2 S.N. Amadi, 3 Moshfique M. Uddin and 4 P. Dubon 1 School of Accountancy & Finance, Leeds Metropolitan University, United Kingdom, 2 Dept. of Banking and Finance, Rivers State University of Science & Technology, Port Harcourt, Nigeria 3 Dept. of Accounting, University of York, United Kingdom 4 Dept. of Accounting & Finance Rivers State Polytechnic Bori, Nigeria ABSTRACT This paper investigates the relationship between oil prices and stock market performance in Nigeria. Different empirical methods including the Johansen s cointegration model, the augmented Dickey-Fuller test, the Vector error correction (VEC) model, as well as the Vector auto regression (VAR) estimation model, were used in the study. Our results suggest that oil price changes are important factors in explaining stock price movement. Specifically, our findings show that there are significant links between oil prices and stock market performance. The results of this study have implications for policy makers and stock market regulators on ways to achieve economic development sustainability in the long run via the use of oil revenue and energized stock market. Key words: Oil price, stock market, Volatility, cointegration, INTRODUCTION Following the discovery of crude oil by Shell D, Arcy Petroleum, pioneer production began in 1958 from the company s oil field in Oloibiri in the Eastern Niger Delta. By the late sixties and early seventies, Nigeria had attained a production level of over 2 million barrels of crude oil a day. Although production figures dropped in the eighties due to economic slump, 2004 saw a total rejuvenation of oil production to a record level of 2.5 million barrels per day. Petroleum production and export play a dominant role in Nigeria s economy and account for about 90% of her gross earnings. This dominant role pushed agriculture, the traditional mainstay of the economy, from early fifties and sixties, to the background. (NNPC, 2009). As of January 1, 2009, the estimated crude oil and natural gas reserve are, respectively, 36.2 billion and 182.4 trillion cubic feet (tcf), and ranks as the 12 th biggest oil producing country of the world with a 2.4m bpd or 3.1% of the estimated world total in 2008 (Liveoilprices, 2008). An unresolved research issue in stock market is the role played by oil prices. If you ask a layman about the relationship between the price of oil and the stock market, the expected answer would be: the relationship is negative. This kind of view is also shared by the financial press. But in reality, the relationship is much more complicated than this simple answer. It depends on the circumstances and the prevailing state of the macro economy (Hammoudeh, 2009). While most prior research on the relationship between oil prices and stock market could not find strong and convincing evidence to support the proposition that oil prices drive the stock market returns (Pescatori and Mowry, 2008., Fisher,2005., Agusman and Deriantino, 2008., Weiner,2005 and Apergis and Miller, 2009), yet a number of studies have found evidence to the contrary. Some of the studies that shows that a relationship exists between oil prices and the stock market include those of Sadorsky (2008), Aloui and Jamazi (2009), El-Sharif et al (2005), Hammoudeh (2009), Gogineni (2007) and Rault and Aruori (2009). It is against this backdrop that this study was carried out to find out whether oil price volatility affects stock market performance in Nigeria. The term volatility has been given different definitions by different scholars across disciplines. In relation to crude oil price, volatility is the measure of the tendency of oil price to rise or fall sharply within a period of time, such as a day, a month or a year. Most oil price movements, especially up to the mid- 1980s' and earlier, consisted in price increases. However, the pattern has changed. There are large price increases and decreases reflecting a substantial rise in the volatility of the real oil price which creates market uncertainties that induce companies to postpone their investments (Sauter and

Awerbuch, 2003). A number of factors have been identified as triggers of oil price volatility. These factors range from demand and supply of crude oil, OPEC decisions, crises, wars to economic downturn. Using a traditional approach to assessing the tightness of the oil market, Merino and Ortiz (2005) contended that the evolution of oil inventories should reflect the interaction between supply and demand forces, and therefore, should contribute to explaining oil price changes. Oil is the life blood of modern economics and when countries urbanized and modernized, demand for oil increases (Eryigit, 2009). Unanticipated economic developments could, in principle, roil crude oil markets and increases volatility. Examples include the unexpected surge in energy demand from China and India, which helped to draw down worldwide buffer stocks, and the decline in the trade weighted value of the U.S dollar increase in the volatility of oil prices and the average price of crude oil (Guo and Kliesen, 2005). A long term explanatory factor for increasing oil price could be the decline of the world reserve base. Factors such as political unrest like that experienced by oil producing Venezuela and Nigeria, OPEC quota system decisions as well as speculative buying and selling all affect prices as financial traders adjust their investment portfolios to reflect market conditions (Pirog, 2004). The fear of global shortage of crude oil may also account for changes in oil price. As noted by Appenzeller (2004), there have been diverse arguments about how much more of crude oil reserve the world has before the wells dry up. Whereas historical oil price shocks were primarily caused by physical disruptions of supply, the price run-up of 2007-2008 was caused by strong demand confronting world production (Hamilton, 2009; Cale, 2004). Mabro (2001) cited the market's perceptions of the state of OPEC's solidarity as well as the dynamics of futures markets as the causes behind violent oil price movements. The emergence of various militants groups in oil producing countries of the world and political crises have also caused increases in oil prices. For instance, the concerns about violence in Nigeria and Algeria occasioned by attacks on oil facilities by militants, as well as delay of elections in Pakistan following the assassination of the former Pakistani Prime Minister Benazir Bhutto increased oil prices in early January 2008 (BBC News, 2008). Another known cause of oil price volatility was the Iraqi War. The invasion of Kuwait by Iraq on August 2, 1990 was quickly followed by an oil embargo which had an immediate impact on oil prices (Lee et al, 1996). The correlation between oil prices and real output has received considerable attention especially for most industrialized countries. One explanation for such a relationship is that oil price increases lower future GDP growth by raising production costs (Papapetrou, 2009). In a study of the relationship between oil prices and economic activity in Greece during the period 1982 to 2008, Papapetrou (2009), applying a regime-switching model ( RS-R) and a threshold regression modeling (TA-R), obtained empirical evidence which suggest that the degree of negative correlation between oil prices and economic activity strengthens during periods of rapid oil price changes and high oil price change volatility (see also Varjavand et al, 2008). Despite the many debate on the effect of oil price volatility, extant studies show, however, that there is still no consensus as to the exact effects of oil price volatility on economy ( Schmidbauer and Kalaycioglu, 2008). In their study of the effects of oil price shocks on the Kazakh economy, Gronwald et al (2009) found that the price of oil is influenced by a variety of factors such as global economic development, OPEC power, and speculative behavior, which results in a considerable degree of uncertainty in the market. According to Ewing and Thompson (2007), oil prices are often considered an important input or determinant of movements in a variety of macroeconomic variables. One area that has received considerable attention, especially, in relation to oil price volatility is stocks. But what impact does oil price volatility has on stock market performance? Stocks (or shares or equity) are financial instruments that are traded in the stock market with a view to earn interest or dividend income in addition to ensuring capital growth (Arnold, 2004). The link between oil prices and macroeconomics through the financial markets has recently been explored in a growing body of research. However, there seems to be no conclusion on the type and direction of the relationship between financial variables and changes in oil prices (Sari and Soytas, 2006). The identification of the forces that drive stock returns and the dynamics of their associated volatilities is a major concern in empirical economics and finance (Giovannini et al, 2004). While there is strong presumption in the financial press that oil prices drive the stock market, the empirical evidence on the impact of oil price shocks on stock prices has been mixed (Kilian and Park, 21

2007). Recent empirical research has found evidence of a relationship between oil price movements and stock prices (Sadorsky, 2008). For instance, findings by Aloui and Jammazi (2009) show that rises in oil price play a significant role in determining both the volatility of stock returns and the probability of transition across regimes. According to Aloui and Jammazi (2009), if crude oil is a decisive determinant of economic growth, then increases in crude oil market prices will be significantly linked to the firm s expected earnings and consequently their stock price levels. As studies show that oil prices have a significant effect on both economic activity and price indexes, its volatility is presumed to have an impact on stock market performance as well (Cunado and Gracia, 2004). Again, since oil price volatility is presumed to also lead to stock market price changes, investors who hold a market portfolio (e.g. a stock market index fund) may face systematic risk i.e stock market volatility (Guo, 2002). Guo (ibid) argue further that because stock market volatility and output affect cost of capital, an increase in stock market fluctuations raises the compensation the shareholders demand for bearing systematic risk. The higher expected return leads to the higher cost of equity capital in the corporate sector, which reduces investment and output. For Anoruo and Mustafa (2007), the fact that oil and stock market returns are co-integrated (rather than segmented), is an indication of causality that runs from stock market to oil market but not vice versa. Taking the results from the above findings together provides evidence in support of the view that the two markets are integrated rather than segmented. Since stock values ideally, reflect the market s best estimate of the future profitability of firms, the effect of oil price shocks on the stock market is a meaningful and useful measure of their economic impact. Furthermore, as asset prices are the present discounted value of the future net earnings of firms, both the current and the expected future impacts of an oil price shock should be absorbed fairly quickly into stock prices and returns, without having to wait for those impacts to actually occur (Jones et al, 2004). Papapetrou (2001), and Masih et al (2011) also supported the argument that oil prices are important in explaining stock price movements. Another puzzle about oil price volatility is the fact that changes in oil prices strongly predict future stock market returns in many countries of the world. Driesprong et al (2003) in their study found a statistically significant predictability in 12 out of 18 countries and in a world market index. In a recently conducted empirical analysis by Hwang (2011), the findings show that oil price and industrial production shocks explain significant portion of the fluctuations in stock price movement. Similarly, Arouri et al (2010) study indicated that stock market returns significantly react to oil price changes in some Gulf Corporation Council (GCC) countries, thus providing evidence of a relationship between oil price volatility and stock market performance (see also, Jawadi and Leoni, 2008; Constantinos et al, 2010; Arouri and Rault, 2009). As a corollary to the foregoing, market commentators and journalists like to draw direct lines between the behaviour of crude oil prices and market behaviour on a given day, with such headlines as oil spike pummels stock market or U.S stocks rally as oil prices fall (Pescatori and Mowry, 2008). These empirical findings notwithstanding, some scholars have maintained that fluctuations in oil prices do not have any relationship with stock market performance. In a study of 22 emerging economies (Nigeria not included), Maghyereh (2004) findings imply that oil shocks have no significant impact on stock index returns in emerging economies. Similarly, in Ghana, findings by Adjasi (2009) show that higher volatility in Cocoa prices and interest rates increased volatility of the stock prices, whilst higher volatility in gold prices, oil prices, and money supply reduced volatility of stock prices. Furthermore, Agren (2006) argue that the stock market's own shocks, which are related to other factors of uncertainty than the oil price, are more prominent in explaining stock price movements. While some authors have maintained that it is not in all stock volatility cases that are oil price change-induced, Fisher (2005) in his (Stop fretting about oil) study of investment analysis quipped: Don't believe these stories. There is no connection between the two... So why do we hear so often something like the market fell today because oil rose. (p. 1). Fisher (ibid) argued that there is no credible cause - and-effect relationship between oil and stocks, and so investors should buy good stocks and hold them irrespective of what he calls investor sentiment as measured by the Investors intelligence data. Most of the empirical studies carried out have focused on the oil-importing economies, particularly the developed economies, whereas few studies exist on the effect of oil price shock on key 22

macroeconomic variables for an oil exporting country like Nigeria (Olomola and Adejumo 2008). METHODOLOGY AND DATA ANALYSIS The study adopts econometric data analysis procedure. The research philosophy adopted for this study is the positivist nomothetic methodology. It focused on empirical facts. Hypotheses were formulated and tested based on the study variables. A quantitative, deductive and objective approach was used which involved the formulation of models of the relationship amongst the variables tested. Tests conducted include: (i) Cointegration test using the Johansen s model to test for cointegration to ascertain the movement of the variables. (ii) The Augmented Dickey-Fuller (ADF) this is a unit root test which was used to test the hypothesis, both in stationary and non stationary forms. (iii) Vector Error Correction (VEC) Mechanism to correct any disequilibrium which may arise from the study variables. The research was carried out in Nigeria with a focus on the oil sector and the capital market indices including: -Stock Price movement -Market Capitalization -Number of listed securities In this study, oil price movement is the main regressor i.e. the independent variable. The stock market performance is the main regressand in this study. Its indices include market capitalization and number of listed securities. The moderating variables are the GDP, Exchange rate, monetary policy rate and cumulative private investment. These variables are introduced because they have a strong contingent impact on the dependent-independent variables relationship. Basically, it is realized that the major regressor regressands do not have a direct relationship. For instance, higher oil prices, for an oil exporting country like Nigeria, will generate higher earnings (revenue) which will boost the economy. This will cause an appreciation of the value of the firms in which the enhanced earnings have been channeled. Not only that, a boost in economic activities could as well translate to higher investment in the real sector and an increase in the country s GDP, which by extension could increase stock price. Again, investment will boost the performance of any industry so affected. Furthermore, exchange rate will impact positively on stock performance. The MPR, INV, GDP and EXR in the models are all intervening variables. The models on share price, market capitalization and number of listed securities are all presumed to have some relationship with oil price changes. In particular, higher oil prices will lead to greater investment in securities and, therefore, lead to increase in number of listed securities because of greater participation by investors as a result of increase in investible funds in the system. Again, oil price will have a direct effect on GDP, Investment, Interest rate, and Exchange rate. This effect will be transmitted to the stock market, via the indicators mentioned above. The effect could be positive or negative, depending on the nature of the impact which oil price will have on the macroeconomic indices. The present state of the Nigerian economy, as a mono-product economy that is dependent on crude oil, influences the choice of the study variables. The study is a time series and macroeconomic one which tested the relationship between oil price volatility and stock performance. Stocks and oil price (Brent crude) data were analyzed for the period between 1980 to 2009 (i.e. a 30 year period ), using a vector auto regression (VAR) model to compare the stock market indices with oil price volatility to see if there is any relationship. The Johansen s model was used to test for cointegration to ascertain the movement of the variables. Johansen s methodology takes its starting point in the Vector auto regression (VAR) of order p given by; Yt = µ+a1yt-1 +.. + ApYt-p + et Where; Yt is an nx1 vector of variables that are integrated of order one i.e. 1(1), and et is an nx1 vector of innovations. To achieve this, differencing or transformation was used to reduce the series to stationarity, and then to reduce the resulting series as VARs. In analyzing time series data, it was assumed that the time series is stationary. Therefore, test of stationarity was done to determine whether or not the time series data are stationary. To perform a multiple regression analysis 23

when the time series data are non-stationary could give spurious or nonsense regressions (Gujarati, 2003). The reason for reducing the series to stationarity is to ensure that the series do not drift apart indefinitely thereby violating the theoretical relationship of the variables (Hung, 2003). The shortrun (OLS regression) and long-run (cointegration) techniques were used to analyze the short-run and long-run relationships between the variables. To correct any disequilibrium which may arise from these variables, the error correction mechanism was used so that a proportion of the disequilibrium is corrected in the next period. To determine the appropriate lags, the Akaike Information Criterion (AIC) or Schwarz Information Criterion (SIC) of the Vector Auto regression (VAR) was employed. When we use a regression model involving time series data, it may happen that there is a structural change in the relationship between the regressand y and the regressors (Gujarati, 2003). This means that the values of the parameters of the model do not remain the same through the entire time period. To ensure this does not affect the regression, a test for the structural relationships in the variables using the Chow s test was done. The unit root test or the test of order of integration was conducted using the Augmented Dickey-Fuller (ADF). The ADF, as a twostep procedure test, was used firstly to test the null hypothesis that the variables in their un-differenced (level) form are non-stationary, integrated order of one, 1(1). Secondly, it tested the null hypothesis that the variables in the first differenced form are stationary, integrated of order zero, 1(0). The decision rule for ADF is: Reject Ho (accept HA) if t-adf (absolute value) > t-adf (critical value). Additional tests The following tests were also carried out in this study: T-test; tested the significance of the regression coefficients. If the t value computed exceeds the critical value at the chosen level of significance, we reject the null hypothesis of insignificance of the coefficient, otherwise we do not reject it. F-test; tested the overall significance of the model i.e. their goodness of fit. Decision rule is that if the computed F exceeds the critical F at the chosen level of significance, we reject the null hypothesis; otherwise we do not reject it. The r squared (R²) test tested for multiple coefficient of determination. The R² lies between 0 and 1. The closer it is to 1, the better is the goodness of fit. Model Specification: The following models of the capital market indicators were specified for this study: i) 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 The theoretical expectation is (a0 a5 > 0) In the above model, SP depend on the variables (independent) on the left hand side of the argument. The functional relationship between sp and op is stated as sp = f(op). Op basically influenced the macroeconomic variables chosen in the study. ii) Market Capitalization Model, represented in the functional form as MC = f(op, GDP, EXR, INV, MPR); and its regression model is stated as; MC = a0 + a1op + a2 GDP + a3exr+ a4inv + a5mpr + µ2 Where, MC = Market Capitalization OP = Oil price GDP = Gross Domestic Product EXR = Exchange Rate INV = Investment MPR = Monetary Policy Rate µ2 = Stochastic Error term The theoretical expectation is (a0 a5 > 0) iii) Number of Listed Securities model, functionalized as NLC = f(op, GDP, EXR,INV,MPR); and its regression model is stated as ; NLC = a0 + a1op + a2 GDP + a3exr+ a4inv 24

+ a5mpr + µ3 Where, NLC = number of listed securities OP = Oil price GDP = Gross Domestic Product EXR = Exchange Rate INV = Investment MPR = Monetary Policy Rate Table 4.1 The SP Equation Dependent Variable: SP Method: Least Squares Sample: 1980 2009 Included observations: 30 µ3 = Stochastic Error term The theoretical expectation is (a0 a5 > 0) Computational Device: The Eviews7 software was used for my data analysis. Statistical tests have been run to ensure that observed results are those that relate specifically to the impact of oil price volatility on stocks in Nigeria. Empirical results: The results of the analysis of the three regression models of SP equation, MC equation and NLC equation are hereby presented in the following tables: Variable Coefficient Std. Error t-statistic Prob. C 0.323687 0.294621 1.098654 0.2828 OP 0.011080 0.004124 2.686374 0.0129 GDP -0.004657 0.001687-2.761040 0.0109 EXR -0.001112 0.001207-0.921254 0.3661 INV 0.116202 0.032206 3.608136 0.0014 MPR -0.017135 0.008974-1.909456 0.0682 R-squared 0.800319 Mean dependent var 0.330333 Adjusted R-squared 0.758718 S.D. dependent var 0.412991 S.E. of regression 0.202863 Akaike info criterion -0.175714 Sum squared resid 0.987683 Schwarz criterion 0.104525 Log likelihood 8.635711 Hannan-Quinn criter. -0.086063 F-statistic 19.23831 Durbin-Watson stat 0.999701 Prob(F-statistic) 0.000000 From table 4.1 of the SP equation, OP and the two predictor variables i.e (GDP & INV) are significant as their tail probabilities of the t-statistic are 0.0129, 0.0109 and 0.0014 respectively which are all less than 0.05. 25

Table 4.2: The MC Equation Dependent Variable: MC Method: Least Squares Sample: 1980 2009 Included observations: 30 Variable Coefficient Std. Error t-statistic Prob. C -12.83861 12.68814-1.011859 0.3217 OP 0.404381 0.177623 2.276627 0.0320 GDP 0.225636 0.072645 3.105999 0.0048 EXR -0.026097 0.051998-0.501886 0.6203 INV -1.885832 1.386967-1.359681 0.1866 MPR 0.144251 0.386455 0.373267 0.7122 R-squared 0.811831 Mean dependent var 11.54200 Adjusted R-squared 0.772629 S.D. dependent var 18.32187 S.E. of regression 8.736494 Akaike info criterion 7.349751 Sum squared resid 1831.832 Schwarz criterion 7.629991 Log likelihood -104.2463 Hannan-Quinn criter. 7.439402 F-statistic 20.70900 Durbin-Watson stat 2.115539 Prob(F-statistic) 0.000000 For the MC equation as show in table 4.2 only OP and one intervening variable (GDP) are significant with tail probabilities of the t-statistic at 0.0320 and 0.0048 respectively. 26

Table 4.3: The NLC Equation Dependent Variable: NLC Method: Least Squares Sample: 1980 2009 Included observations: 30 Variable Coefficient Std. Error t-statistic Prob. C 69.19434 26.29363 2.631601 0.0146 OP -1.250069 0.368088-3.396119 0.0024 GDP 0.656898 0.150543 4.363523 0.0002 EXR 0.630308 0.107756 5.849387 0.0000 INV -2.347068 2.874211-0.816595 0.4222 MPR 2.168549 0.800851 2.707806 0.0123 R-squared 0.893413 Mean dependent var 149.4333 Adjusted R-squared 0.871208 S.D. dependent var 50.44812 S.E. of regression 18.10464 Akaike info criterion 8.807070 Sum squared resid 7866.668 Schwarz criterion 9.087309 Log likelihood -126.1060 Hannan-Quinn criter. 8.896721 F-statistic 40.23378 Durbin-Watson stat 1.732026 Prob(F-statistic) 0.000000 For NLC equation in the table above, only investment with a tail probability of the t - statistic of 0.4222 is not significant. The OP and the other moderating variables (GDP, EXR, INV & MPR) show significant tail probabilities of the t statistic of 0.0024, 0.0002, 0.0000 & 0.0123 respectively. Table 4.4: Augmented Dickey-Fuller Unit Root Test on SP Null Hypothesis: SP has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=0) The Augmented Dickey-Fuller unit root test was also carried out on SP, MC and NLC; the results are as shown on tables 4.4, 4.5 and 4.6 below: t-statistic Prob.* Augmented Dickey-Fuller test statistic -3.416983 0.0185 Test critical values: 1% level -3.679322 5% level -2.967767 10% level -2.622989 *MacKinnon (1996) one-sided p-values. 27

Augmented Dickey-Fuller Test Equation Dependent Variable: D(SP) Method: Least Squares Sample (adjusted): 1981 2009 Included observations: 29 after adjustments Variable Coefficient Std. Error t-statistic Prob. SP(-1) -0.185687 0.054343-3.416983 0.0020 C 0.015355 0.028920 0.530955 0.5998 R-squared 0.301889 Mean dependent var -0.047586 Adjusted R-squared 0.276033 S.D. dependent var 0.141109 S.E. of regression 0.120065 Akaike info criterion -1.335101 Sum squared resid 0.389219 Schwarz criterion -1.240805 Log likelihood 21.35897 Hannan-Quinn criter. -1.305569 F-statistic 11.67577 Durbin-Watson stat 1.977996 Prob(F-statistic) 0.002021 The ADF statistic value is -3.417 and the associated one-sided P-value (for a test with 29 observations) is 0.0185. The critical values at the 1%, 5% and 10% levels are -3.679, -2.968 and -2.623 respectively. At Table 4.5: Augmented Dickey-Fuller Unit Root Test on MC 5% level of -2.968, SP with an ADF t - statistic of - 3.417 (i.e. < - 2.968) does not have a unit root meaning there is stationarity. Null Hypothesis: MC has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=7) t-statistic Prob.* Augmented Dickey-Fuller test statistic -1.582648 0.4784 Test critical values: 1% level -3.679322 5% level -2.967767 10% level -2.622989 *MacKinnon (1996) one-sided p-values. 28

Augmented Dickey-Fuller Test Equation Dependent Variable: D(MC) Method: Least Squares Date: 04/27/10 Time: 15:30 Sample (adjusted): 1981 2009 Included observations: 29 after adjustments Variable Coefficient Std. Error t-statistic Prob. MC(-1) -0.189700 0.119863-1.582648 0.1251 C 3.135380 2.498506 1.254902 0.2203 R-squared 0.084894 Mean dependent var 1.090345 Adjusted R-squared 0.051001 S.D. dependent var 11.82115 S.E. of regression 11.51576 Akaike info criterion 7.791782 Sum squared resid 3580.543 Schwarz criterion 7.886078 Log likelihood -110.9808 Hannan-Quinn criter. 7.821314 F-statistic 2.504776 Durbin-Watson stat 1.599450 Prob(F-statistic) 0.125146 The ADF statistic value is -1.583 and the associated one-sided P-value (for a test with 29 observations) is 0.478. The reported critical values at 1%, 5% and 10% are -3.679, -2.968 and -2.623 respectively. At Table 4.6: Augmented Dickey-Fuller Unit Root Test on NLC Null Hypothesis: NLC has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=7) 5% critical level of -2.968, MC with an ADF t - statistic of - 1.583 (i.e. greater than -2.968) has a unit root, meaning there is no stationarity i.e. t is significant. t-statistic Prob.* Augmented Dickey-Fuller test statistic -0.958158 0.7544 Test critical values: 1% level -3.679322 *MacKinnon (1996) one-sided p-values. 5% level -2.967767 10% level -2.622989 29

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 Variable Coefficient Std. Error t-statistic Prob. NLC(-1) -0.049871 0.052049-0.958158 0.3465 C 12.06894 8.079407 1.493790 0.1468 R-squared 0.032884 Mean dependent var 4.724138 Adjusted R-squared -0.002935 S.D. dependent var 13.72666 S.E. of regression 13.74679 Akaike info criterion 8.145959 Sum squared resid 5102.302 Schwarz criterion 8.240256 Log likelihood -116.1164 Hannan-Quinn criter. 8.175492 F-statistic 0.918067 Durbin-Watson stat 2.439742 Prob(F-statistic) 0.346485 The ADF statistic value is -0.958 and the associated one-sided P-value (for a test with 29 observations) is 0.754. The reported critical values at 1%, 5% and 10% are -3.679, -2.968 and -2.623. At 5% critical level of -2.968, NLC with an ADF t - statistic of -0.958 (i.e. greater than - 2.968) has a unit root, meaning there is no stationarity i.e. t is significant. Vector Auto regression (VAR) estimates were made firstly with intercept C and OP as exogenous variables and secondly, with intercept C as the only exogenous variable. Both results are presented on tables 4.7 and 4.8 below: 30

Table 4.7:Vector Autoregression Estimates with Intercept C and OP as Exogenous Variables. Sample (adjusted): 1982 2009 Included observations: 28 after adjustments Standard errors in ( ) & t-statistics in [ ] SP MC NLC SP(-1) 0.814217 4.154085 8.769694 (0.21806) (17.3692) (17.4486) [ 3.73394] [ 0.23916] [ 0.50260] SP(-2) -0.063404-3.100811-7.819722 (0.19833) (15.7975) (15.8696) [-0.31970] [-0.19629] [-0.49275] MC(-1) 0.000980 0.286043-0.142462 (0.00362) (0.28831) (0.28963) [ 0.27077] [ 0.99212] [-0.49187] MC(-2) -0.002651-0.140715 0.078500 (0.00285) (0.22680) (0.22784) [-0.93109] [-0.62044] [ 0.34455] NLC(-1) 0.001177 0.159614 0.546579 (0.00199) (0.15867) (0.15939) [ 0.59085] [ 1.00597] [ 3.42917] NLC(-2) -0.001363-0.077937 0.349376 (0.00193) (0.15405) (0.15476) [-0.70481] [-0.50592] [ 2.25760] OP 0.001417 0.580218 0.127024 (0.00248) (0.19723) (0.19813) [ 0.57236] [ 2.94189] [ 0.64112] C 0.022622-19.87625 20.64214 (0.16344) (13.0186) (13.0781) [ 0.13841] [-1.52676] [ 1.57838] R-squared 0.870553 0.795903 0.963940 Adj. R-squared 0.825246 0.724469 0.951319 Sum sq. resids 0.307575 1951.485 1969.359 S.E. equation 0.124011 9.877969 9.923101 F-statistic 19.21476 11.14180 76.37657 Log likelihood 23.42707-99.14826-99.27590 Akaike AIC -1.101934 7.653447 7.662564 Schwarz SC -0.721304 8.034077 8.043194 Mean dependent 0.252143 12.18357 156.0714 S.D. dependent 0.296652 18.81839 44.97483 Determinant resid covariance (dof adj.) 126.7049 Determinant resid covariance 46.17524 Log likelihood -172.8450 Akaike information criterion 14.06036 Schwarz criterion 15.20225 31

Table 4.8:Vector Autoregression Estimates with Intercept C as the only Exogenous Variable Sample (adjusted): 1982 2009 Included observations: 28 after adjustments Standard errors in ( ) & t-statistics in [ ] SP MC NLC SP(-1) 0.817815 5.627066 9.092165 (0.21445) (20.2809) (17.1950) [ 3.81355] [ 0.27746] [ 0.52877] SP(-2) -0.051157 1.913176-6.722037 (0.19399) (18.3457) (15.5542) [-0.26372] [ 0.10428] [-0.43217] MC(-1) 0.002532 0.921621-0.003319 (0.00236) (0.22302) (0.18908) [ 1.07392] [ 4.13251] [-0.01755] MC(-2) -0.003276-0.396492 0.022504 (0.00259) (0.24469) (0.20746) [-1.26611] [-1.62039] [ 0.10847] NLC(-1) 0.001308 0.213229 0.558317 (0.00195) (0.18411) (0.15610) [ 0.67181] [ 1.15813] [ 3.57666] NLC(-2) -0.001329-0.063929 0.352442 (0.00190) (0.17986) (0.15250) [-0.69872] [-0.35543] [ 2.31115] C 0.023614-19.47007 20.73106 (0.16079) (15.2065) (12.8927) [ 0.14686] [-1.28038] [ 1.60797] R-squared 0.868433 0.707583 0.963199 Adj. R-squared 0.830842 0.624035 0.952685 Sum sq. resids 0.312614 2795.964 2009.833 S.E. equation 0.122010 11.53868 9.782961 F-statistic 23.10233 8.469198 91.60667 Log likelihood 23.19961-104.1825-99.56071 Akaike AIC -1.157115 7.941605 7.611479 Schwarz SC -0.824064 8.274656 7.944530 Mean dependent 0.252143 12.18357 156.0714 S.D. dependent 0.296652 18.81839 44.97483 Determinant resid covariance (dof adj.) 177.0893 Determinant resid covariance 74.70954 Log likelihood -179.5813 Akaike information criterion 14.32724 Schwarz criterion 15.32639 32

Table 4.9 below show the results of Johansen Cointegration Test for the series SP, MC and NLC. Table 4.9: Johansen Cointegration Test for the Series SP, MC and NLC 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 Unrestricted Cointegration Rank Test (Trace) Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.568426 32.58109 29.79707 0.0233 At most 1 0.306708 9.892558 15.49471 0.2891 At most 2 8.65E-05 0.002335 3.841466 0.9594 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 Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.568426 22.68854 21.13162 0.0300 At most 1 0.306708 9.890222 14.26460 0.2192 At most 2 8.65E-05 0.002335 3.841466 0.9594 Max-eigenvalue 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 33

Unrestricted Cointegrating Coefficients (normalized by b'*s11*b=i): SP MC NLC -1.979990-0.071030 0.021076-4.314613-0.003232-0.036237-2.042513 0.345057-0.034578 Unrestricted Adjustment Coefficients (alpha): D(SP) 0.066901 0.011797-7.89E-05 D(MC) 2.784461-2.048223 0.084865 D(NLC) -0.495092 3.329008-0.007934 1 Cointegrating Equation(s): Log likelihood -146.0297 Normalized cointegrating coefficients (standard error in parentheses) SP MC NLC 1.000000 0.035874-0.010645 (0.03391) (0.00412) Adjustment coefficients (standard error in parentheses) D(SP) -0.132464 (0.02845) D(MC) -5.513207 (4.60624) D(NLC) 0.980277 (2.76479) 2 Cointegrating Equation(s): Log likelihood -141.0846 Normalized cointegrating coefficients (standard error in parentheses) SP MC NLC 1.000000 0.000000 0.008805 (0.00241) 0.000000 1.000000-0.542157 (0.10806) Adjustment coefficients (standard error in parentheses) D(SP) -0.183364-0.004790 (0.06700) (0.00100) D(MC) 3.324080-0.191160 (10.8163) (0.16201) D(NLC) -13.38310 0.024407 (5.54951) (0.08312) The trace test indicates 1 cointegration of 0.023, and since this is < 0.05 level the hypothesis is rejected. That shows SP is stationary. The maximum eigenvalue test also indicates 1 cointegration of 0.030. Again, hypothesis is rejected because 0.030 is < 0.05. To correct any disequilibrium which may arise from the study variables, the results of the ECM estimates are presented in table 4.10: 34

Table 4.10: Vector Error Correction Estimates for the Series SP, MC and NLC with C and OP as Exogenous Variables Sample (adjusted): 1983 2009 Included observations: 27 after adjustments Standard errors in ( ) & t-statistics in [ ] Cointegrating Eq: CointEq1 SP(-1) 1.000000 MC(-1) 0.031130 (0.02249) [ 1.38436] NLC(-1) -0.002508 (0.00274) [-0.91498] C -0.226877 Error Correction: D(SP) D(MC) D(NLC) CointEq1-0.210695-3.624501-3.408233 (0.04770) (6.61564) (4.61981) [-4.41690] [-0.54787] [-0.73774] D(SP(-1)) -0.011375 10.46805 2.273545 (0.11852) (16.4371) (11.4783) [-0.09597] [ 0.63685] [ 0.19807] D(SP(-2)) -0.272292 11.17156 6.186320 (0.11821) (16.3941) (11.4483) [-2.30346] [ 0.68144] [ 0.54037] D(MC(-1)) 0.007270-0.597602 0.288794 (0.00279) (0.38726) (0.27043) [ 2.60355] [-1.54314] [ 1.06790] D(MC(-2)) 0.009570-1.049530 0.197151 (0.00328) (0.45503) (0.31775) [ 2.91692] [-2.30651] [ 0.62045] D(NLC(-1)) 0.004192 0.272085-0.029829 (0.00163) (0.22631) (0.15804) [ 2.56890] [ 1.20225] [-0.18875] D(NLC(-2)) 0.005981 0.173669-0.072608 (0.00132) (0.18288) (0.12771) [ 4.53562] [ 0.94962] [-0.56854] C -0.201674-12.67328 7.988568 (0.03997) (5.54272) (3.87057) [-5.04616] [-2.28647] [ 2.06392] OP 0.001866 0.558028-0.126687 (0.00132) (0.18280) (0.12765) [ 1.41583] [ 3.05270] [-0.99245] R-squared 0.770921 0.509357 0.154195 Adj. R-squared 0.669107 0.291294-0.221718 Sum sq. resids 0.099616 1916.005 934.3324 S.E. equation 0.074392 10.31721 7.204676 F-statistic 7.571919 2.335821 0.410188 Log likelihood 37.31935-95.85051-86.15528 35

Akaike AIC -2.097729 7.766705 7.048539 Schwarz SC -1.665784 8.198650 7.480485 Mean dependent -0.034074 1.190370 4.444444 S.D. dependent 0.129325 12.25544 6.518219 Determinant resid covariance (dof adj.) 21.93680 Determinant resid covariance 6.499794 Log likelihood -140.2029 Akaike information criterion 12.60762 Schwarz criterion 14.04744 SP 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 Fig.Plot of SP (1980-2009) 36

MC 90 80 70 60 50 40 30 20 10 0 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 Fig 4.2: Plot of MC (1980-2009) NLC 240 200 160 120 80 40 0 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 Fig 4.3: Plot of NLC (1980-2009) 37

DISCUSSIONS OF FINDINGS A. Regression Models A1 Model 1 SP = 0.324 + 0.011 OP - 0.005 GDP-0.001 EXR + 0.116NV-0.017 MPR (0.28) (0.01) (0.01) (0.36) (0.001) (0.07) R 2 = 0.76 Prob (F) = 0.00 A2 Model 2 MC = 12.84 + 0.40 OP + 0.22 GDP 0.03 EXR 1.88 INV + 0.14 MPR (0.32) (0.03) (0.00) (0.62) (0.19) (0.71) R 2 = 0.77 Prob (F) = 0.00 A3 Model 3 NLC = 69.19-1.25 OP + 0.65 GDP + 0.63 EXR 2.35 INV + 2.17MPR (0.01) (0.00) (0.00) (0.00) (0.42) (0.01) R 2 = 0.87 Prob (F) = 0.00 The regressors OP, GDP, EXR, INV and MPR have jointly explained the dependent variable. The joint impact is significant in each case (see Model 1, 2 and 3) since Prob (F) =0.00 < α = 0.5, where Prob (F) is the tail probability of the F statistic and α is the level of significant of the test. In Model 1, only OP, GDP and INV are significant as the tail probabilities of their t statistic written in parentheses are less than α = 0.50 (See table 4.). In particular, Op (petrol prices) significantly explains stock prices (SP). We see here that oil prices are relevant in changes that occur in stock prices. The same observation is seen in Models 2 and 3. In Model 2, significant coefficients are that of OP and GDP (see table 4.2) whereas in Model 3, all coefficients are significant with the exception of investment (INV) coefficient. See table 4.3. Stationary test (unit root test): The series SP, MC and NLC have been tested for stationarity. The null hypothesis in each case is of the form; HO: Series has a unit root. Vs Hi: Series does not have a unit root while the null hypothesis was accepted in the case of MC and NLC; it was rejected in the case of SP. Hence SP does not have a unit root, implying that it is stationary. We make use of the tail probability of the augmented Dickey-Fuller Test Statistic.See tables 4.4, 4.5 and 4.6. For SP the ADF statistic value is -3.417 and the associated one-sided P-value (for a test with 29 observations) is 0.0185. The reported critical values at the 1%, 5% and 10% levels are -3.679, -2.968 and -2.623 respectively. At 5% level of -2.968, SP with an ADF t - statistic of - 3.417 (i.e < - 2.968) does not have a unit root meaning there is stationarity. For MC the ADF statistic value is -1.583 and the associated one-sided P-value (for a test with 29 observations) is 0.478. The reported critical values at 1%, 5% and 10% are - 3.679, -2.968 and -2.623 respectively. At 5% level of - 2.968, MC with an ADF t - statistic of - 1.583 (i.e. greater than -2.968) has a unit root. And for NLC the ADF statistic value is -0.958 and the associated onesided P-value (for a test with 29 observations) is 0.754. The reported critical values at 1%, 5% and 10% are -3.679, -2.968 and -2.623. At 5% level of - 2.968, NLC with an ADF t - statistic of -0.958 (i.e. greater than - 2.968) has a unit root. Vector Autoregression (Var): Two sets of VAR were constructed and estimated. The first included SP, MC, NLC with OP. and constant C as exogenous variables. The second VAR omitted OP. The information criteria used to assess improvement of one model over the other are Akaike Information Criterion (AIC) and Schwarz Criterion, also known as Bayesian Information Criterion (BIC). The first model that incorporated OP has AIC and BIC values, respectively as 14.06 and 15.20 (see table 4.7).The second VAR specification that excludes OP has values of AIC and BIC of 14.33 and 15.33 respectively (see table 4.8). The set (14.06, 15.20) has smaller values than the set (14.33, 15.33).This shows that the model with OP incorporated is a better model than the VAR model without OP. The smaller the information criteria; the better the model. Cointegration: The series SP, MC and NLC have been tested, in each case for stationarity. Results show that only SP has no unit root and as such is stationary. The series MC and NLC having been found with unit roots can be said not to be stationary. From here, there is a motivation to see if there is a long-run relationship among the series that is stationary. Johansen cointegration test (See Table 4.9) shows that there is cointegration at a significance level of 0.05. For instance, from table 4.9 under review the trace test indicates 1 cointegration of 0.023, and since this is < 0.05 level the hypothesis 38

is rejected. That shows SP is stationary. The maximum eigenvalue test also indicates 1 cointegration of 0.030. Again, hypothesis is rejected because 0.030 is < 0.05. Vector Error Correction (Vec): Error correction mechanism is another way of testing for cointegration. If there is cointegration, which establishes a long-run property of the data, then there exists an ECM which is an appropriate way of capturing dynamic adjustment to that long-run relationship. This assertion we furnished by granger representation theorems: if variables are cointegrated, then the relationship between the variables can be expressed as an ECM (Gujarati, 2003). The F statistic for the SP equation in the VEC output is 7.57. For MC and NLC equations, the F statistic respectively take the values 2.34 and 0.41. The SP equation in ECM is significant as F = 7.57 is greater than the critical value of F (9,18) = 2.42 at a significant level 0.05. The t value for the coefficients of OP in SP, MC and NLC equation in the VEC output are respectively 1.42, 3.05 and -0.99. Since the critical value of t is 2.42 at a significant level of 0.05, then OP co-efficient in MC equation is significant, it is not significant in SP and NLC equations. The series NLC shows a rising trend in general for the period of study. Comments On Plots Of Sp, Mc And Nlc: The series SP shows a decline between 1981 and 1983 with rise from 1983 to 1984. From 1984, it keeps declining until 1994. Again, goes up between 1994 and 1998 goes down from 1998 to 2000. The series appears to be steady between 2000 and 2004. However, it begins to rise steadily from 2004 until 2008. The MC variable seems to be almost steady between 1981 and 1994 with a small crest between 1994 and 1999. With a steady growth between 2000 and 2004, the series shows a speedy and steep rise between 2004 and 2006. The behavior of the series between 2004 and 2006 may be linked with reform in the banking sector by the then Central Bank Governor, Professor Charles Soludo. CONCLUSION There is a growing body in the literature on the relationship between oil prices and the stock markets. However, very few studies have been conducted in this area for an oil exporting, developing country like Nigeria who relies on oil sales for its revenue. This paper investigated the relationship between oil prices and stock market returns in Nigeria. The examination of the relationship between oil price movements and stock market returns is achieved through a formal empirical frame work. Daily data were converted into yearly figures for the periods January 1980 to December 2009 i.e. 30 years. Predictor variables such as gross domestic product (GDP), investment (INV), interest rate (MPR) and exchange rate (EXR) were introduced into the model equations since they have a strong contingent impact on the dependent independent variables relationship. The empirical findings of this paper suggest that changes in oil prices affects stock market performance in Nigeria. The results are consistent with findings (by Sadorsky, 2008; Aloui and Jamazi, 2009; Papapetrou, 2001 and Masih et al, 2011) that oil prices are important in explaining stock price movements. The result of this study may have implications for policy makers, particularly in Nigeria, on the ways to achieve economic development sustainability in the long run via the use of oil revenue and energized stock market. Further research might usefully examine the relationship between oil prices and stock markets in a different economic and industrial setting like sector indices. A study on oil prices and stock markets: a granger causality analysis may also be imperative. Furthermore, since volatility study has to do with high frequency data, weekly or monthly data may be used in future studies to see if it will produce different results from the analyzed data in this study. REFERENCES Adjasi, C (2009) Macroeconomic uncertainty and conditional stock-price volatility in frontier African Markets: Evidence from Ghana. Journal of risk finance Vol. 10 (4) PP333-349. Agren, M (2006) Does oil price uncertainty transmit to stock markets? University of Uppsala, Sweden. WorkingPaper No. 23 October, Agusman, A and Deriantino, E (2008) oil price and Industry stock returns: Evidence from Indonesia. 21 st Australian Finance and Banking conference 2008. Paper. AI Fayoumi, N (2009) oil prices and stock market Returns in Oil Importing Countries: The case of Turkey, Tunisia and Jordan. European Journal of Economics, Finance and Administrative Sciences. Issue 16. Aloui, C., and Jammazi, R (2009): The effects of crude oil shocks on stock markets shifts behaviours: A regime switching approach. Internal finance group Tunisia, faculty of management and Economic sciences of Tunis Anoruo, E., and Mustafa, M (2007) An empirical 39

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