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Volatility Analysis of National Stock Exchange of India


     

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The paper investigates the nature and pattern of Volatility of National Stock Exchange (NSE)'s price index namely S&P CNX Nifty. The data include daily observations for NSE price index covering period from 1st January, 2000 to 10th September, 2007. Various volatility estimators and diagnostics tests suggest certain stylized facts about volatility like volatility clustering, mean reverting and asymmetry. Lagrange Multiplier test indicates the presence of ARCH effect in the stock market. The paper applies family of ARCH models to examine the asymmetric volatility of the NSE. We find that first order GARCH model fits the data better than high order ARCH models. Our analysis suggests that the EGARCH and TARCH models outperform the conventional symmetrical GARCH models. The estimated TARCH and EGARCH parameters show that the impact of news is asymmetric, indicating there is an existence of leverage effect in future price of the stock. The Leverage effect is captured well by TARCH models in Nifty. Application of ARCH-M models found no strong evidence for high return during the period of high volatility.

Keywords

Volatility, Volatility clustering, ARCH, GARCH, EGARCH, TARCH, ARCH-M, JEL Classification: C22, C52
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  • Volatility Analysis of National Stock Exchange of India

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Authors

Abstract


The paper investigates the nature and pattern of Volatility of National Stock Exchange (NSE)'s price index namely S&P CNX Nifty. The data include daily observations for NSE price index covering period from 1st January, 2000 to 10th September, 2007. Various volatility estimators and diagnostics tests suggest certain stylized facts about volatility like volatility clustering, mean reverting and asymmetry. Lagrange Multiplier test indicates the presence of ARCH effect in the stock market. The paper applies family of ARCH models to examine the asymmetric volatility of the NSE. We find that first order GARCH model fits the data better than high order ARCH models. Our analysis suggests that the EGARCH and TARCH models outperform the conventional symmetrical GARCH models. The estimated TARCH and EGARCH parameters show that the impact of news is asymmetric, indicating there is an existence of leverage effect in future price of the stock. The Leverage effect is captured well by TARCH models in Nifty. Application of ARCH-M models found no strong evidence for high return during the period of high volatility.

Keywords


Volatility, Volatility clustering, ARCH, GARCH, EGARCH, TARCH, ARCH-M, JEL Classification: C22, C52

References