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A Study on Stock Market Volatility Pattern of BSE and NSE in India


Affiliations
1 Rourkela Institute of Management Studies, Rourkela
2 Centurion University, Bhubaneswar, India
     

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The investors, policy makers and researchers give lot of attention towards evaluation of risk while analysing the stock market. The quality of risk measures very largely depends on how well the econometric model captures behaviour of underlying asset. The paper examines the relationship between returns and volatility, volatility clustering, leverage effect and the persistence of volatility for the Indian stock markets viz. National Stock Exchange(NSE) and Bombay Stock Exchange (BSE) for period from 1990 to 2016. The main focus of this research paper is to examine the nature of the volatility in the Indian stock markets. In this study ARCH and GARCH models have applied to study the behaviour of stock market volatility. This study shows that GARCH (1, 1) model is more satisfactorily explains volatility clustering and its high persistence for better decision making purpose.

Keywords

Stock Index, Volatility, Heteroscedasticity, Arch, Garch.
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  • A Study on Stock Market Volatility Pattern of BSE and NSE in India

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Authors

Goutam Tanty
Rourkela Institute of Management Studies, Rourkela
P. K. Patjoshi
Centurion University, Bhubaneswar, India

Abstract


The investors, policy makers and researchers give lot of attention towards evaluation of risk while analysing the stock market. The quality of risk measures very largely depends on how well the econometric model captures behaviour of underlying asset. The paper examines the relationship between returns and volatility, volatility clustering, leverage effect and the persistence of volatility for the Indian stock markets viz. National Stock Exchange(NSE) and Bombay Stock Exchange (BSE) for period from 1990 to 2016. The main focus of this research paper is to examine the nature of the volatility in the Indian stock markets. In this study ARCH and GARCH models have applied to study the behaviour of stock market volatility. This study shows that GARCH (1, 1) model is more satisfactorily explains volatility clustering and its high persistence for better decision making purpose.

Keywords


Stock Index, Volatility, Heteroscedasticity, Arch, Garch.