Asymmetric volatility of the Thai stock market in crisis periods Chiradul Pitaktam 5910341007*
Abstract Since the 2004 uprising, Thailand has experienced ongoing political unrest and encountered the US subprime crisis which has damaged the country’s financial markets. This article aims to investigate the existence and leverage effect and volatility feedback effect in the stock market returns of Thailand during the crisis period. The GARCH in-mean models are conducted in this analysis including GJR, EGARCH, and PGARCH. The result indicates the existence of asymmetry volatility of the leverage effect and volatility feedback effect. The evidence presented in this article provides important implications for investors and portfolio speculators.
Keywords: Asymmetric volatility, feedback effect, leverage effect, political unrest, subprime crisis
* School of Development Economics, National Institute of Development Administration (NIDA)
118 Sereethai Road, Klong-Chan, Bangkapi, Bangkok 10240, THAILAND - Email:
[email protected]
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1. Introduction The relationship between stock index return and its volatility found by the previous studies show some negative and contrast with some positive. The leverage effect and volatility feedback are two perspectives to explain the contradiction. The leverage effect proposes the stock return shocks lead to changes in conditional volatility (Black, 1996 and Christie, 1982). If the leverage takes effect, the negative return shocks lead to higher subsequent volatilities. In contrast with Bekaert and Wu (2000), the volatility feedback affects the changes in conditional volatility lead to changes in stock return shocks. This study focus on the stock return shocks and conditional volatilities importance to the monetary and financial mechanism during the crisis period. The September 2004 uprising of Thailand encountered by the political unrest that weight heavily to the financial market. The political problem polarizes for ten years with broad impact on the people well-being and economic activity in the country. The US subprime crisis occurs in between this period and affects the Thai's financial market. Figure 1 shows the trend of the stock index of Thailand from 2nd July 1997 to 24th August 2015 those include the political unrest period on 27th September 2004 until 22nd May 2014 and the US subprime. At that period the index moves in the narrow frame, then more fluctuate in the later time.
Thailand SET index 1800.000 1600.000 1400.000 1200.000 1000.000 800.000 600.000 400.000 200.000 0.000
Figure 1 Stock Exchange of Thailand index from 2nd July 1997 to 24th August 2015 Source: Stock Exchange of Thailand (2017) This article aims to investigate the existence of leverage effect and volatility feedback effect between the stock return and its volatility along the uprising period. There are three parametric generalize autoregressive conditional heteroskedastic-in-mean (GARCH-M) models with corresponding to GJR, EGARCH and PGARCH model, respectively. The model applies to the daily stock market return data of Stock exchange of Thailand during 2nd July 1997 to 24th August 2015. The finding indicates that the volatility feedback and leverage effect are presented in the Thai stock market. The paper shows the following. Next section presents prior research. Section 3 presents data and the estimation methods. Section 4 presents empirical results. The final section concludes. 2
2. Prior Research The relationships between stock index return and its volatility have been investigated in many studies. It can be classified into three group of research. First, the leverage effect points that stock return shocks cause changes of conditional volatility. Christie (1982) study the stochastic behavior of common stock variance and investigate the relationship between the equity return variation and other explanatory variables. The study expresses the particular mechanism of leverage effect in the stock market by the negative shocks leads to increase their volatility. Second, the feedback effect stated that the changes in volatility lead to a change in return stock. Nelson (1991) studies the conditional heteroscedasticity in asset return present a new approach of ARCH models that do not suffer from some of the disadvantages of GARCH models. The study found a negative risk-return relationship which satisfies the volatility feedback effect from the risk premium. Campbell and Hetschel (1992) apply the generalized autoregressive conditionally heteroskedastic (GARCH) model of return to allow for volatility feedback effect. The volatility feedback on the large negative stock return that leads to the potential large crashes expressed from the study. Bekaert and Wu (2000) provide a hypothesis test to simultaneously forecast asymmetric volatility and to examine the leverage effects and time-varying risk premiums. The result shows conditional covariance with the market increase only significantly following negative market news. Third, there are some further studies that show the controversial relationships between stock return shock and its volatility. Brandt and Kang (2004) find that the conditional correlation between the mean return and volatility is negative, but the unconditional correlation is positive due to lead and lag correlation. Hatemi and Irandoust (2011) used the leveraged adjustment and ARCH and found the volatility cause return negatively and return cause volatility positively. Mukhopadhyay and Sarkar (2013) study stock returns under alternative volatility and a distributional assumption by employing daily data with three models of "leverage effect" in return including EGARCH, TGARCH and PARCH. The PARCH gives the best result for both in-sample and out-of-sample forecasts that implies the significant leverage in the stock market. From the original paper of this study, Thakolsri, Sethapramote, and Jiranyakul (2015) study asymmetric volatility of the Thai stock market. This study employs the daily data from the stock exchange of Thailand during the period 4th January 2005 to 27th December 2013 which happen the Subprime crisis. The result from GJR, EGARCH, and PGARCH show the minimal effect of Subprime to stock return volatility. These models associated with feedback and leverage effect imply the portfolio diversification and risk management.
3. Data and Methodology 3.1 Data descriptions We employ the first difference of log daily data from Stock Exchange of Thailand with the number of 4,441 observations starting from 2nd July 1997 to 24th August 2015 including the political unrest that might affect the volatility of stock market return. Political unrest period is the period of time between 27th September 2004 until 22nd May 2014. In the period of crisis, issuing the regulation 3
of emergency management by military combined with Subprime crisis destroy the confidence of Thai and foreign people and lower the economic growth and financial market growth. Table 1 Descriptive statistics of equity sector index return Mean 0.000186 Median 0.000238 Maximum 0.113495 Minimum -0.160633 Standard deviation 0.01616 Skewness 0.029195 Kurtosis 10.65205 Jarque-Bera statistic 10835.55 ADF statistic (constant only) -13.0172(0.000) KPSS 0.126602*** 2 ARCH: Q (4) 763.37(0.000) Number of observations 4441 Note: The number in parenthesis is p-value *, **, *** is significant level at 10%, 5% and 1% respectively Table 1 shows descriptive statistics of stock market return. The mean of this series approach zero, the skewness is positive and kurtosis which is greater than 3 implies leptokurtic distribution. The rejection of Jarque-Berra null hypothesis means the stock market is not a normal distribution. For the stationary testing, this paper applies ADF test which is greater than the critical value at 1 percent of the significant level consistent with KPSS test, so this series is stationary. Moreover, this series also demonstrates the ARCH effect, it can be applied GARCH model to estimate conditional volatility. 3.2 Estimation methods The GARCH in mean or GARCH-M is employed to test the validity of leverage and volatility feedback effect that allow for testing for asymmetric effect of negative shocks and positive shocks. The mean equation follows the autoregressive of p order (AR(p)) process, which is specified in equation (1) p
rt b0 bi rt i c t t
(1)
i 1
where r is the stationary series of stock market return and is shock which positive is good news and negative is bad news. The term is the conditional volatility which can be standard deviation or conditional variance depends on type of GARCH model. The conditional variance equations are specified in equation (2), (3), and (4) 2 2 𝜎𝑡2 = 𝛼0 + 𝛼1 𝜎𝑡−1 + 𝛼2 𝜀𝑡−1 + 𝛼3 𝜀(−)2𝑡−1 + 𝜑𝐷𝑡
(2)
Glosten, Jaganathan, and Runkle (1994) illustrate how good news and bad news have a different effect on volatility. From equation 2 , t 1 0 is a threshold that separate the different effects to 4
volatility. The shocks that greater than the threshold have different effects than shocks below the threshold. The term ()t21 can be restated as d t 1 t21 where d t 1 is a dummy variable which is equal to one if previous shock is bad news ( t 1 0) and is equal to zero if previous shock is good news ( t 1 0) . When t 1 0, d t 1 1 the effect of the previous shock is ( 2 3 ) t21 . If 3 0 , then the negative shocks will have greater effects on its volatility than positive shocks. Exponential GARCH (EGARCH) and the conditional variance of EGARCH can be represented by equation (3). 2 𝑙𝑜𝑔(𝜎𝑡2 ) = 𝛼 + 𝛽 𝑙𝑜𝑔(𝜎𝑡−1 )+ 𝛾
𝜀𝑡−1 2 √𝜎𝑡−1
𝜀𝑡−1
+𝜆|
2 √𝜎𝑡−1
| + 𝜑𝐷𝑡
(3)
Nelson (1991) proposed an exponential specification on GARCH which does not require nonnegativity constraints. EGARCH is specified by log-linear form and the volatility is log( t2 ) . 2 EGARCH model uses the standardize of t 1 which is 𝜀𝑡−1 ⁄√𝜎𝑡−1 . The positive term provide the conditional variance will be affected by the magnitude of (𝛾 + 𝜆) in contrast with negative term, the effect of shock on the log of conditional variance is (−𝛾 + 𝜆).
Finally, the power GARCH (PGARCH) captures the leverage effect which model by Taylor (1986) and Schwert (1989) use the conditional standard deviation as a measure of volatility instead of the conditional variance. This model is generalized by Ding et al. (1993) using the PGARCH model as follows; 𝛿 𝜎𝑡𝛿 = 𝛼0 + 𝛼1 (|𝜀𝑡−1 | − 𝛾1 𝜀𝑡−1 )𝛿 + 𝛽1 𝜎𝑡−1 + 𝜑𝐷𝑡
(4)
where 1 and 1 are ARCH and GARCH parameter, 1 is the leverage parameter, and the condition imply the power parameter. In the PGARCH model, if 1 ≠ 0 , this captures asymmetric effects. In addition, PGARCH can be restated by increase the flexibility of the considering as another coefficient that must also be estimated. Moreover, these variance equations include the dummy variables vector (Dit) which is 1 during crisis and 0 if otherwise. Dst subprime crisis is in the period from December 2007 to June 2009. Dct represents the existing of Thai’s political unrest which is the period from September 2004 until May 2014. The vector of dummy variables is generated to capture the effect of crisis that result in the conditional volatility.
4. Empirical result The estimation of equation (1) by using the conditional volatility of GARCH models according to equations (2), (3) and (4) of GJR, EGARCH, and PGARCH respectively are shown in Table 2. Additionally, the results of Ljung-Box test show these estimate equations have no serial correlation and no ARCH effect form all models.
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The result from Table 2 shows the coefficient of the conditional volatilities those are estimated by GJR-GARCH, EGARCH and PGARCH are negative effects and significant to the return of the set index according to the sign of the coefficient in the mean equation. Additionally, GJR-M model that report in Panel A of Table 2 shows non-zero 𝛼3 , in the variance equation, implies the asymmetry variance between positive and negative news effect to the return. The positive 𝛼3 examine the negative shock has the greater impact on the volatility than the positive shock. Additionally, the non-zero with a negative sign of Gamma in EGARCH-M from Panel B and the non-zero with a positive sign of PGARCH-M from Panel C explain the asymmetry of stock return shock. The standardize residual term of EGARCH variance equation show the higher negative shock magnitude in 𝛾 + 𝜆 while the lower magnitude is −𝛾 + 𝜆 from positive shock. Moreover, the negative shock of PGARCH enlarge the conditional variance and multiply with 𝛼1 . The coefficient of subprime crisis dummy variable in all model expresses the positive impacts of the conditional variance in contrast with the coefficient of political unrest dummy that shows the negative impacts to the variance equation. Both of situations have effects to the Thai stock market but minimal. These three models of conditional volatility equations are able to explain the impact of positive and negative impact of shock to the stock market return. The negative shock has the larger impact than the positive shock to the market return supported by the theory of NIC (News Impact Curve) expressed in the study of Pagan and Schwert (1990) and Engle and Ng (1993). However, the PGARCH-M tend to be the best model to represent the market return because this model is able to provide the better log-likelihood value than other models. The asymmetric of the conditional volatility of the stock market which illustrates the higher impact from negative shocks than the positive shocks and also the impact of the US subprime crisis to Thai stock market relates to the previous study of Thakolsri, Sethapramote, and Jiranyakul (2015).
5. Conclusion This paper demonstrates the existing of asymmetry volatility of Thai stock market during the period of crisis. The daily data of stock return is employed to set the three asymmetric GARCHM models including, GJR, EGARCH, and PGARCH to capture the effect of conditional volatility. The results showed that all three models significantly explain the asymmetry volatility of Thai stock market. This result also supports the volatility feedback effect and leverage effect since 1) the negative volatility causes market return which supports the volatility feedback effect and 2) the negative shocks result in higher than positive shocks that imply the validity of asymmetry volatility and favor leverage effect. Furthermore, the effect of crisis can be separately concluded that 1) the subprime crisis has a positive impact on volatility and 2) the political unrest in Thailand has a negative effect on volatility that both of impacts are little effect. International investors and portfolio speculators should consider factor both within and outside country that influences the fluctuation in the market especially emerging market by using optimize diversification and risk management.
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