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On the Daily Returns & Conditional Volatility of S&P CNX NSE Nifty: Impact of Recent Global Recession


Affiliations
1 Department of Commerce, Rabindra Mahavidyalaya, Champadanga, Hooghly, West Bengal, India
     

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The present study has tried to explore a few stylized facts regarding the volatility of daily returns of S&P CNX NSE NIFTY. Highly significant JB statistic confirms that the return series is not normally distributed. Moreover, clear evidence of volatility clustering could be observed during the study period. Furthermore EGARCH (1, 1) model has been used to compute conditional variance of the NIFTY daily returnsof the sample period. The empirical results confirm that above model is a good fit and it clearly indicates that volatility in NSE persists over a long period. The empirical results establish that news asymmetry and Leverage Effect are present in this market. Finally, it has been clearly established that the recent sub-prime crisis has significant effect on the daily returns and volatility of S&P CNX NSE NIFTY.

Keywords

NIFTY, EGARCH (1,1), Sub-Prime Crisis.
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  • Banerjee, A., & Sarkar, S. (2006). Modeling daily volatility of the Indian stock market using intraday data, Working Paper No. 588. [Online] Retrieved from http://www.iimcal. ac.in/res/upd%5CWPS%20588.pdf.
  • Batra, A. (2004). Stock return volatility patterns in India, Working Paper no. 124 [Online] Rerievedfrom http:// www.icrier.org/pdf/wp124.pdf.
  • Black, F. (1976). Studies of stock price volatility changes. Proceedings of the 1976 meetings of the American Statistical Association, Business and Economics Statistics Section. Washington, DC: American Statistical Association, 177-181.
  • Bollerslev, T., Chou, R. Y., & Kroner, K. F. (1992). ARCH modeling in finance. Journal of Econometrics, 52(1/2), 5-59.
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307-327.
  • Brailsford, T. J., & Faff, R. W. (1996). An evaluation of volatility forecasting techniques. Journal of Banking and Finance, 20, 419-438.
  • Brooks, C. (1998). Predicting stock market volatility: Can market volume help? Journal of Forecasting, 17, 59-80.
  • Fama, E. (1965). The behaviour of stock market prices. Journal of Business, 38(1), 34-105.
  • Goudarzi, H., & Ramanaraynan, C. S. (2011). Modeling asymmetric volatility in the Indian stock market. International Journal of Business and Management, 6(3), 221-231.
  • Goyal, R. (1995).Volatility in stock market returns.Reserve Bank of India Occasional Papers, 16(3), 175-195.
  • Joshi, P., & Pandya, K. (2008). Exploring movements of stock market volatility in India. TheIcfai Journal of Applied Finance, 14(3), 5-32.
  • Karmakar, M. (2005). Modeling conditional volatility of the Indian stock markets. Vikalpa, 30(3), 21-37.
  • Kaur, H. (2004). Time varying volatility in the Indian stock market. Vikalpa, 29(4), 25-42.
  • Kim, D., & Kon, S. (1994). Alternative models for the conditional heteroscedasticity of stock returns. Journal of Business 67, 563-598.
  • Kumar, K., & Mukhopadhyay, C. (2002). A case of US and India. Paper published as part of the NSE Research Initiative, Retrieved from www.nseindia.com.
  • MacKinnon, J. G. (1996). Numerical distribution functions for unit ischolar_main and cointegration tests. Journal of Applied Econometrics, 11, 601-618.
  • Mandelbrot, B. (1963). The variation of certain speculative prices. Journal of Business, 36, 394-419.
  • Nelson, D. B. (1991).Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59, 347-370.
  • Pattanaik, S., & Chatterjee, B. (2000). Stock returns and volatility in India: An empirical puzzle? Reserve Bank of India Occasional Papers, 21(1), Summer, 37-60.
  • Phillips, P. C. B., & Perron, P. (1988). Testing for a unit ischolar_main in time series regression. Biometrika, 75, 335-346.
  • Reddy, Y. S. (1997-98). Effects of microstructure on stock market liquidity and volatility. Prajnan, 26(2), 217-231.
  • Roy, M. K., & Karmakar, M. (1995). Stock market volatility: Roots and results. Vikalpa, 20(1), 37-48.
  • Schwert, W. G. (1989). Why does stock market volatility change over time?. Journal of Finance, 44(5), 1115-1151.
  • Sen, S. S (2010). On the volatility of S & P CNX NIFTY. Indian Journal of Finance, 4(5), 53-57.
  • Sen, S. S., & Bandyopadhyay, T. (2012). Characteristics of conditional volatility of BSE SENSEX: The impact of information asymmetry and leverage. South Asian Journal of Management, 19(4), 27-44

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  • On the Daily Returns & Conditional Volatility of S&P CNX NSE Nifty: Impact of Recent Global Recession

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Authors

Som Sankar Sen
Department of Commerce, Rabindra Mahavidyalaya, Champadanga, Hooghly, West Bengal, India

Abstract


The present study has tried to explore a few stylized facts regarding the volatility of daily returns of S&P CNX NSE NIFTY. Highly significant JB statistic confirms that the return series is not normally distributed. Moreover, clear evidence of volatility clustering could be observed during the study period. Furthermore EGARCH (1, 1) model has been used to compute conditional variance of the NIFTY daily returnsof the sample period. The empirical results confirm that above model is a good fit and it clearly indicates that volatility in NSE persists over a long period. The empirical results establish that news asymmetry and Leverage Effect are present in this market. Finally, it has been clearly established that the recent sub-prime crisis has significant effect on the daily returns and volatility of S&P CNX NSE NIFTY.

Keywords


NIFTY, EGARCH (1,1), Sub-Prime Crisis.

References