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Nature of Volatility in Indian Stock Market – An Empirical Analysis
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The plots of market volatility can act as an electrocardiogram, reflecting the pulse of capital markets. It has been identified that stock indices in many developed as well as developing economies exhibit volatility clustering, which in finance literature is called “time-varying conditional volatility”. The purpose of the study is to characterize the time-varying volatility of the Indian capital market in the long run. The study focus on aspects like time varying volatility, the predictability of volatility, the symmetric nature of volatility towards positive and negative shocks, and the explanation for high volatility of Indian capital market for a period of 20 years. This study has analyzed the price data as well as return data of NSE CNX Nifty. We have used GARCH (1,1) Model to estimate time varying volatility and TARCH (1, 1) Model, to measure asymmetric volatility effect. The magnitude of volatility was found to fluctuate between periods and showed a repeating trend over time. It validates the asymmetric nature of volatility in Indian capital market and volatility clustering in the long run and give insights on the predictability of volatility.
Title: Nature of Volatility in Indian Stock Market – An Empirical Analysis
Description:
The plots of market volatility can act as an electrocardiogram, reflecting the pulse of capital markets.
It has been identified that stock indices in many developed as well as developing economies exhibit volatility clustering, which in finance literature is called “time-varying conditional volatility”.
The purpose of the study is to characterize the time-varying volatility of the Indian capital market in the long run.
The study focus on aspects like time varying volatility, the predictability of volatility, the symmetric nature of volatility towards positive and negative shocks, and the explanation for high volatility of Indian capital market for a period of 20 years.
This study has analyzed the price data as well as return data of NSE CNX Nifty.
We have used GARCH (1,1) Model to estimate time varying volatility and TARCH (1, 1) Model, to measure asymmetric volatility effect.
The magnitude of volatility was found to fluctuate between periods and showed a repeating trend over time.
It validates the asymmetric nature of volatility in Indian capital market and volatility clustering in the long run and give insights on the predictability of volatility.
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