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A Study on Volatility of S&P CNX Nifty in Indian Securities Market


Affiliations
1 Management Education and Research Institute, New Delhi, India
     

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S&P CNX Nifty is national stock exchange of India’s benchmark stock market index for Indian equity market. The present paper attempts to investigate whether the Previous day’s Nifty returns has an explanatory power for today’s Nifty returns. Daily data of sample of 3671 observations from January 2000 to December 2015 has been used for the study. CNX Nifty returns are calculated as the first difference of the log of the daily closing price. GARCH (1,1) has been developed to model the volatility of CNX Nifty returns. The GARCH model is a time series modeling technique which provides for Heteroscedasticity in the observed returns. Empirical results have shown that previous day’s Nifty returns have GARCH effect in the today’s Nifty returns.

Keywords

Heteroscedasticity, ARCH, GARCH, Volatility, CNX Nifty, Lagged Returns.
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  • A Study on Volatility of S&P CNX Nifty in Indian Securities Market

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Authors

Kavita
Management Education and Research Institute, New Delhi, India

Abstract


S&P CNX Nifty is national stock exchange of India’s benchmark stock market index for Indian equity market. The present paper attempts to investigate whether the Previous day’s Nifty returns has an explanatory power for today’s Nifty returns. Daily data of sample of 3671 observations from January 2000 to December 2015 has been used for the study. CNX Nifty returns are calculated as the first difference of the log of the daily closing price. GARCH (1,1) has been developed to model the volatility of CNX Nifty returns. The GARCH model is a time series modeling technique which provides for Heteroscedasticity in the observed returns. Empirical results have shown that previous day’s Nifty returns have GARCH effect in the today’s Nifty returns.

Keywords


Heteroscedasticity, ARCH, GARCH, Volatility, CNX Nifty, Lagged Returns.

References





DOI: https://doi.org/10.25089/MERI%2F2018%2Fv11%2Fi2%2F173960