Output growth, inflation and interest rate on stock return

An Expert's View about Economic Trends/Outlook in Malaysia

Posted on: 24 May 2010

Economic volatility, as measured by movement in inflation, output growth, and interest rate, has weak predictor power on stock market volatility and returns, for 3 matured and 4 emerging markets.

Output growth, inflation and interest rate on stock return and volatility: the predictive power Wai Ching POON* and Gee Kok TONG** * School of Business, Monash University Sunway Campus, Jalan Lagoon Selatan, 46150 Bandar Sunway, Selangor, Malaysia. Tel: +6(03) 5514 4908. E-mail: poon.wai.ching@buseco.monash.edu.my ** School of Information Technology, Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia. Tel: +6(03) 8312 5240. E-mail: gktong@mmu.edu.my Abstract Using monthly data from seven mature and emerging markets and GARCH and EGARCH models, the study of Davis and Kutan (Applied Financial Economics, 13, 693- 700, 2003) on inflation and output on stock returns and volatility is extended by including interest rate to compare the effect between three mature markets (US, Japan and Singapore) and four crisis experienced emerging markets (Malaysia, India, Korea and Philippines). Results reveal that economic volatility, as measured by movement in inflation, output growth, and interest rate, has weak predictor power on stock market volatility and returns. In line with the evidence reported in Davis and Kutan (2003), the findings suggest that the Fisher effect in stock returns among the seven mature and emerging markets is not supported. Keywords: predictive power, output, inflation, interest rate, stock return volatility 1. Introduction In conjunction with financial crisis and the substantial variability in production levels, a question on the relationship between stock returns and economic activity arises (Mauro, 2000). Study on the impact of inflation, output growth and interest rate movement on conditional stock market volatility has important implications for investors and policy makers. High market volatility increases unfavourable market risk premium. It is critical for policy makers to reduce the stock market volatility and ultimately enhance economy stability in order to improve the effectiveness of the asset allocation decisions. While previous studies have examined the relationship between macroeconomic factors and stock return volatility, no study is placed on real output, inflation and interest rate in sync as exogenous variables in both the mean and conditional variance equations to simultaneously estimate the effect of these variables on stock market returns. This paper is the extension of Davis and Kutan?s (2003) study who employed Generalized Auto- Regressive Conditional Heteroscedasticity (GARCH) and Exponential GARCH (EGARCH) models to simultaneously estimate the predictive power of real output and 1 inflation on monthly stock returns and volatility using data from 13 countries. The main purpose of this paper is to estimate the predictive power of output growth, inflation and interest rate on monthly stock return and their conditional volatility using the data from three mature markets (hereafter MM) and four Asia emerging markets (hereafter EM) for nominal stock returns prediction. This paper employed GARCH and EGARCH models as proposed by Bollerslev (1986) and Nelson (1991) respectively since these models account for conditional volatility. Furthermore, EGARCH model is suitable for asymmetric volatility time series data, considering time varying volatility (volatility clustering) on stock return. The result can also be used to explain the volatility of stock market return and examine the validity of Fisher Effects in international stock returns. This paper is organized as follows. Section 2 provides a review of the relevant literature. Section 3 outlines the methodology, presents data used and sample period. Section 4 discusses the empirical results and the implications of the findings, and section 5 concludes the paper. 2. Literature Review Studies on stock market volatility have been reported in the extant literature. The variability of the market factor of the New York Stock Exchange is linked to the volatility of macroeconomic variables (Officer, 1973). Mixed evidence between stock returns and output economic activity were exhibited from the past studies. McQueen and Roley (1993) claimed that a positive relation between future economic activity and stock returns. The positive linkage between the two can be through a channel mechanism where higher stock returns have a bi-directional effect on higher consumption and investment levels that ultimately enhance economic activity. On the other hand, the empirical evidence indicated negative linkage between stock returns and past economic activity for the US (Balvers et al., 1990). Also, Tsouma (2008) explained the existence of a negative linkage between current economic activity and future stock returns. However, it does not always show a negative significant relationship in the G-7 countries (Hassapis and Kalyvitis, 2002). Similarly, Lee (1992) found that economic activity does not significantly explain the variability in the stock returns in the US economy. In addition, Binswanger (2000) found no predictor variation in future returns correlated with economic activity for the US during the period 1984-1997. Meanwhile, Campbell and Hentschel (1992), Braun et al. (1995), and Bekaert and Harvey (1997) found that there were time-varying volatilities in stock return. Aggarwal et al. (1999) who studied volatility in emerging stock markets found that during the Mexican peso crisis, the Filipinos Marcos-Aquino conflict, and the Indian stock market scandal, significant volatilities in the stock market took place with higher volatility during recession (Schwert, 1989). Therefore, it is hypothesized that higher predictive power of output growth and inflation on monthly stock return and volatility in the financial crisis country, a priori. Schwert (1989a) studied the relation of stock volatility with economic activity, financial leverage and stock trading activity using the US monthly data from 1857 to 1987. Results revealed that aggregate leverage is significantly correlated with volatility and explains 2 relatively small movements in stock volatility. Using simple models of stock valuation, he characterized the changes in stock market volatility to time-varying volatility was unusually high during Great Depression, and found weak evidence of inflation, industrial production growth rates, and monetary base growth rates in predicting stock market volatility. Schwert believed that the conditional variance of stock prices is proportional to the conditional variance of the expected future cash flows if discount rates are constant over time. In addition to that, Schwert (1989b) claimed that stock volatility increases for immediate effect following the worst panics, but there is absent of long-term effects on volatility. Macroeconomic volatility is related to long- and short-term interest rate (Mascaro and Meltzer, 1983). Stock return volatility is correlated with interest rate (Schwert, 1989a). Many studies investigate the interdependency between stock returns and interest rate, yet the evidence is mixed. Previous studies found negative correlation between changes in interest rate and stock returns, among those, are Fama and Schwert (1977), and Geske and Roll (1983). Peiro (1996) further argued that stock returns are affected by current changes in the interest rate and by future changes in production. The changes in interest rate seem to be higher than changes in production. Domian, Gilster, and Louton (1996) argued that falls in interest rate are followed by twelve months of excess stock returns while increases in interest rates have little effects. However, Titman and Warga (1989) found positive relation between stock return and future interest rate changes. Fisher (1930) asserted that the nominal interest rate consists of a real rate plus the expected inflation rate. Fisher Hypothesis stated that expected real rate of the economy is determined by the real factors such as productivity of capital and time preference of savers and is independent of the expected inflation rate. If Fisher effect holds, there is no change in inflation and nominal stock returns since stock returns are allowed to hedge for inflation. Some opposed to Fisher Hypothesis, and claimed that the real rates of common stock return and expected inflation rates are independent and that nominal stock returns vary in one-to-one correspondence with expected inflation. On the other hand, using Pigou real wealth effect, Mundell (1963) revealed that the real rate of interest is negatively related to expected inflation. Santomero (1973) shows that changes in the growth rate of the labour productivity may give rise to a direct relation between the expected real rate and expected inflation. Furthermore, introducing progressive income taxes may cause further dependencies between two variables. 3. Methodology Seven countries are selected in this study with three MM (Japan, USA, Singapore) and four Asia crisis experienced EM (Malaysia, Korea, Philippines, and India). The country selection criteria are driven by data availability. Monthly data on stock prices indices, industrial production index and consumer price Index (CPI), treasury bills rate, money market rate for the sample period are used in this paper. Due to data unavailability, data for countries were starting from different period, but ended at the same period as shown in Table 2. The data for Japan and US were available from 1957 August onwards, while most of the EM were started from early 1980s. All data are obtained from International 3 Financial Statistics (IFS) database. The manufacturing production index was used as a proxy for industrial production index for Philippines. T-Bills Rate was used as proxy for changes in short term interest rate for US, Malaysia, and Singapore, while money market rates were used for Japan, Korea, Philippines and India. Take the logarithmic difference of the stock indices, CPI and industrial production indices to generate stock return, inflation and output growth respectively. All variables are based in the log-differenced, multiplied by 100. Most time series data become stationary after first differences. But this transformation often exhibits volatility, which suggests that variance of financial time series varies over time. In order to overcome this problem, GARCH and EGARCH are used to account for ?varying variance?. GARCH as proposed by Bollerslev (1986) is employed to test if monthly stock returns have time-varying volatility, while EGARCH model as proposed by Nelson (1991) is applied to verify whether shock on stock returns volatility is asymmetry. It is followed by estimating the impact of output growth, inflation, and interest rate on stock markets returns and their volatilities. Following Bollerslev and Wooldridge (1992), robust variance estimator is employed to compute asymptotic standard errors. A comparison study of volatility between MM and EM are then followed. Table 1 depicts the important events that caused sudden changes in volatility in selected emerging markets. Table 1: Events influencing volatility in selected emerging markets Countries Period Events India 24-9-1975 Rupee?s ties to Pound sterling were broken, and floating to a basket of currencies. 11-7-1990 to 13-3-1991 Balance of payments crisis, unstable government due to elections. July 1991 IMF and World Bank approved emergency loans to repay international debts. 26-2-1992 to 27-5-1992 Stock market scandal. 15-4-1997 Controls on capital and money market instruments. Malaysia 1985 Banking crises due to economic recession, bubble burst and weak demand. 21-10-1987 to 20-1-1988 October 1987 crash, Chinese-Malay riots. 1-12-1993 to 2-3-1994 Increased reserved requirements, capital control measures. 2-9-1998 to 21-7-2005 Capital control. 1-9-1998 to 31-8-2008 Peg exchange rate to USD. Philippines 21-8-1983 Beginning of capital flight. 26-2-1986 to 16-9-1987 Marcos-Aquino conflict, coup attempt. 5-4-1989 to 27-3-1991 Debt problems, coup attempt. February 1992 The IMF approved an extension of 18-month standby credit. August 1992 The government lifted all foreign exchange restrictions allowing foreign investors to freely repatriate their capital. South Korea 1984 Minimum and maximum bank interest rate ranges were introduced. 18-4-1990 to 16-1-1991 Large trade deficit. January 1992 Stock market was opened to investors. November 1997 The government abandoned its defence of won, sought loan from IMF. December 1997 Korea got bailout package from IMF. 4 Note: * Adapted from Aggarwal et al. (2001), Bakaert and Harvey (2004), authors? compilation. GARCH model is estimated using the maximum likelihood, assuming that the error terms are conditionally normally distributed. Following Davis and Kutan (2003), the conditional variance is modelled by GARCH (1,1) specification. GARCH (1,1) refers to the presence of a first-order GARCH term and a first-order ARCH term. In the standard GARCH (1,1) specification introduced by Bollerslev (1986), the mean equation in Eq.(1) is written as a function of exogenous or predetermined endogenous variables, x , with an t ' error term. The estimated conditional variance, 2t? , is the one-period ahead forecast variance based on its past information. The conditional variance equation specified in Eq.(2) is a function of constant variance ( ? ), news about the volatility from the previous period, measured as the lag of the squared residual from the mean equation, 2t 1? (the ARCH term), and the past variance, 2t ?1 (the GARCH term). Monthly seasonal dummies variables are also included in those models. yt x t ' ? t ? (1) 2 2 (2) t ? ? ?t 1 ? ?2t 1 ? For GARCH (1,1) model, the contribution to the log likelihood from observation t is as follows: ) l 1t 2 log(2 )? 12 (3log 2 1t ? 2 yt x t ' 2?/ 2t ? where 2 4) t ? ? ?y t 1 x t 1 ' 2? ?(2t 1 ? This model is consistent with the volatility clustering, where large changes in returns are likely to be followed by further large changes. For countries that experience asymmetric stock return volatility due to the downward movements in the market would be followed by higher volatilities than upward movements in the same magnitude. This phenomenon happens because good and bad news generate different impact on stock return volatility. EGARCH (1,1) as proposed by Nelson (1991) is employed to account for this phenomenon. The specification for the conditional variance used is shown in Eq. (5). log 2? ? (5) lo?g 2 ? ?t ?1? ?t 1 t t 1 1 t t ??1 The left hand side of Eq.(5) is the log of the conditional variance. This implies that the leverage effect is exponential, and this will guarantee that the forecast of the conditional 5 variance will not be negative. The presence of the leverage effects can be tested by the hypothesis where ? >0. The impact is asymmetric if ?0? . It is noteworthy that two differences between the Eviews specification of the EGARCH model and the model proposed by Nelson (1991). First, Nelson assumes that ? follows a generalized error distribution, while Eviews assumes normally distributed errors. Second, Nelson?s specification for the log conditional variances differs slightly from the specification above. Nelson (1991) specification for the log conditional variance is stated in Eq.(6). Estimating this model under the assumption of normal errors yields identical estimates to those reported by Eviews except for the intercept term, ? , which differs by ????? 2 ? ÷÷ . ?? log 2? ? (6) lo?g 2 ? ? ???t ?1? 2 1 1 1 ? ÷?? ?tt t t t ??1 We examine the predictive power of output growth and inflation on stock returns and volatility using GARCH (1,1) or EGARCH (1,1) after determined the models for stock returns. Hamilton and Lin (1996) used Eq.(7) and Eq.(8) to model conditional variance of stock returns as a function of past squared forecast economic variables. yt I t ? t ? (7) 2? ? ? 2 1 ?(8) ?2t ? k kt t 1 u?i (OutputGrowth)t i ?i (?Inflation)t i i 1 i 1 This paper modified the model used by Hamilton and Lin (1996), and incorporated the model with three lags as suggested by Davis and Kutan (2003) because the stock market reacts to information relatively faster than goods market. Also, we employed Bollerslev and Wooldridge?s (1992) robust variance estimator to compute asymptotic standard errors since the residuals may not be conditionally normally distributed. When the assumption of conditional normality does not hold, the ARCH parameter will still be consistent, subject to the mean and variance functions are correctly specified. The estimates of the covariance matrix will not be consistent unless this option is specified. The parameter estimates is unchanged using this option but the estimated covariance matrix will be altered. Hence, the models we employed are as shown in Eq.(9) and Eq.(10). Some differences exist between the present specification and that of Hamilton and Lin (1996). This paper takes a ?long-run? view and examines the impact of overall output volatility on stock market volatility, but Hamilton and Lin focused on the impact of regime changes of recessions on stock market volatility. Also, this paper includes output growth in both mean and variance equation. Inflation variable is included in both mean and variance equation to test the validity of the Adaptive Expected Fisher Effect to build some inferences about the central behaviour. 6 R k O(9) I ? a?( u ow kt t i tputGr th)t i b?i (Inflation)t i t ?i 1 i 1 2t? ? ? (10) 2 ? ?21 1 ? k u kt t ?i (OutputGrowth)t i ?i (?Inflation)t i i 1 i 1 Next, the Inflation-Output-Interest Rate-Stock Return (IOIS) models as shown in Eq.(11) and Eq.(12) are employed to analyse the effect of inflation, output growth, and interest rate changes on stock return and volatility simultaneously. R ? k ? (11) k kt I t ai (OutputGrowth)t i b?i (Inflation)t i c?i (Interest)t i t ?i 1 i 1 i 1 2? ? ? 2 ? ?2 k k kt t 1 t 1 ? u?i (OutputGrowth)t i ?i (?Inflation)t i ?i?(Interest)t i i 1 i 1 i 1 (12) The specification in Eq.(5) in EGARCH (1,1) model is used should there be the existence of asymmetric volatility evidence to replace the specification in Eq.(10) and Eq.(12). 4. Empirical Results Descriptive Statistics Table 2 reports the descriptive statistics for stock return, inflation, output growth and interest rate for all seven countries. Results reveal that the average monthly stock returns ranged from 0.40% to 1.51%. Philippines has the highest stock returns with the highest standard deviation (17.66%), i.e., high return is accompanied by high risk. The rest of the countries tend to share about the same level of variation with respect to stock returns. For inflation, it ranged from 0.09% (Singapore) to 0.73% (Philippines). Results indicate that the monthly stock returns volatility and inflation are higher for EM than MM on the average (except for Singapore). Also, high inflation country like Philippines tend to have higher stock returns, while low inflation country like Singapore is associated with relatively lower returns. In term of output growth, Asia EM is higher as compared to US and Japan. Output growth rate is most volatile in Philippines (0.87%), while it is lowest in the US (0.25%). The rest of the countries do not appear to differ much with respect to output deviations. Japan has the highest average interest rate growth during the sample period, with a negative monthly growth rate of 0.53%, while Malaysia is experiencing the lowest interest rate growth of 0.007%. US, Malaysia and Korea are having relatively lower deviation, and Japan, Singapore, Philippines, Korea and India are having large deviation with respect to interest rate. Table 2: Descriptive Statistics Country Stock Return Inflation Output Growth Interest Rate Sample Mean Std Mean Std Mean Std Mean Std Period 7 Deviation Deviation Deviation Deviation Japan 0.574466 4.452786 0.283798 0.714241 0.428252 6.787222 -0.53759 23.49894 M8:1957- M12:2007 US 0.548543 3.454901 0.331810 0.324714 0.250383 2.329236 -0.01636 7.493839 M8:1957- M12:2007 Singapore 0.402442 7.277752 0.086397 0.414721 0.577815 11.56442 0.40310 25.43392 M1:1997- M12:2007 Malaysia 0.562080 6.762946 0.251701 0.377240 0.656489 6.573378 -0.00735 7.520232 M1:1980- M12:2007 Korea 0.874470 6.646702 0.438906 0.673037 0.758073 5.883138 -0.45221 7.329097 M1:1980- M12:2007 Philippines 1.518202 17.66026 0.725549 1.054933 0.876222 6.019563 -0.23817 25.64854 M1:1981- M12:2007 India 0.978856 5.555704 0.594263 1.058500 0.509464 5.978584 0.15017 26.95897 M1:1960- M12:2007 Time Varying Volatility The result of the estimated coefficients for the standard GARCH (1,1) model is reported in Table 3. All countries show significant time-varying volatility in stock returns. It shows that the standard specification of GARCH (1,1) fit the countries under study. To diagnose if stock returns is associated with asymmetric volatility, EGARCH (1,1) is employed, and the estimation result is reported in Table 4. Results reveal that six countries are better fitted with EGARCH (1,1) as compared to the standard GARCH models, which implies that the present of asymmetric volatility in stock returns. Those six countries are Japan, USA, Malaysia, Singapore, Philippines and India, while GARCH (1,1) model is hold for Korea. From Tables 3 and 4, there are significant evidences to show that time-varying volatility on stock returns for both MM and EM. This result is consistent with the claimed made by Bekaert and Harvey (1997), Aggarwal et al. (1999), and Davis and Kutan (2003) for emerging market returns. Table 3: GARCH (1,1) estimates for stock returns Country Mean Equation Conditional Variance Equation Constant Constant ? ? Japan 0.875360* 1.213492* 0.286685* 0.683251* USA 0.634455* 1.086189*** 0.093248** 0.811681* Malaysia 1.099107* 2.267284** 0.244410** 0.716426* Singapore 0.929438** 0.459555 0.171899** 0.831281* Korea 0.846179** 1.299712*** 0.118007*** 0.850580* Philippines 0.522986 7.968690 0.043099*** 0.938478* India 0.391058** 0.088762 0.093413* 0.910270* Note: Asterisks (*, **, ***) denote statistical significance at the 1%, 5% and 10% levels respectively. Table 4: EGARCH (1,1) estimates for stock returns Country Mean Equation Conditional Variance Equation Constant Constant ? ? ? Japan 0.685643* 0.000498 0.429140* 0.874808* -0.107802*** USA 0.558887* 0.249281** 0.190709** 0.829840* -0.175852* Malaysia 0.626919 0.156752 0.399690* 0.862835* -0.179450** Singapore 0.751980*** 0.016866 0.219286** 0.947531* -0.126465*** Korea 0.903452** -0.000827 0.217954*** 0.952143* -0.020691 Philippines 0.525772 5.471884* 0.873788* -0.116382 0.257125** India 0.441083** 0.017654 0.002617 0.993290* 0.068710* Note: Asterisks (*, **, ***) denote statistical significance at the 1%, 5% and 10% levels respectively. 8 Table 5: The cumulative impact of output growth and inflation on stock returns and volatility (3 month horizon) Country Returns Volatility Diagnostic Tests 3 put n ? Out 3 3 ? Output 3 Inflation i 1 ? Inflatioi 1 i 1 i ?1 Japan 0.136771*** -0.151951 -0.050500** -0.045010 Q= 13.061 Q2=6.5791 JB=40.86761* ARCH=0.015038 USA -0.256549** -2.087616* 0.033037 0.339693* Q= 11.524 Q2=10.733 JB=35.27363* ARCH=0.046730 Malaysia -0.198354 -0.109171 -0.003022 0.030926 Q= 8.4543 Q2= 8.7637 JB= 9.726066* ARCH= 0.302208 Singapore -0.103561 -2.318395 -0.062054*** 0.478050 Q= 13.847 Q2= 14.597 JB= 3.155927 ARCH= 1.707335 Korea -0.018636 -1.565998* 0.036697*** 0.091288* Q= 4.9418 Q2= 6.9164 JB= 0.140441 ARCH= 0.049390 Philippines -0.057436 -0.411307* 0.230536* -1.333707* Q= 14.870 Q2=3.9086 JB=253.4596* ARCH=0.037232 India 0.091173 -0.184002 0.017038** 0.034790* Q= 12.031 Q2=4.8228 JB=1.876478 ARCH=0.105341 Notes: F-statistics (Using Wald-Coefficient Restriction) are for the statistical significance of the sum of three coefficients. Q-test is the test for serial correlation with lag 12, Q2-test is the test for dependency in squared residuals at lag 12, and ARCH is the F-statistics for ARCH LM Test with lag 1. Asterisks (*, **, ***) denote statistical significance at the 1%, 5% and 10% levels respectively. Predictive Power of Output and Inflation To analyse the predictive power of output growth and inflation on monthly stock returns and the volatility, both output growth and inflation are set to be the exogenous variables for the mean and conditional variance simultaneously. The diagnostic tests indicate no evidence of significant autocorrelation and no evidence of dependency in squared residuals for all these countries. The impact of changes in output growth and inflation on stock returns over a three-month horizon are reported in Table 51. Results reveal that there is significant predictive power of output growth on monthly stock returns for both Japan and US. Over a three-month period, a 1% increase in output growth rate is associated with a cumulative effect of 0.136% raise and 0.256% fall in monthly stock return for Japan and US respectively. 1 Only the results with respect to output and inflation are reported here. Past stock returns and the estimates of the GARCH coefficients including the coefficient for dummies are not reported here. 9 There is a positive (negative) relationship between output growth rate and stock return in Japan (US). Positive output growth rate can boost up investors confidence in the market. Meanwhile, there is no significant output predictive power on stock returns in EM. The negative relation between inflation and stock return is consistent with Gregoriou et al.?s (2009) study. It is possible when the transmission mechanism for real money into inflation is through consumption growth. In the long run, the monetary expansion on inflation cancels in response to similar increases in real stock returns. That is to say, consumptions react positively to stock returns, but these responses are reduced by the impact of inflation. The negative relation between output growth and stock return could be due to temporary higher level of economic activity and is expected to follow trend reversion; causing current saving reduces stock returns (Tsouma, 2008). There is significant negative predictive power of inflation on stock returns. The impact of inflation on stock returns is significant only for US (-2.09%), Korea (-1.57%) and Philippines (-0.41%). This implies that a 1% increase in inflation in US reduces the stock returns by 2.09% over a three-month period. This result is also consistent with Ely and Robinson (1991), Kaul (1987), Solnik (1983), and Mundell (1963). Pigou real wealth effect shows that the real rate of interest is negatively related to expected inflation. Progressive taxes effect explains the negative impact of inflation on stock returns. Current returns depend on future cash flows. If future tax liabilities increase due to the higher expectation of future inflation, it will reduce future cash flows and result in lower stock return. For stock return volatility, generally the results show that most countries show significant impact of output movements on stock return volatility, where Asia EM depicts stronger evidence. Philippines (0.23%), Korea (0.04%) and India (0.02%) show significant positive impact of output growth on stock returns volatility, while significant negative impact for Japan (-0.05%) and Singapore (-0.06%); the latter exhibits higher volatility as compared to the former (Table 2). This implies that MM with relatively higher output volatility is associated with higher conditional stock returns volatility, vice versa; while the result is mixed for EM. The effect of inflation on the conditional stock market volatility is significant for US (0.34%), Korea (0.09%), Philippines (-1.33%) and India (0.03%). The result indicates that the evidence of inflation volatility on stock return volatility is relatively strong for Asia EM. Low inflation rates countries tend to have unreceptive effect on volatility than higher inflation rates countries. High inflation rate country like Philippines adversely affected stock market volatility. Table 6: The cumulative impact of output growth, inflation and interest rate on stock returns and volatility (3 month horizon) Country Returns Volatility Diagnostic Tests n 3 ? Output 3 rest rate erest rate i 1 ? Inflation3i 1 ? Inte i 1 ? Output Inflatio3 3 ?i 1 i 1 ? Int3i 1 Japan 0.143693** -0.227211 -0.004788 -0.043622*** -0.034618 -0.001289 Q=13.023 10 2 Q =6.5167 JB=41.53867* ARCH=0.002812 USA -0.214557*** -2.159412* 0.012071 0.063496*** 0.366155* -0.012949** Q=11.348 Q2=11.448 JB=34.65016* ARCH=0.036594 Malaysia -0.207718*** -0.044447 -0.086887 0.006155 0.119427 -0.019032** Q=10.964 Q2=6.6110 JB=8.757416** ARCH=0.663850 Singapore -0.137228 -2.237742 -0.055021*** -0.093861* 0.415210 -0.001792 Q=11.265 Q2=10.888 JB=3.504884 ARCH=1.315481 Korea 0.048481 -1.567078* -0.045803 0.036611*** 0.095203* 0.000293 Q= 5.2773 Q2= 6.0421 JB= 0.210542 ARCH= 0.021811 Philippines 0.367085* -0.701649* -0.122073* 0.215960* -1.074164* 0.028789** Q=14.696 Q2=6.6923 JB=232.0956* ARCH=0.036108 India 0.072845 -0.212485 -0.003797 0.015493 0.033883* 0.000590 Q=10.998 Q2=6.3674 JB=1.234169 ARCH=0.072466 Notes: F-statistics (Using Wald-Coefficient Restriction) are for the statistical significance of the sum of three coefficients. Q-test is the test for serial correlation with lag 12, Q2-test is the test for dependency in squared residuals at lag 12, and ARCH is the F-statistics for ARCH LM Test with lag 1. Asterisks (*, **, ***) denote statistical significance at the 1%, 5% and 10% levels respectively. Inflation-Output Growth-Interest Rate-Stock Return (IOIS) Model By including interest rate changes, it improves the predictive power of output growth on stock returns. In terms of the impact of output growth on stock returns, there is improvement in IOIS model (Table 6) as compared to the previous model (Table 5). There is no autocorrelation pattern in the series for IOIS model, as shown from Q statistics, Q2 statistics and ARCH tests. Four countries show significant evidence where Japan (0.14%) and Philippines (0.37%) demonstrate positive relation between output growth and stock returns, and US and Malaysia (-0.21%) illustrate negative impact. Positive output growth would cause explicit earning growth and result in higher expected future dividends and ultimately increase stock returns. Meanwhile, according to Ang (2008), economic booms encourage the adoption of a riskier behaviour and encourage speculative economic activities which may create over-leveraged situation. The instability of stock speculation regarding over-leveraged situations can have severe negative consequences for an economy due to psychological factors. In term of the impact of inflation on stock returns, results revealed that there is no significant change in the IOIS Model as compared to the previous model. US, Korea and Philippines have significant negative relation between inflation and stock returns. If the government is not able to obtain sufficient revenue to finance its sizeable expenditures, which will probably happen during economic crisis and stock return usually drop, the expected inflation would rise if the deficits are monetised, and result in negative relationship between inflation and stock returns. Out of seven countries, only Singapore and Philippines show significant negative relationship between interest rate changes and stock returns. The increase in interest rates for mere reasons of arbitrage means higher 11 financing cost for investment in production and stock investment. Higher investment cost will cause a fall in future earning due to the plunge in future production, which will lead to a reduction in future cash flows and hence leading to a reduction in stock returns. Besides that, higher interest rate will lead to higher cost to finance stock investment, hence reduce the willingness of investor in stock investment and fall in stock price. The evidence of output growth on stock return volatility in IOIS Model is relatively strong with five countries out of seven countries show significant results. US (0.06%), Korea (0.04%) and Philippines (0.22%) show positive relation between output growth on stock return volatility, and Japan (-0.04%) and Singapore (-0.09%) show negative relations. Meanwhile, the predictive power of inflation on stock return volatility in IOIS Model is akin with the result in Table 5. It is noteworthy that Philippines exhibits a negative relationship between inflation and stock return volatility, while USA, Korea and India evidently show positive relation. The amplitude of inflation on stock return volatility is substantial for Philippines. Therefore, policy maker should take precaution steps to manage inflation due to its large impact on stock return volatility. On the other hand, the predictive power of interest rate changes on stock return volatility is moderate. US and Malaysia show negative relation. Such negative relation suggesting that the removal of interest rates controls may promote financial development. Only Philippines shows positive relation between interest rate and stock volatility, suggesting that financial liberalisation appears to have positive effect on asset return progress. However, the amplitude of the impact is relatively small. 5. Conclusions Using GARCH and EGARCH models, this paper examines the predictive power of output growth and inflation on stock return and its volatility. Results reveal strong evidence of output growth and inflation on stock return volatility. The results have proved a priori that there is evidence where more evidences were showed by Asia financial crisis countries as compared to US and Japan. The evidence of output growth on stock return is extremely different between MM and Asia crisis EM as both US and Japan show significant evidence that output growth acts as an important determinant to determine stock returns, but none of the EM under study reveals significant results. It is noteworthy that the evidence of predictive power of inflation on stock return does not support the Adaptive Fisher effect in stock return. This could be justified using Pigou real wealth effect. Progressive taxes effect might explain the negative impact of inflation on stock returns. Generally, the negative impact of inflation to predict stock return is significant in US, Korea and Philippines. Results reveal that four out of seven countries show significant evidence of the predictive power of inflation on stock returns volatility. High inflation influences stock returns volatility in Philippines. This result provides important insight for policy maker to control inflation to reduce the stock return volatility since higher volatility means higher risk for investor. The result from IOIS Model shows that the inclusion of interest rate changes improves the prediction power of the output growth on the stock return. Japan and Philippines 12 show positive relation in which positive output growth will cause positive earning growth and bring better expected future dividends and ultimately cause the stock return to increase. Even with the inclusion of interest rate changes, results do not support Fisher Effect. The negative relationship between inflation and stock return might be caused by the deficit fiscal policy carried out by the government. If the government fails to obtain sufficient revenue to finance its large expenditures, the expected inflation will increase and bring negative effect on stock return. The result also proves that interest rate changes do have some significant influences on stock return volatility in US, Malaysia and Philippines although the direction of impact is ambiguous. For future study, there is much room for improvement to examine other factors that influence the stock return and its volatility such as money supply growth rate, saving rate, income per capita, political stability, stock market regulation and etc. One may extend the sample period across the sample countries to improve the predictive power of the model. References Aggarwal, R., Inclan, C. and Leal, R. (1999) Volatility in emerging stock markets. Journal of Financial and Quantitative Analysis, 34(1), 33-55. Ang, J.B. (2008) What are the mechanisms linking financial development and economic growth in Malaysia, Economic Modelling, 25, 38-53. Balvers, R.J., Cosimano, T.F., and McDonald, B. (1990) Predicting stock returns in an efficient market. Journal of Finance, 45(4), 1109-1128. 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Posted: 24 May 2010

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