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Modelling the distribution of the extreme share returns in Singapore

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  • Tolikas, Konstantinos
  • Gettinby, Gareth D.
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    Abstract

    This study aims to model the probability distribution of the extreme daily share returns in Singapore Stock Exchange over the period 1973 to 2005. For that reason the suitability of the Generalized Extreme Value (GEV), Generalized Pareto (GP) and Generalized Logistic (GL) distributions are investigated. The empirical results indicate that the GL distribution best fitted the empirical data over the period of study. Using the too much celebrated GEV and GP distributions for risk assessment could, therefore, lead to underestimation of the extreme risk which could potentially lead to inadequate protection against catastrophic losses.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Empirical Finance.

    Volume (Year): 16 (2009)
    Issue (Month): 2 (March)
    Pages: 254-263

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    Handle: RePEc:eee:empfin:v:16:y:2009:i:2:p:254-263

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    Web page: http://www.elsevier.com/locate/jempfin

    Related research

    Keywords: Extreme share returns Risk measurement Generalized Logistic distribution Generalized Extreme Value distribution L-moments Probability Weighted Moments;

    References

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    1. Longin, Francois M, 1996. "The Asymptotic Distribution of Extreme Stock Market Returns," The Journal of Business, University of Chicago Press, vol. 69(3), pages 383-408, July.
    2. G. D. Gettinby & C. D. Sinclair & D. M. Power & R. A. Brown, 2006. "An analysis of the distribution of extremes in indices of share returns in the US, UK and Japan from 1963 to 2000," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 11(2), pages 97-113.
    3. John Cotter & Kevin Dowd, 2011. "Extreme Spectral Risk Measures: An Application to Futures Clearinghouse Margin Requirements," Papers 1103.5653, arXiv.org.
    4. G. D. Gettinby & C. D. Sinclair & D. M. Power & R. A. Brown, 2004. "An Analysis of the Distribution of Extreme Share Returns in the UK from 1975 to 2000," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(5-6), pages 607-646.
    5. Gencay, Ramazan & Selcuk, Faruk, 2004. "Extreme value theory and Value-at-Risk: Relative performance in emerging markets," International Journal of Forecasting, Elsevier, vol. 20(2), pages 287-303.
    6. Turan G. Bali, 2003. "An Extreme Value Approach to Estimating Volatility and Value at Risk," The Journal of Business, University of Chicago Press, vol. 76(1), pages 83-108, January.
    7. Pownall, Rachel A. J. & Koedijk, Kees G., 1999. "Capturing downside risk in financial markets: the case of the Asian Crisis," Journal of International Money and Finance, Elsevier, vol. 18(6), pages 853-870, December.
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    Cited by:
    1. Tolikas, Konstantinos, 2014. "Unexpected tails in risk measurement: Some international evidence," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 476-493.
    2. Konstantinos Tolikas, 2011. "The rare event risk in African emerging stock markets," Managerial Finance, Emerald Group Publishing, vol. 37(3), pages 275-294, March.
    3. Bertrand B. Maillet & Jean-Philippe R. M├ędecin, 2010. "Extreme Volatilities, Financial Crises and L-moment Estimations of Tail-indexes," Working Papers 2010_10, Department of Economics, University of Venice "Ca' Foscari".
    4. Kerstens, Kristiaan & Mounir, Amine & de Woestyne, Ignace Van, 2011. "Non-parametric frontier estimates of mutual fund performance using C- and L-moments: Some specification tests," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1190-1201, May.

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