Application Of Conditional Heteroscadicity Model On Sectoral Index In India With Special Reference To Banking Sector Using Garch Model
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Abstract
The objective of this paper is to investigate the impact of volatility in stock prices of banking sector. The sample data consist of closing prices of Bank Nifty from January 1, 2017 to December 31, 2019. The study uses EGARCH model tocapture volatility clustering, persistence and leverage effect.The result shows ARCH Effect (C3) is positive i.e. there is a positive relation between the past variance and the current variance in absolute value, Leverage effect (C4) is negative indicates an asymmetric effect i.e. Bad news will increase volatility more than a good new of the same size and Significant and positive value of GARCH Term (C5) indicates present volatility or conditional variance is significantly affected by previous period conditional variance
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References
1. Krishna Murari,2013,Volatility Modelling and Forecasting for Banking Stock Returns
2. Gurmeet Singh, 2015, Volatility Modelling and Forecasting for NIFTY Stock Returns
3. William Coffie, 2015, Modelling and Forecasting the Conditional Heteroscedasticity of Stock Returns Using Asymmetric Models: Empirical Evidence from Ghana and Nigeria
4. Ramona Birăua, Jatin Trivedib, Mihail Antonesc, 2015,Modelling S&P Bombay Stock Exchange BANKEX Index Volatility Patterns Using GARCH Model
5. Dr. Prema Chandran, 2016,AStudy on the Volatility and Returns of the Indian Banking Sector Index with Reference to NSE Nifty
6. Omorogbe J. Asemota1 and Ucheoma C. Ekejiuba ,2017, An Application of Asymmetric GARCH Models on Volatility of Banks Equity in Nigeria's Stock Market
7. Amanjot singh,2017,modelling conditional volatility of Indian banking sector's stock market returns
8. Dr. Shveta Singh, Teena,2019,Modelling the volatility of banking sectors of national stock exchange
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