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Evidence on Hedging Effectiveness in Indian Derivatives Market


  • Barik Kumar


  • M. Supriya



The hedging effectiveness for bank futures and CNX nifty are evaluated in this study. The study is based on 9,569 observations of the daily data for these index futures. For evaluation ordinary least square, co-integrated ordinary least square, generalized auto-regressive conditional heteroscedasticity (1, 1), and constant correlation generalized auto-regressive conditional heteroscedasticity (1, 1) hedging methods are estimated and compared. Result shows that constant correlation generalized auto-regressive conditional heteroscedasticity (1, 1) is an efficient hedging method that maximizes investors’ utility function considering transaction costs. Therefore, investors can rely on this constant correlation generalized auto-regressive conditional heteroscedasticity (1, 1) hedging method. Copyright Springer Japan 2014

Suggested Citation

  • Barik Kumar & M. Supriya, 2014. "Evidence on Hedging Effectiveness in Indian Derivatives Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 21(2), pages 121-131, May.
  • Handle: RePEc:kap:apfinm:v:21:y:2014:i:2:p:121-131 DOI: 10.1007/s10690-014-9179-6

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    References listed on IDEAS

    1. Grossman, Sanford J & Shiller, Robert J, 1981. "The Determinants of the Variability of Stock Market Prices," American Economic Review, American Economic Association, vol. 71(2), pages 222-227, May.
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    3. Tae H. Park & Lorne N. Switzer, 1995. "Bivariate GARCH estimation of the optimal hedge ratios for stock index futures: A note," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(1), pages 61-67, February.
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    5. Houthakker, Hendrik S. & Williamson, Peter J., 1996. "The Economics of Financial Markets," OUP Catalogue, Oxford University Press, number 9780195044072, June.
    6. Ederington, Louis H, 1979. "The Hedging Performance of the New Futures Markets," Journal of Finance, American Finance Association, vol. 34(1), pages 157-170, March.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    9. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 39(3), pages 106-135.
    10. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    11. Geczy, Christopher & Minton, Bernadette A & Schrand, Catherine, 1997. " Why Firms Use Currency Derivatives," Journal of Finance, American Finance Association, vol. 52(4), pages 1323-1354, September.
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    More about this item


    Hedging effectiveness; Constant correlation generalized auto-regressive conditional heteroscedasticity (1; 1)hedging method; Bank futures; CNX nifty; G10; G12; G21;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages


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