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Volatility Interactions Across Indian and Chinese Stock Markets


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1 Delhi School of Economics, University of Delhi, India
     

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Volatility is an important component in risk return analysis of financial assets. It imparts liquidity to the financial system and also serves as an information source for rational decision making. Since the latter half of the 20th century, volatility in stock returns has been found to be time varying and exhibiting patterns and therefore, various models have been developed to capture such dynamic properties of volatility. The introduction of Autoregressive Conditional Heteroscedasticity (ARCH) models by Engle in 1982 has led to a better understanding of the behaviour of stock market volatility than the traditional measures including standard deviation. The present study attempts to model various aspects including clustering, leverage effect and spillover effect of stock market volatility in Indian and Chinese stock markets during 2001-2016 using daily time-series data with Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models. Volatility has been seen to be highly persistent in both the markets. The T-GARCH model has been applied in order to assess the presence of information asymmetry that bad news impacts volatility more than good news. Our results reveal that both Indian and Chinese stock markets’ volatility shows time varying behaviour. The theoretical reasoning of the asymmetric impact of news that bad news affects volatility more than good news has been confirmed in both markets. Furthermore, the spillover effect of volatility across the two markets has been tested using the T-GARCH-X model. The results show unidirectional spillover effect of volatility from Chinese stock market to the Indian stock market. This implies that shocks from Chinese stock market impact conditional volatility in the Indian stock markets only but not vice-versa.

Keywords

T-GARCH, Leverage Effect, Spillover, Volatility Clustering.
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  • Volatility Interactions Across Indian and Chinese Stock Markets

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Authors

Khalid Ul Islam
Delhi School of Economics, University of Delhi, India
Sartaj Hussain
Delhi School of Economics, University of Delhi, India

Abstract


Volatility is an important component in risk return analysis of financial assets. It imparts liquidity to the financial system and also serves as an information source for rational decision making. Since the latter half of the 20th century, volatility in stock returns has been found to be time varying and exhibiting patterns and therefore, various models have been developed to capture such dynamic properties of volatility. The introduction of Autoregressive Conditional Heteroscedasticity (ARCH) models by Engle in 1982 has led to a better understanding of the behaviour of stock market volatility than the traditional measures including standard deviation. The present study attempts to model various aspects including clustering, leverage effect and spillover effect of stock market volatility in Indian and Chinese stock markets during 2001-2016 using daily time-series data with Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models. Volatility has been seen to be highly persistent in both the markets. The T-GARCH model has been applied in order to assess the presence of information asymmetry that bad news impacts volatility more than good news. Our results reveal that both Indian and Chinese stock markets’ volatility shows time varying behaviour. The theoretical reasoning of the asymmetric impact of news that bad news affects volatility more than good news has been confirmed in both markets. Furthermore, the spillover effect of volatility across the two markets has been tested using the T-GARCH-X model. The results show unidirectional spillover effect of volatility from Chinese stock market to the Indian stock market. This implies that shocks from Chinese stock market impact conditional volatility in the Indian stock markets only but not vice-versa.

Keywords


T-GARCH, Leverage Effect, Spillover, Volatility Clustering.

References