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REAL STOCK RETURNS, VOLATILITY AND REAL ECONOMIC ACTIVITY: EVIDENCE FROM NIGERIA RUFUS AYODEJI OLOWE1 This paper uses the VAR model to investigate the inter relationship between real economic activity, real stock returns, real economic activity volatility and real stock return volatility in Nigeria using quarterly data over the period 1985.2 to 2009.3 in the light of financial reforms, stock market crash and the global financial crisis. EGARCH (1, 2) and EGARCH (1, 3) were used to estimate real economic activity and real stock return volatilities respectively. The results showed that there is no causal relationship between real economic activity and real stock returns. This is consistent with the results of Binswanger (2000, 2004) and Mao and Wu (2007). The results also show there is a one-way causality going through from real economic activity to real economic activity volatility; real economic activity to real stock return volatility; real stock returns to real stock return volatility; and real economic activity volatility to real stock return volatility. The results showed that past volatilities in real stock returns and real economic activity negatively influenced real economic activity volatility. The results also show that private sector credit influences real economic activity. The result also shows that real treasury bill yield negatively influences real economic activity volatility. The results also show that the introduction of new capital requirement for banks and introduction of new capital requirement for insurance influences real stock returns. The result also show that the global financial crisis influences real stock return volatility. Policy makers should introduce policies towards increasing real output of companies in Nigeria. The quality of financial reporting of companies in Nigeria should also be enhanced. Field of Research: Stock market, Economic Activity, EGARCH, VAR, Granger causality, Financial Reforms, Global Financial crisis, Stock market crash JEL: O40, G1 1. INTRODUCTION The debate on whether the stock market can serve an important indicator for the prediction of future economic activity or vice versa has been well documented. Most of the authors focused on the inter relationship between stock prices and real economic activity. However, the dramatic change in stock market volatility during financial crises (such as the 1987 stock market crash, the 1997 East Asia crisis, and the 1998 Russian financial crisis) and periods of uncertainty (such as the 1962 Cuban missile crisis) has made it necessary to examine also the impact of stock market volatility on the economy (Guo, 2002) . The recent global financial crisis and associated declines in economic activity experienced by a number of emerging market economies has made it imperative to 1 Associate Professor of [email protected] Finance, University of Lagos, Akoka, Lagos, Nigeria. E-mail: examine the links between stock prices, and real economic activity or output and their volatilities. Engle (1982) introduced the autoregressive conditional heteroskedasticity (ARCH) to model volatility. Engle (1982) modeled the heteroskedasticity by relating the conditional variance of the disturbance term to the linear combination of the squared disturbances in the recent past. Bollerslev (1986) generalized the ARCH model by modeling the conditional variance to depend on its lagged values as well as squared lagged values of disturbance, which is called generalized autoregressive conditional heteroskedasticity (GARCH). Since the work of Engle (1982) and Bollerslev (1986), the financial econometrics literature has been successful at measuring, modeling, and forecasting time-varying return volatility which has contributed to improved asset pricing, portfolio management, and risk management, as surveyed for example in Andersen, Bollerslev, Christoffersen and Diebold (2006a, 2006b). Schwert (1989a) argues that stock market volatility, by reflecting uncertainty about future cash flows and discount rates, provides important information about future economic activity. Campbell et al. (2001), citing work by Lilien (1982), reason that stock market volatility is related to structural change in the economy. Structural change consumes resources, which depresses gross domestic product (GDP) growth. Another link between stock market volatility and output rests on a cost-of-capital channel. That is, an increase in stock market volatility raises the compensation that 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. Consistent with these hypotheses about the link between stock market volatility and economic activity, Campbell et al. (2001) show that—after controlling for the lagged dependent variable— stock market volatility has significant predictive power for real GDP growth. Bhide (1993) argues that speculations and volatility in stock markets may reduce investment efficiency, which has detrimental effect to economic growth. Mauro (1995) indicate that the development of stock market will reduce economic growth through decreasing the public's precautionary savings. Guo (2002) shows that if the cost of capital is the main channel through which volatility affects future output, stock market returns have a more important role in forecasting economic activity than volatility does. Several empirical studies found a strong relationship between stock returns and real economic activity( Fama,(1981; Fischer and Merton,1984; Barro, 1990; Fama, 1990; Schwert, 1990; Peiro, 1996; Domian and Louton, 1997; Foresti, 2006; Choi et al., 1999 ; and Hassapis and Kalyvitis, 2002). Some other studies provided evidence that stock market performance is not correlated with real economic activity (Stock and Watson, 1990, 1998; Ffu, 1993). Binswanger (2000, 2004) and Mao and Wu (2007) argued that the that the relation between stock returns and real economic activity in the US has disappeared since the early 1980's indicating that stock return ceased to lead real economic activity. Most of the studies discussed so far are for developed economies. Few studies have been conducted for emerging economies on the interrelationship between stock returns and real economic activity. Rangvid (2001) provided evidence on the relation between stock return and real economic activity for several emerging economies. Mauro (2003) provided evidence for emerging and advanced countries while Kaplan (2008) provided evidence for Turkey. Little or no work has been done on the relation between stock returns or its volatility on real economic activity in Nigeria. This paper attempts to fill this gap. The recapitalization of the banking industry in Nigeria in July 2004 and the Insurance industry in September 2005 boosted the number of securities on Nigerian stock market increasing public awareness and confidence about the Stock market. The increased trading activity on the stock market could have affected the volatility of the stock market. However, since 1 April 2008, investors have been worried about the falling stock prices on the Nigerian stock market. The global financial crisis of 2008, an ongoing major financial crisis, could have affected stock volatility. The crisis which was triggered by the subprime mortgage crisis in the United States became prominently visible in September 2008 with the failure, merger, or conservatorship of several large United States-based financial firms exposed to packaged subprime loans and credit default swaps issued to insure these loans and their issuers. On September 7, 2008, the United States government took over two United States Government sponsored enterprises Fannie Mae (Federal National Mortgage Association) and Freddie Mac (Federal Home Loan Mortgage Corporation) into conservatorship run by the United States Federal Housing Finance Agency (Wallison and Calomiris, 2008; Labaton and Andrews, 2008). The two enterprises as at then owned or guaranteed about half of the U.S.'s $12 trillion mortgage market. This causes panic because almost every home mortgage lender and Wall Street bank relied on them to facilitate the mortgage market and investors worldwide owned $5.2 trillion of debt securities backed by them. Later in that month Lehman Brothers and several other financial institutions failed in the United States (Labaton, 2008). The crisis rapidly evolved into a global credit crisis, deflation and sharp reductions in shipping and commerce, resulting in a number of bank failures in Europe and sharp reductions in the value of equities (stock) and commodities worldwide. In the United States, 15 banks failed in 2008, while several others were rescued through government intervention or acquisitions by other banks (Letzing, 2008). The financial crisis created risks to the broader economy which made central banks around the world to cut interest rates and various governments implement economic stimulus packages to stimulate economic growth and inspire confidence in the financial markets. The financial crisis dramatically affected the global stock markets. Many of the world's stock exchanges experienced the worst declines in their history, with drops of around 10% in most indices ( Kumar, 2008). In the US, the Dow Jones industrial average fell 3.6%, not falling as much as other markets (Cox, 2008). The economic crisis caused countries to temporarily close their markets. This paper investigated the inter relationship between real economic activity, real stock returns, real economic activity volatility and real stock return volatility in Nigeria using quarterly data over the period 1986.4 and 2009.3 in the light of the structural adjustment programme, banking reforms, insurance reform, stock market crash and the global financial crisis. The rest of this paper is organised as follows: Section two discusses an overview of the Nigerian stock market while Section three discusses Theoretical background and literature. Section four discusses methodology while the results are presented in Section five. Concluding remarks are presented in Section six. 2. OVERVIEW OF THE NIGERIAN STOCK MARKET The Nigerian Stock Exchange (NSE) which started operation in 1961 with 19 securities has grown overtime. As at 1998, there are 264 securities listed on the NSE, made up of 186 equity securities and 78 debt securities. By 2008, the number of listed securities has increased to 301 securities made up of 213 equity securities and 88 debt securities. Table 1 highlights the trends in the number of listed securities on the Nigerian Stock market. Table 2 shows the trend in the trading transactions in the Nigerian stock market. Between 1980 and 1987, there was hardly any trading transaction on the equity market. Government and industrial loan stocks dominated the transactions on the Nigerian stock market (Olowe, 2008). Table 2 shows that the value of equity traded as a proportion of total value of all securities traded, equity traded as a proportion of total market capitalisation and equity traded as a proportion of GDP are all zero between 1971 and 1987. However, since 1988 the value of equity traded transaction has been increasing in the Nigerian stock market (Olowe, 2008). Table 2 shows that equity traded as a proportion of total value of all securities traded grew from 0.7348 in 1988 to 0.9988 in 1998 and to 0.9973 in 2007. Table 2 shows that between 1988 and 2005, the equity market is still small relative to the size of the stock market. The value of equity traded as a proportion of total market capitalisation was 0.0624 in 1988 but fell to 0.0022 in 1989. Since 1989, the value of equity traded as a proportion of total market capitalisation has been fluctuating rising slightly to 0.0516 in 1998, increasing to 0.1059 in 2004 and falling to 0.0878 in 2005. On July 4, 2004, Central Bank of Nigeria proposed banking reforms increasing the capitalisation of Nigerian banks to N25 billion. In the process of complying with the minimum capital requirement, N406.4 billion was raised by banks from the capital market, out of which N360 billion was verified and accepted by the CBN (Central Bank of Nigeria, 2005). The introduction of the 2004 bank capital requirements could have affected quoted securities on the Nigerian stock exchange. The recapitalisation of the Nigerian banking industry and influx of banking stocks into the Nigerian stock market made the value of equity traded as a proportion of total market capitalisation to increase to 0.1761 in 2008. Table 2 shows that the stock market is small relative to the size of the economy. The value of equity traded as a proportion of GDP was 0.0023 in 1988 but fell to 0.0001 in 1989. Since 1989, the value of equity traded as a proportion of GDP has been fluctuating rising slightly to 0.0033 in 1998, increasing to 0.0192 in 2004 and falling to 0.0171 in 2005. The recapitalisation of the Nigerian banking industry which led to influx of banking stocks into the Nigerian stock market made the value of equity traded as a proportion of GDP to increase to 0.0703 in 2008 (Olowe, 2008). In sum, prior to 2001, the equity market appears to be small in Nigeria considering the low values of both the value of equity traded as a proportion of total market capitalisation and the value of equity traded as a proportion of GDP. The growth in the equity could have due to some reasons such as introduction of universal banking, introduction of code of corporate governance, introduction of new capital requirements for banks and insurance companies. In response to the trend in the world financial system, the imperatives of globalisation and financial deregulation, in January 2001, the universal banking system was adopted in Nigeria. A universal bank is an all-inclusive bank which in addition to the traditional banking functions provides all other financial ser vices including insurance and capital market services. On January 1, 2001, the Central Bank of Nigeria recalled the existing licences of all the commercial and merchant banks and issued them with new, uniform licences. The new licences allow the banks to choose which segment(s) of the financial market (i.e., money market, capital market, insurance business or any combination of these) they wish to operate in, after considering and evaluating appropriately their own competencies. The adoption of universal banking led to an increase in number of financial institutions engaged in capital market services. The adoption of universal banking could have affected the relation between stock return or its volatility and the real economic activity. Table 1: Number of Securities Listed on the Nigerian Stock Exchange, 1980-2008 Year Equity Debt Total Securities Securities 1980 91 66 157 1981 93 70 163 1982 93 75 168 1983 93 86 179 1984 94 83 177 1985 96 85 181 1986 99 87 186 1987 100 85 185 1988 102 86 188 1989 111 87 198 1990 131 86 217 1991 142 97 239 1992 153 98 251 1993 174 98 272 1994 177 99 276 1995 181 95 276 1996 183 93 276 1997 182 82 264 1998 186 78 264 1999 196 73 269 2000 195 65 260 2001 194 67 261 2002 195 63 258 2003 200 65 265 2004 207 70 277 2005 214 74 288 2006 202 86 288 2007 212 98 310 2008 213 88 301 Source: Olowe (2009b) . Table 2: Year Trading Transactions on the Nigerian Stock Exchange Govt. Equities Total ET/TVT ET/TMC Securities and Industrial Loan 1980 388.7 388.7 0 0 1981 304.8 304.8 0 0 1982 215 215 0 0 1983 397.9 397.9 0 0 1984 256.5 256.5 0 0 1985 316.6 316.6 0 0 1986 497.9 497.9 0 0 1987 382.4 382.4 0 0 1988 225.5 624.8 850.3 0.7348 0.0624 1989 582.4 27.9 610.3 0.0457 0.0022 1990 158.5 66.9 225.4 0.2968 0.0041 1991 98.7 143.4 242.1 0.5923 0.0062 1992 91.7 400 491.7 0.8135 0.0128 1993 348.2 456.2 804.4 0.5671 0.0096 1994 192.3 793.6 985.9 0.8050 0.0120 1995 50.8 1,788.00 1,838.80 0.9724 0.0099 1996 62.8 6,916.80 6,979.60 0.9910 0.0242 1997 107.9 10,222.60 10,330.50 0.9896 0.0363 1998 15.8 13,555.30 13,571.10 0.9988 0.0516 1999 0.8 14,071.20 14,072.00 0.9999 0.0469 2000 8.1 28,145.00 28,153.10 0.9997 0.0596 2001 35.6 57,648.20 57,683.80 0.9994 0.0870 2002 2.6 59,404.10 59,406.70 1.0000 0.0777 2003 6520.1 113,882.50 120,402.60 0.9458 0.0838 2004 2047.5 223,772.50 225,820.00 0.9909 0.1059 2005 8252.7 254,683.10 262,935.80 0.9686 0.0878 2006 1665 468,588.40 470,253.40 0.9965 0.0915 2007 1136.5 1,074,883.90 1,076,020.4 0.9989 0.0809 2008 3528.9 1,675,609.80 1,679,138.7 0.9979 0.1761 0 Source: Olowe (2009b); Central Bank of Nigeria Annual 0Report and Accounts, Various issues. ET/GDP 0 0 0 0 0 0 0 0 0.0023 0.0001 0.0001 0.0003 0.0004 0.0004 0.0005 0.0006 0.0017 0.0036 0.0050 0.0044 0.0061 0.0122 0.0086 0.0134 0.0196 0.0175 0.0252 0.0520 0.0703 Notes: ET represents value of equity securities traded. TVT represents total value of all securities traded. TMC represents Total market capitalisation. GDP represents Gross domestic prices at current prices On September 5, 2005, the Federal Government of Nigeria announced the recapitalization of Insurance and Reinsurance companies as N2 billion for life insurance companies, N3 billion for non-life operators, N5 billion for composite insurance companies and N10 billion for re-insurers (NAICOM, 2008). In the process of complying with the minimum capital requirement, substantial money was raised by insurance companies from the capital market. The introduction of the new capital requirements for banks in 2004 and insurance companies in 2005 with the prospect of increase in volume of activities on the Nigerian stock market could have brighten the confidence of investors in the Nigerian economy and the stock market, thus, encouraging investment in capital market securities and increasing capital formation. They could also have affected the volatility of the stock market. The Nigerian Stock Exchange index has grown overtime. The index grew from 134.6 on January 1986 to 65005.48 by March 18, 2008 falling to 63016.56 by April 1, 2008. Since April 1, 2008, Nigerian stock exchange index has been falling. As at January 16, 2009, the Nigerian stock exchange index stood at 27108.54. Figure 1 shows the trend in the Nigerian Stock Exchange index over the period January 1986 to December 2008. Figure 1: Trend in the Nigerian Stock Exchange Index over the period, January 1986 to December, 2008 70000 60000 50000 40000 30000 20000 10000 0 86 88 90 92 94 96 98 00 02 04 06 08 Nigerian S toc k E xchang e Index 3. LITERATURE REVIEW The relationship between stock prices and real economic activity has been well documented. Various explanations have been offered as to different channels through which stock prices are connected to real activity. Morck et al. (1990) explanations imply that firms and managers base their investment decisions on information provided by the stock market and the stock prices reflect the present discounted value of all future dividends (see Kaplan (2008)). This implies that stock prices should lead the real activity as long as stock price movements are related to fundamentals (Kaplan, 2008). Greenwood and Smith (1997) argue that stock market performance affects real economic activity through lowering the cost of mobilizing savings and thereby facilitating investment in the most productive technologies. Levine (1991) and Benchivenga, Smith and Starr ( 1996) argue that the stock markets affects real economic activity by providing liquid capital through which they contribute to growth. Holmstrom and Tirole (1993) argue that the stock market affects real economic activity by providing increasing incentives to get information about firms to investors. Deveraux and Smith (1994) and Obstfeld (1994) argue that the stock markets affects real economic activity by providing opportunities for risk reduction through global diversification make high- risk-high return domestic and international projects viable, and , consequently, allocate savings between investment opportunities more efficiently. Mauro (2003) stock market affects real economic activity by increasing the wealth of investors and hence increasing consumption. The volatility of stock prices in the stock market has been of concern to researchers. Stock return volatility which represents the variability of stock price changes could be perceived as a measure of risk faced by investors. Shiller (1981) argues that stock prices are more volatile than what is justified by time variation in dividends. Similarly, Schwert (1989b) concludes that stock market volatility cannot be fully explained by changes in economic fundamentals. Engle (1982) introduced the autoregressive conditional heteroskedasticity (ARCH) to model volatility. Engle (1982) modeled the heteroskedasticity by relating the conditional variance of the disturbance term to the linear combination of the squared disturbances in the recent past. Bollerslev (1986) generalized the ARCH model by modeling the conditional variance to depend on its lagged values as well as squared lagged values of disturbance, which is called generalized autoregressive conditional heteroskedasticity (GARCH). Since the work of Engle (1982) and Bollerslev (1986), the financial econometrics literature has been successful at measuring, modeling, and forecasting time-varying return volatility which has contributed to improved asset pricing, portfolio management, and risk management, as surveyed for example in Andersen, Bollerslev, Christoffersen and Diebold (2006a, 2006b). The links between stock market volatility and real economic activity or output has been investigated. Schwert (1989a) argues that stock market volatility, by reflecting uncertainty about future cash flows and discount rates, provides important information about future economic activity. Campbell et al. (2001), citing work by Lilien (1982), reason that stock market volatility is related to structural change in the economy. Structural change consumes resources, which depresses gross domestic product (GDP) growth. Another link between stock market volatility and output rests on a cost-of-capital channel. That is, an increase in stock market volatility raises the compensation that 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. Consistent with these hypotheses about the link between stock market volatility and economic activity, Campbell et al. (2001) show that—after controlling for the lagged dependent variable— stock market volatility has significant predictive power for real GDP growth. Bhide (1993) argues that speculations and volatility in stock markets may reduce investment efficiency, which has detrimental effect to economic growth. Mauro (1995) indicate that the development of stock market will reduce economic growth through decreasing the public's precautionary savings. Guo(2002) shows that if the cost of capital is the main channel through which volatility affects future output, stock market returns have a more important role in forecasting economic activity than volatility does. Most empirical test on the relationship between stock prices and real economic activity focus on returns not volatility. Several empirical studies Fama (1981), Fischer and Merton (1984), Barro (1990), Fama (1990), Schwert (1990), Peiro (1996), Domian and Louton (1997), Choi et al., (1999), Hassapis and Kalyvitis, 2002) and Foresti (2006) found a strong relationship between stock returns and real economic activity. Stock and Watson (1990,1998) provided evidence that stock market performance is not correlated with real economic activity. Binswanger (2000, 2004) and Mao and Wu (2007) argued that the that the relation between stock returns and real economic activity has disappeared since the early 1980's indicating that stock return ceased to lead real economic activity. Binswanger (2000) found the results for USA while Binswanger (2004) found similar result for Canada Japan and the four European G-7 countries . Mao and Wu (2007) found similar result for Australia using VAR methodology. Empirical studies have also been conducted on the causality relationship between stock market performance and real economic activity. Fama (1990), Schwert (1990), Doong (2001), Canova and De Nicolo (1995) and Mauro (2003) finds that an increase in stock market returns cause an increase in real economic activity. Mao and Wu (2007) found a bidirectional long-run Granger causality between stock market prices and real economic activity in Australia. Gjerde and Saettem (1999); and Know and Shin (1999) provide evidence that the stock market performance is not a leading indicator for real economic activity. Most of the studies discussed are for developed countries. Few studies have been conducted for emerging economies on the interrelationship between stock returns and real economic activity. Rangvid (2001) provided evidence on the strong relation between stock return and real economic activity for several emerging economies. Mauro (2003) provided evidence for emerging and advanced countries while Kaplan (2008) provided evidence for Turkey. Little or no work has been done on the relation between stock returns or its volatility on real economic activity in Nigeria. This paper attempts to fill this gap. 4. METHODOLOGY 4.1 THE DATA The time series data used in this analysis consists of quarterly data obtained from Central Bank of Nigeria (2008), National Bureau of Statistics and the daily official list of the Nigerian Stock Exchange over the period 1985.2 to 2009.3. In this study, real stock return is defined as: (1 SRt ) RSRt = -1 (1) (1 INFt ) Where SRt represents stock return at time t. SRt is obtained as ln(NSIt/NSIt-1). NSIt mean Nigerian stock Exchange index at time t while NSIt-1 represent Nigerian Stock Exchange index at time t-1. INFt represents inflation rate at time t obtained as CPIt/CPIt-1 -1. CPIt represents consumer price index at time t. Growth rate in real gross domestic product (RGDP) is the proxy for real economic activity and is obtained as REGDPt/REGDPt-1 – 1. REGDPt represents real gross domestic product at quarter t obtained by deflating gross domestic product at current prices by the consumer price index at quarter t. RGDP and RSR will be used in obtaining the real GDP and real stock returns volatilities. The control variables used in this study includes growth in Money supply, growth in private sector credits (PSG), growth rate in real exports to United States (REXG), growth in real exchange rate (REXRG) and real Treasury bill yield (RTR). Money Supply Friedman and Schwartz (1963) argued that the growth rate of money supply would affect the aggregate economy and hence the expected stock returns. An increase in M2 growth would indicate excess liquidity available for buying securities, resulting in higher security prices. Empirically, Hamburger and Kochin (1972) and Kraft and Kraft (1977) found a strong linkage between the two variables, while Cooper (1974) and Nozar and Taylor (1988) found no relation. Money supply used in this study is the broad money supply (M2). This is defined as M1 + quasi-money. Quasi-money is defined as Time, savings and Foreign currency deposits of Commercial and Merchant banks. Growth rate in money supply (MSG) is calculated as (M2t/M2t-1)-1. Private Sector Credit Private sector credit (PSC) is the proxy for banking development. Levine and Zervos (1998) among others have examined the impact of banking development on economic growth. Credits granted by banks could have influenced real economic activity and real stock returns in Nigeria. In this study, growth in private sector credit (PSG t) is obtained as PSCt/PSCt-1 – 1. PSCt represents private sector credits at quarter t. Real export Growth in real export could have positive impact on the economy due to increase in foreign exchange earnings. As at 2009, the United States is the highest trading partner of Nigeria. In this study real exports (REXPt) represents real export to United States at quarter t obtained by deflating export to United States in Nigerian currency in quarter t by consumer price index in quarter t. Growth in real export (REXGt) is obtained as ln(REXPt/REXPt-1). The quarterly data on exports to United states was downloaded from the website of the US. Census bureau. Real exchange rate Mukherjee and Naka (1995) argued that there is a positive correlation between stock market returns and change in exchange rate. However, the impact of exchange rate on the economy or the stock market could have been affected by the inflation rates. In this study, the impact of real exchange rate on the stock market and the economy will be examined. The bilateral nominal exchange rate, defined as the domestic currency price of the U.S. dollar (EXH), is converted into a real exchange rate by multiplying the nominal rate by the ratio of the U.S. CPI to the domestic CPI. Growth in real exchange rate (REXRG) is obtained as ln(EXHt/EXHt-1). The US. CPI data was downloaded from the website of the Economic Research department of the Federal Reserve Bank of St. Louis. Interest Rate (Treasury Bill yield) Mukherjee and Naka (1995) argued that changes in both short- and long-term government bond rates would affect the nominal risk-free rate and thus affect the discount rate. Leon (2008) and Zafar, Urooj and Durrani (2008) found interest rates to have a strong positive predictive power for stock returns but weak predictive power for volatility. In this study the impact of short term interest rate (such as real treasury bill yield) on the economy and stock market is examined. Real treasury bill yield (RTR) is obtained as ((1+TRYt)/(1+INFt))-1. The nominal treasury bill yield (TRY) at quarter t is obtained as ln(TRPt)/TRPt-1). The nominal price of a treasury bill with 91 days to maturity in quarter t (TRPt) is N100. The nominal price of a treasury bill with 91 days to maturity at quarter t-1 (TRPt-1) is obtained as N100(1- (91/365)TRt). TRt represents treasury bill rate in quarter t. Financial Reform variables Financial reforms are represented with dummy variables in this study. Four financial reforms are included in this study. The first reform is the introduction of structural adjustment in September 1986 defined as SAP. The value 0 is entered for the quarters on/or before September 1986 and 1 for other quarters. The second financial reform is the deregulation of the capital market in January 1993 defined as CAPD. The value 0 is entered for the quarter before 1993 and 1 for other quarters. The third financial reform is the introduction of Universal banking in January 2001 defined as UB. The value 0 is entered for the quarter before January 2001 and 1 for other quarters. The fourth financial reform is the introduction of new capital requirements of N25 billion for banks in July 2004 defined as BC. The value 0 is entered for the quarters before July 2004 and 1 for other quarters. The fifth financial reform is the introduction of new capital requirements for Insurance companies in September 5, 2005 defined as INS. The value 0 is entered for the quarters before September 5, 2005 and 1 for other quarters. Stock market crash variable Since April 1, 2008, stock prices on the Nigerian Stock market has been declining. The stock index fell from 63016.56 on April 1, 2008 to 27108.4 on March 2, 2009. To account for the stock market crash (MCS) in this paper, a dummy variable is set equal to 0 for the quarters before April 1, 2008 and 1 thereafter. Global Financial Crisis variable In this study, September 7, 2008 is taken as the date of commencement of the global financial crisis. On this day, the United States government took over two United States Government sponsored enterprises Fannie Mae (Federal National Mortgage Association) and Freddie Mac (Federal Home Loan Mortgage Corporation) into conservatorship run by the United States Federal Housing Finance Agency. The global financial crisis (GFC) will be accounted for in the paper by setting a dummy variable equal to 0 for the quarters before September 7, 2008 and 1 thereafter. 4.2 PROPERTIES OF THE DATA The summary statistics of the real GDP growth (RGDP), real stock returns (RSR), money supply growth (MSG), private sector credit growth (PSG), real export growth (REXG), real exchange rate growth (REXRG) and real treasury bill yield (RTR) series are given in Table 3. The mean return for the real stock returns (RSR), real GDP growth (RGDP), money supply growth (MSG), private sector credit growth (PSG), real export growth (REXG), real exchange rate growth (REXRG) and real treasury bill yield (RTR) series are 0.0227, 0.0059, 0.0682, 0.0764, 0.0431, 0.0117 and -0.0439 respectively while their standard deviations are 0.1465, 0.1342, 0.0956, 0.1185, 0.4338, 0.2220 and 0.0715 respectively. The skewness for the real GDP growth (RGDP), money supply growth (MSG), private sector credit growth (PSG), real export growth (REXG), real exchange rate growth (REXRG) and real treasury bill yield (RTR) series are 2.8102, -0.6874, 1.7892, 0.5888, 1.7149, 4.5056 and -0.4151respectively. This shows that the distribution, on average, is positively skewed relative to the normal distribution (0 for the normal distribution) for the RGDP, MSG, PSG, REXG and REXRG series while the distribution is negatively skewed relative to the normal distribution (0 for the normal distribution) for the RSR and RTR series. This is an indication of a non-symmetric series. The kurtosis for the real GDP growth (RGDP), money supply growth (MSG), private sector credit growth (PSG), real export growth (REXG), real exchange rate growth (REXRG) and real treasury bill yield (RTR) series are very much larger than 3, the kurtosis for a normal distribution. Skewness indicates non-normality, while the relatively large kurtosis suggests that distribution of the return series is leptokurtic, signaling the necessity of a peaked distribution to describe this series. This suggests that for the RGDP, RSR, MSG, PSG, REXG, REXRG and RTR series, large market surprises of either sign are more likely to be observed, at least unconditionally. The Ljung-Box test Q statistics for real stock returns (RSR), money supply growth (MSG), private sector credit growth (PSG), real export growth (REXG) and real exchange rate growth (REXRG) series are all insignificant at the 5% for all reported lags confirming the absence of autocorrelation in the RSR, MSG, PSG, REXG and REXRG series. However, the Ljung-Box test Q statistics for the real GDP growth (RGDP) and real treasury bill yield (RTR) are significant at the 5% level confirming the presence of autocorrelation in the RGDP and RTR series. The Jarque-Bera normality test rejects the hypothesis of normality for the real GDP growth (RGDP), real stock returns (RSR), money supply growth (MSG), private sector credit growth (PSG), real export growth (REXG) and real exchange rate growth (REXRG) but accept the hypothesis for real treasury bill yield (RTR) series. Figures 2, 3, 4, 5, 6, 7 and 8 shows the quantile-quantile plots of the real GDP growth (RGDP), real stock returns (RSR), money supply growth (MSG), private sector credit growth (PSG), real export growth (REXG), real exchange rate growth (REXRG) and real treasury bill yield (RTR) series. Figures 2, 3, 4, 5, 6, 7 and 8 clearly show that the distribution of the real GDP growth (RGDP), real stock returns (RSR), money supply growth (MSG), private sector credit growth (PSG), real export growth (REXG) and real exchange rate growth (REXRG) series show a strong departure from normality. The distribution of real treasury bill yield (RTR) is approximately normal. The Ljung-Box test Q2 statistics for the real stock returns (RSR) series are significant at the 5% for all reported lags confirming the presence of heteroscedasticity in the stock returns return series. However, the Ljung-Box test Q2 statistics for the real GDP growth (RGDP), private sector credit growth (PSG) and real export growth (REXG) series are insignificant at the 5% for all reported lags. Table 4 shows the results of unit root test for the real GDP growth (RGDP), money supply growth (MSG), private sector credit growth (PSG), real export growth (REXG), real exchange rate growth (REXRG) and real treasury bill yield (RTR) series. The Augmented Dickey-Fuller test statistics for the real GDP growth (RGDP), real stock returns (RSR), money supply growth (MSG), private sector credit growth (PSG), real export growth (REXG) and the real exchange rate growth (REXRG) series are less than their critical values at the 1%, 5% and 10% level. This shows that the real GDP growth, real stock returns, money supply growth, private sector credit growth, real export growth (REXG) and the real exchange rate growth (REXRG) series have no unit root. Thus, RGDP, RSR, MSG, PSG REXG and REXRG are I (0) variables and there is no need to difference the data. However, RTR has a unit root. The results further showed that RTR is first difference stationary. Thus, RTR is an I(1) variable. There is a need to difference the RTR data. In summary, the analysis of the real GDP growth (RGDP), money supply growth (MSG), private sector credit growth (PSG), real export growth (REXG), real exchange rate growth (REXRG) and real treasury bill yield (RTR) series indicates that the empirical distribution of the real GDP growth, real stock returns, private sector credit growth and real export growth series is non-normal, with very thick tails. The leptokurtosis reflects the fact that the market is characterised by very frequent medium or large changes. These changes occur with greater frequency than what is predicted by the normal distribution. The empirical distribution confirms the presence of a nonconstant variance or volatility clustering. Thus, GARCH model will be used in estimating the volatilities of real GDP growth (RGDP) and real stock returns. Table 3: Summary Statistics and Autocorrelation of the Real economic activity, Stock returns Return, Private Credit Growth and Real exports growth series over the quarters, 1985.2 – 2009.3 RGDP RSR Mean 0.0227 0.0059 Median 0.0087 0.0129 Maximum 0.7970 0.2787 Minimum -0.1969 -0.4714 Std. Dev. 0.1465 0.1342 Skewness 2.8102 -0.6874 Kurtosis 15.1656 4.0797 Jarque-Bera 733.3220 12.4773 Probability (0.0000)* (0.0020)* Observations 98 98 Ljung-Box Q Statistics Q(1) 2.5843 3.8339 (0.1080) (0.0500) Q(2) 8.3686 5.7408 (0.0150)* (0.0570) Q(3) 8.6320 5.7414 (0.0350)* (0.1250) Q(4) 12.5910 5.7521 (0.0130)* (0.2180) Q(5) 14.2640 5.7770 (0.0140)* (0.3290) 2 Ljung-Box Q Statistics Q2(1) 0.0013 24.6500 (0.9710) (0.0000)* Q2(2) 0.0055 31.0260 (0.9970) (0.0000)* Q2(3) 0.2428 32.4690 (0.9700) (0.0000)* Q2(4) 0.5528 32.4910 (0.9680) (0.0000)* Q2(5) 0.7766 32.5280 (0.9790) (0.0000)* Notes: p values are in parentheses. MSG PSG REXG REXR RTR 0.0682 0.0764 0.0431 0.0117 -0.0439 0.0547 0.0611 -0.0033 -0.0217 -0.0367 0.5135 0.5610 1.8614 1.3562 0.1410 -0.1208 -0.3910 -0.7050 -0.3681 -0.2382 0.0956 0.1185 0.4338 0.2220 0.0715 1.7892 0.5888 1.7149 4.5056 -0.4151 8.4468 7.9344 7.9506 28.0353 3.6677 173.431 105.083 148.110 2890.861 4.635 (0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0985) 98 98 98 98 98 0.6548 (0.4180) 1.2829 (0.5270) 2.6721 (0.4450) 5.6579 (0.2260) 5.8623 (0.3200) 1.4553 (0.2280) 1.5974 (0.4500) 1.6041 (0.6580) 1.7066 (0.7900) 9.2004 (0.1010) 2.4611 (0.1170) 3.8300 (0.1470) 4.9195 (0.1780) 4.9954 (0.2880) 5.0613 (0.4080) 0.5543 (0.4570) 1.0771 (0.5840) 1.1914 (0.7550) 1.7930 (0.7740) 1.7941 (0.8770) 5.1505 (0.0230)* 12.6110 (0.0020)* 15.3070 (0.0020)* 50.9010 (0.0000)* 51.9280 (0.0000)* 0.3378 5.6307 0.0561 0.0070 1.9146 (0.5610) (0.0180) (0.8130) (0.9340) (0.1660) 2.2612 17.4230 0.0734 0.0488 2.2751 (0.3230) (0.0000)* (0.9640) (0.9760) (0.3210) 3.1150 19.1480 0.3329 0.1577 2.2874 (0.3740) (0.0000)* (0.9540) (0.9840) (0.5150) 3.5801 25.1270 0.7682 0.2758 22.9920 (0.4660) (0.0000)* (0.9430) (0.9910) (0.0000)* 3.5811 34.0660 5.9987 0.3711 22.9930 (0.6110) (0.0000)* (0.3060) (0.9960) (0.0000)* * indicates significant at the 5% level Figure 2: Quantile-Quantile Plot of Quarterly Real GDP Series over the period, 1985.2 – 2009.3 .5 .4 Quantiles of Normal .3 .2 .1 .0 -.1 -.2 -.3 -.4 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 Quantiles of RGDP Figure 3: Quantile-Quantile Plot of Quarterly Real stock return series over the period, 1985.2 – 2009.3 .4 .3 Quantiles of Normal .2 .1 .0 -.1 -.2 -.3 -.4 -.5 -.4 -.3 -.2 -.1 .0 Quantiles of RSR .1 .2 .3 Figure 4: Quantile-Quantile Plot of Money Supply growth Series over the period, 1985.2 – 2009.3 .4 Quantiles of Normal .3 .2 .1 .0 -.1 -.2 -.2 -.1 .0 .1 .2 .3 .4 .5 .6 Quantiles of MSG Figure 5: Quantile-Quantile Plot of Quarterly Private credit growth Series over the period, 1985.2 – 2009.3 .4 Quantiles of Normal .3 .2 .1 .0 -.1 -.2 -.3 -.4 -.2 .0 .2 Quantiles of PSG .4 .6 Figure 6: Quantile-Quantile Plot of Quarterly real export growth series over the period, 1985.2 – 2009.3 1.2 Quantiles of Normal 0.8 0.4 0.0 -0.4 -0.8 -1.2 -0.8 -0.4 0.0 0.4 0.8 1.2 1.6 2.0 Quantiles of REXG Figure 7: Quantile-Quantile Plot of Quarterly real exchange rate growth series over the period, 1985.2 – 2009.3 .6 Quantiles of Normal .4 .2 .0 -.2 -.4 -.6 -0.4 0.0 0.4 0.8 Quantiles of REXRG 1.2 1.6 Figure 8: Quantile-Quantile Plot of Quarterly real interest rate yield series over the period, 1985.2 – 2009.3 .15 .10 Quantiles of Normal .05 .00 -.05 -.10 -.15 -.20 -.25 -.3 -.2 -.1 .0 .1 .2 Quantiles of RTR Table 4: Unit Root Test of the Real GDP, Real Stock returns, Private Credit growth and Real export over the quarters, 1985.2 – 2009.3 Variable Exogenous Augmented Dickey-Fuller test Statistic Critical Values (%) 1% level 5% level 10% level RGDP RSR MSG PSG REXG REXRG RTR D(RTR) L L L L LC L L L LC LTC L -9.2714 -7.8532 -4.7619 -1.5984 -10.9978 -12.3183 -10.5207 -1.5097 -2.5876 -3.1274 -14.8393 -2.5893 -2.5890 -2.5903 -2.5901 -3.4992 -2.5890 -2.5890 -2.5898 -3.5014 -4.0586 -2.5898 -1.9442 -1.9442 -1.9444 -1.9443 -2.8916 -1.9442 -1.9442 -1.9443 -2.8925 -3.4583 -1.9443 -1.6145 -1.6146 -1.6144 -1.6145 -2.5828 -1.6146 -1.6146 -1.6145 -2.5834 -3.1552 -1.6145 Notes: The appropriate lags are automatically selected employing Akaike information criterion. L indicates unit root test using level observation only . LC indicates the result of the unit root test by including a constant in the test equation. LTC indicates the result of the unit root test by including a constant and trend in the test equation. FD represents the first difference. The first difference has no constant in the test equation. The bolded item indicates stationary. 4.3 MODELS USED IN THE STUDY This study uses the Vector autoregression model (VAR) to investigate the interrelationship between the real economic activity (RGDP), real stock returns (RSR), real economic activity volatility (GDPV) and real stock return volatility (RSV). The VAR will assist in assessing the direction of causality among the variables. The control variables (MSG, PSG, REXG, REXRG and RTR) and the financial reform variables (SAP, CAPD, UB, BC, INS, GFC and MCS) will serve as exogenous variables in all equations. However, to formulate our VAR, real economic activity volatility (GDPV) and real stock return volatility (RSV) needs to first be estimated. Due to possible non-symmetric property of the real GDP growth (RGDP) and real stock return (RSR) series, this paper employs the exponential GARCH (EGARCH) model advanced by Nelson (1991). The EGARCH model, unlike the GARCH model, indicates that the conditional variance is an exponential function, thereby removing the need for restrictions on the parameters to ensure positive conditional variance. Thus, the model used in estimating the volatility of the real GDP growth (RGDP) series is the EGARCH (1, 2) model while the volatility of real stock return series is estimated using the EGARCH (1, 3) model. The lag length of the RGDP used in accounting for autocorrelation of RGDP has been chosen on the basis of Akaike information Criterion. Due to non-normality and excess kurtosis of the RGDP and RSR series the two models are under the assumption of Generalized Error Distribution (GED). The mean and variance equations of the RGDP series are given as: 2 RGDPt =b0+ b j RGPGt j + εt j 1 t / t 1 ~ N(0, 2t ,v t ) (2) t 1 2 (3) log(2t 1 ) t 1 t 1 t 1 Where vt is the degree of freedom The EGARCH variance series from (3) will be used as an estimate of the volatility of real GDP (GDPV). The mean and variance equations of the RSR series are given as: log(2t ) Rt = b0 + dσt + εt log(2t ) t / t 1 ~ N(0, 2t ,v t ) (4) 3 t 1 2 j log(2t j ) t 1 t 1 j1 t 1 (5) where vt is the degree of freedom The EGARCH variance series from (5) will be used as an estimate of the volatility of real stock returns (RSV). 5. THE RESULTS Estimation of Real economic volatility and Real Stock Return Volatility The results of estimating the EGARCH (1, 2) model for the real GDP (RGDP) series and EGARCH-M (1, 2) model for the real stock return (RSR) series are presented in Table 5. In the mean equation of the RGDP series, b1 (coefficient of first lag of real GDP) is significant at the 5% level confirming the correctness of adding the variable to correct for autocorrelation in the real GDP series. However, b2 (coefficient of second lag of real GDP) is insignificant at the 5% level. The variance equation in Table 5 shows that the α coefficient is statistically insignificant in the EGARCH (1, 2) model for the RGDP series. This tends to confirm the result from preliminary testing of absence of ARCH effect in the RGDP series. Table 5 shows that the α coefficient is statistically significant in the EGARCH (1, 3) model. This tends to confirm that the ARCH effects are very pronounced implying the presence of volatility clustering. Table 5 shows that in the EGARCH (1,2) model, β2 coefficient (the determinant of the degree of persistence) is statistically significant at the 5% level while in the EGARCH (1,3) model, β3 is statistically significant at the 5% level. Even though β1 is statistically insignificant in the EGARCH (1,2) model, its value is negligible compared to β2. Similarly, though β1 and β2 are insignificant in the EGARCH (1,3) model, their values are negligible compared to β3. The β2 coefficients in the EGARCH (1,2) model and β3 in the EGARCH (1,3) model are 0.9903 and 0.8236 respectively. This appears to show that there is a high persistence in volatility in Nigeria. Table 5 shows that the coefficients of γ, the asymmetry and leverage effects, are negative and statistically significant at the 5% level in the EGARCH (1,3) model but insignificant in the EGARCH (1, 3) model. The predominance negatively significance of γ in the results, appears to show that the asymmetry and leverage effects are accepted in the EGARCH (1,3) model of the RSR series but rejected in the EGARCH (1, 2) model of the RGDP series. Table 6 shows the results of the diagnostic checks on the estimated EGARCH (1,2) and EGARCH (1,3) models for the real GDP and real stock returns respectively. Table 6 shows that the Ljung-Box Q-test statistics of the standardized residuals for the remaining serial correlation in the mean equation shows that autocorrelation of standardized residuals are statistically insignificant at the 5% level for the real GDP growth (RGDP) and real stock returns (RSR) series confirming the absence of serial correlation in the standardized residuals. This shows that the mean equations are well specified. The Ljung-Box Q2-statistics of the squared standardized residuals in Table 6 are all insignificant at the 5% level for the real GDP growth (RGDP) and real stock returns (RSR) series confirming the absence of ARCH in the variance equation. The ARCH-LM test statistics in Table 6 for the real GDP growth (RGDP) and real stock returns (RSR) further showed that the standardized residuals did not exhibit additional ARCH effect. This shows that the variance equations are well specified in for the full sample, all sub periods and the augmented model. In sum, all the models are adequate for forecasting purposes. Parameter Estimates of the EGARCH Models over the quarters, 1985.2 – 2009.3 RGDP RSR Mean equations b0 0.0028 0.0179 (0.2642) (0.1526) b1 -0.1312 (0.0012)* b2 -0.1393 (0.0687) Variance equations ω 0.3692 -0.8565 (0.5298) (0.1119) α -0.1878 0.5953 (0.3439) (0.0034)* γ -0.1480 -0.2828 (0.1577) (0.0167)* β 0.0569 -0.0373 (0.4188) (0.6964) β2 0.9903 0.1100 (0.0000)* (0.1551) β3 0.8236 (0.0000)* GED 1.2091 2.5330 (0.0000)* (0.0023)* Persistence 1.0472 0.8963 LL 84.9193 70.4094 AIC -1.5817 -1.2737 SC -1.3412 -1.0626 HQC -1.4845 -1.1883 N 98 98 EGARCH EGARCH(1,2) EGARCH (1,3) Table 5: Notes: p values are in parentheses. * indicates significant at the 5% level. LL, AIC, SC, HQC and N are the maximum log-likelihood, Akaike information Criterion, Schwarz Criterion, Hannan-Quinn criterion and Number of observations respectively Table 6: Autocorrelation of Standardized Residuals, Autocorrelation of Squared Standardized Residuals and ARCH LM test of Order 4 for the EGARCH and EGARCH-in-Mean Models over the quarters, 1985.2 – 2009.3 RGDP RSR Ljung-Box Q Statistics Q(1) 3.1080 1.2981 (0.0780) (0.2550) Q(2) 4.0836 1.4782 (0.1300) (0.4780) Q(3) 4.3568 2.7213 (0.2250) (0.4370) Q(4) 4.3568 6.6376 (0.3600) (0.1560) Q(5) 4.5968 10.2780 (0.4670) (0.0680) 2 Ljung-Box Q Statistics Q2(1) 0.0576 0.5455 (0.8100) (0.4600) Q2(2) 0.2763 0.7202 (0.8710) (0.6980) Q2(3) 0.7404 1.4177 (0.8640) (0.7010) Q2(4) 0.7539 1.4450 (0.9450) (0.8360) 2 Q (5) 0.8262 1.4454 (0.9750) (0.9190) ARCH-LM TEST ARCH-LM (1) 0.0552 0.5154 (0.8148) (0.4746) ARCH-LM (5) 0.1869 0.2519 (0.9668) (0.9378) Note: p values are in parentheses Table 7 shows the results of unit root test for the estimated real economic activity volatility (GDPV) and real stock returns volatility (RSV) from the EGARCH variance series from Table 5. The Augmented Dickey-Fuller test statistics show that both RSV and GDPV have a unit root. The results further showed that both RSV and GDPV are first difference stationary. They are, thus, I(1) variables. Thus, there is a need to difference both the GDPV and RSV data. Table 7: Unit Root Tests of Real Economic Activity Volatility and Real Stock Return Volatility over the quarters, 1985.2 – 2009.3 Variable Exogenous Augmented Dickey-Fuller test Statistic Critical values 1% level 5% level 10% level GDPV L -0.7949 -2.5898 -1.9443 -1.6145 LC -0.9456 -3.5014 -2.8925 -2.5834 LTC -1.4751 -4.0586 -3.4583 -3.1552 D(GDPV) L -134.6310 -2.5898 -1.9443 -1.6145 RSV L 0.3565 -2.5895 -1.9442 -1.6145 LC -1.3163 -3.5007 -2.8922 -2.5832 LTC -3.2723 -4.0586 -3.4583 -3.1552 D(RSV) L -17.7076 -2.5895 -1.9442 -1.6145 Notes: The appropriate lags are automatically selected employing Akaike information criterion. L indicates unit root test using level observation only . LC indicates the result of the unit root test by including a constant in the test equation. LTC indicates the result of the unit root test by including a constant and trend in the test equation. FD represents the first difference. The first difference has no constant in the test equation. The bolded item indicates stationary. Interrelationship between Real economic activity, Real stock returns, Real economic activity volatility and real stock return volatility The Vector Autoregression models (VAR) used in investigating the Interrelationship between Real economic activity, Real stock returns, real economic activity volatility and real stock return volatility are given as follows: 2 2 2 2 12 i1 i1 i 1 i 1 j 1 RGDPt=α1+ i1RGDPt i + i1RSRt i + i1D(GPV)t i + i1D(RSV)t i + j1FRE jt +εi1 2 2 2 2 12 i1 i 1 i 1 i 1 j 1 RSRt=α2+ i2RGDPt i + i2RSRt i + i2D(GPV)t i + i2D(RSV)t i + j2FRE jt +εi2 2 2 2 2 12 i1 i 1 i 1 i 1 j 1 D(GPV)t=α3+ i3RGDPt i + i3RSRt i + i3D(GPV)t i + i3D(RSV)t i + j3FRE jt +εi3 2 2 2 2 12 i1 i 1 i 1 i 1 j 1 RSVt=α4+ i4RGDPt i + i4RSRt i + i4D(GDPV)t i + i4D(RSV)t i + j4FRE jt +εi4 (6) (7) (8) (9) Where FREj represents the twelve financial reforms and other exogenous variables and they include MSG, PSG, REXG, REXRG, RTR, SAP, CAPD, UB, BC, INS, GFC and MCS. The results of the VAR of Equations (6) to (9) are presented in Table 8. The lag length of the VAR was selected on the basis Final prediction error and Akaike information criterion. The VAR in Table 8 satisfies the stability condition as no root lies outside the unit circle. The result of the VAR of Equation (6) as presented in Table 8 showed that the coefficients of RGDP(-2), and PSG are negative and statistically significant at the 5% level while all other variables are statistically insignificant at the 5% level. This implies that the real GDP growth two quarters ago and growth in private sector credits negatively influenced the real economic activity. All other variables do not influence real economic activity. The result of Equation (7) as shown in Table 8 showed that the coefficients of lags of RGDP, RSR, D(GDPV) and D(RSV) are all statistically insignificant at the 5% level implying that these variables have no influence on real stock return. The statistical insignificance of lags of RSR is in line with the weak form efficiency of the Nigerian stock market (Olowe, 1999). Equation (7) as shown in Table 8 further showed the coefficients of all the control variables are insignificant. However, Equation (7) shows that the coefficient of BC is negative and statistically significant at the 5% level while the coefficient of INS is positive and statistically significant at the 5% level. This implies that the introduction of new capital requirements of N25 billion for banks in July 2004 negatively influenced real stock returns while the introduction of new capital requirements for Insurance companies in September 5, 2005 positively influenced real stock returns. The result of Equation (8) as shown in Table 8 showed that the coefficients of RGDP (-1), RGDP (-2), D(GDPV(-1)) and D(RTR (-1)), are negative and statistically significant at the 5% level while all other variables are insignificant. This implies that the real economic activity last quarter, the real economic activity two quarters ago, real economic activity volatility last quarter, and treasury bill yield last quarter negatively influenced real economic activity volatility. The result of Equation (9) as shown in Table 8 showed that the coefficients of RGDP(-1), RSR (-1), RSV(-1) and RSV(-2), are negative and statistically significant at the 5% level while the coefficient of RSR(-2) and GFC are positive and statistically significant at the 5% level. This implies that the real GDP growth last quarter, real stock return last quarter, real stock return volatility last quarter and real stock return volatility two quarters ago negatively influenced real stock return volatility. However, real stock returns two quarters ago and global financial crisis positively influenced real stock return volatility. In order to properly determine the direction of causality, the Granger causality test is implemented within the framework of a vector autoregression (VAR) model. Estimation results obtained from using quarterly data are presented in Table 9 for the period 1985.2-2009.3. Table 9 provides Chi-square statistics and probability values of pairwise Granger causality/block exogeneity Wald test results between the endogenous variables. The null hypothesis of non-causality shows that there is no causal relationship between real economic activity and real stock returns at the 5% level. However, Table 9 shows that the direction of causality is one way from real economic activity to real economic activity volatility; real economic activity to real stock return volatility; real stock returns to real stock return volatility; and real economic activity volatility to real stock return volatility. The result of no causal relationship between real economic activity and real stock return is in line with the results obtained by Binswanger (2000, 2004) and Mao and Wu (2007). Table 8: VAR results for the Interrelationship between Real economic activity, Real Stock Return, Real economic activity volatility and real stock return volatility over the quarters, 1985.2 – 2009.3 RGDP C -0.2509 (-1.3621) RGDP(-1) -0.1206 (-1.0412) RGDP(-2) -0.4142 (-2.8786)* RSR(-1) 0.2030 (1.4552) RSR(-2) 0.0105 (0.0695) D(GDPV(-1)) -9.0188 (-1.0457) D(GDPV(-2)) -6.6767 (-0.8077) RSV(-1) 0.6484 (0.6302) RSV(-2) -0.4453 (-0.4717) MSG 0.1885 (1.0968) PSCG -0.3233 (-2.3400)* REXG 0.0257 (0.4664) REXR 0.1009 (0.9358) D(RTR(-1)) 0.0060 (0.0293) SAP 0.2986 (1.6153) CAPD 0.0108 (0.2842) UB -0.0287 (-0.6174) BC 0.0312 (0.4150) INS -0.0270 (-0.3340) MCS -0.0129 (-0.0873) GR 0.0306 (0.1856) Adj. R-squared 0.5402 Log likelihood 93.7242 Akaike AIC -1.5731 Schwarz SC -1.0624 Note: t-statistics are in parentheses RSR D(GDPV) 0.0033 ( 0.0203) -0.0472 (-0.4557) 0.2038 ( 1.5836) 0.0330 ( 0.2647) -0.0929 (-0.6877) 2.2091 ( 0.2863) 0.4394 (0.0594) 0.3549 (0.3856) -0.3761 (-0.4454) -0.2391 (-1.5549) -0.1045 (-0.8458) 0.0646 (1.3129) -0.0352 (-0.3651) 0.2857 (1.5686) 0.0282 (0.1704) -0.0029 (-0.0854) 0.0448 (1.0768) -0.1503 (-2.2380)* 0.1983 (2.7454)* -0.2282 (-1.7251) -0.1004 (-0.6818) 0.3386 83.4302 -1.3564 -0.8456 * RSV -0.0016 0.0008 (-0.6414) -0.0643 -0.0111 -0.0270 (-7.2056)* (-3.2769)* -0.0043 0.0009 (-2.2168)* ( 0.0878) 0.0015 -0.0666 ( 0.8219) (-6.7161)* 0.0016 0.0282 ( 0.8003) (2.6268)* -0.9493 0.2550 (-8.2538)* (0.4158) 0.0279 0.0724 ( 0.2528) ( 0.1232) -0.0002 -0.9743 (-0.0165) (-13.3192)* 0.0007 -0.7179 ( 0.0575) (-10.6956)* 0.0011 -0.0068 ( 0.4792) (-0.5550) 0.0001 0.0064 ( 0.0429) (0.6474) -0.0006 0.0046 (-0.7890) (1.1639) 0.0022 -0.0078 (1.5283) (-1.0203) -0.0059 0.0187 (-2.1830)* (1.2937) 0.0018 0.0016 (0.7152) (0.1194) 0.0004 -0.0011 (0.7491) (-0.4127) -0.0012 -0.0010 (-1.8777) (-0.2910) 0.0013 0.0072 (1.2597) (1.3517) -0.0017 -0.0076 (-1.6048) (-1.3285 0.0006 -0.0083 ( 0.2944) (-0.7867) 0.0009 0.0266 (0.4105) (2.2724)* 0.8821 0.4625 478.5851 67.6863 -9.6755 -1.0250 -9.1647 -0.5142 indicates significant at the 5% level. Table 9: * Granger causality/block exogeneity Wald test results between the endogenous variables over the quarters, 1985.2 – 2009.3 Null Hypothesis Χ2 df Prob. RGDP does not Granger cause RSR 2.2398 2 0.3263 RGDP does not Granger cause D(GDPV) 4.8364 2 0.0891 RGDP does not Granger cause RSV 1.5576 2 0.4590 RSR does not Granger cause RGDP 2.6454 2 0.2664 RSR does not Granger cause D(GDPV) 3.6439 2 0.1617 RSR does not Granger cause RSV 0.8824 2 0.6433 D(GDPV) does not Granger cause RGDP 58.7204* 2 0.0000 D(GDPV) does not Granger cause RSR 1.6206 2 0.4447 D(GDPV) does not Granger cause RSV 0.0075 2 0.9963 RSV does not Granger cause RGDP 10.7458* 2 0.0046 RSV does not Granger cause RSR 47.0338* 2 0.0000 RSV does not Granger cause D(GDPV) 6.0839* 2 0.0477 indicates significant at 5% level Thus, the findings in this study show that the developments in the stock market have no impact on output in Nigeria. The banking reform in 2004 and insurance reform in 2005 contributes to the development of the stock market as they influenced real stock return. However, due to speculative nature of Nigerian economy, past volatilities in stock return and real economic activity influenced real stock return volatility. The global financial crisis also has direct positive impact on the real stock return volatility. Despite the development of the stock market as a result of banking and insurance reform, the real economic activity is not affected. This could possibly due to speculative bubbles in the stock markets. Stock prices might be increasing when they are not fully supported by fundamentals of the companies. There might be no connection between companies performance and stock price performance. If, at all there is a connection between companies performance and stock price performance, it could also be having been due to manipulation by some companies - window dressing their accounts so as to have a favourable impact on the stock market. The economy will not be affected since these companies did not really contribute to output in Nigeria. 6. SUMMARY AND CONCLUDING REMARKS This paper investigated the inter relationship between real economic activity, real stock returns, real economic activity volatility and real stock return volatility in Nigeria using quarterly data over the period 1986.4 and 2009.3 in the light of financial reforms, stock market crash and the global financial crisis. The results showed that there is no causal relationship between real economic activity and real stock returns. This is consistent with the results of Binswanger (2000, 2004) and Mao and Wu (2007). The results also show there is a one-way causality going through from real economic activity to real economic activity volatility; real economic activity to real stock return volatility; real stock returns to real stock return volatility; and real economic activity volatility to real stock return volatility. The results showed that past volatilities in real stock returns and real economic activity negatively influenced real economic activity volatility. The results also show that private sector credit influences real economic activity. The result also shows that real treasury bill yield negatively influenced real economic activity volatility. The results also show that the introduction of new capital requirement for banks and introduction of new capital requirement for insurance influences real stock returns. The result also show that the global financial crisis influences real stock return volatility. Despite the development of the stock market as a result of banking and insurance reform, the real economic activity is not affected. This could possibly be due to speculative bubbles in the stock market. Stock prices might be increasing when they are not fully supported by fundamentals of the companies. There might be no connection between companies performance and stock price performance. If at all there is a connection between companies performance and stock price performance, it could have been be due to manipulations by some companies -window dressing their accounts so as to have a favourable impact on the stock market. The economy will not be affected since these companies did not really contribute to output in Nigeria. Policy makers should introduce economic policies towards increasing real output of companies in Nigeria. 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