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
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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
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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.
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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
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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.
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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.
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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
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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
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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.
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