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The Time-Varying Risk and Return Trade Off in Indian Stock Markets

Author

Listed:
  • Mohanty, Roshni
  • P, Srinivasan
Abstract
This paper investigates the relationship between stock market returns and volatility in the Indian stock markets using AR(1)-EGARCH(p, q)-in-Mean model. The study considers daily closing prices of two major indexes of Indian stock exchanges, viz., S&P CNX NIFTY and the BSE-SENSEX of National Stock Exchange (NSE) and Bombay Stock Exchange (BSE), respectively for the period from July 1, 1997 to December 31, 2013. The empirical results show positive but insignificant relationship between stock returns and conditional variance in the case of NSE Nifty and BSE SENSEX stock markets. Besides, the analysis reveals that volatility is persistent and there exists leverage effect supporting the work of Nelson (1991) in the Indian stock markets. The present study suggests that the capital market regulators, investors and market participants should employ the asymmetric GARCH-type model that sufficiently captures the stylized characteristics of the return, such as time varying volatility, high persistence and asymmetric volatility responses, in determining the hedging strategy and portfolio management and estimating and forecasting volatility for risk management decision making at Indian Stock Exchange.

Suggested Citation

  • Mohanty, Roshni & P, Srinivasan, 2014. "The Time-Varying Risk and Return Trade Off in Indian Stock Markets," MPRA Paper 55660, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:55660
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    References listed on IDEAS

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    More about this item

    Keywords

    Stock Market Returns; Weak-From Efficiency; India; AR-EGARCH-M model;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G1 - Financial Economics - - General Financial Markets
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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