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Extreme Risk and Fat-tails Distribution Model:Empirical Analysis

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  • Ibrahim Onour

Abstract

This paper investigates estimation of extreme risk in a number of stock markets in the Gulf Cooperation Council (GCC) countries , Saudi, Kuwait, and United Arab Emirates, in addition to S& P 500 stock index, using the Generalized Pareto Distribution (GPD) model. The estimated tails parameter values for stock returns of Kuwait, Saudi, and Dubai, markets show the likelihood of significant extreme losses as well as significant extreme gains, compared to the case of more mature S&P 500 stock returns, which exhibit possibility of significant extreme losses with insignificant gain prospects.
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  • Ibrahim Onour, "undated". "Extreme Risk and Fat-tails Distribution Model:Empirical Analysis," API-Working Paper Series 0911, Arab Planning Institute - Kuwait, Information Center.
    • Handle: RePEc:api:apiwps:0911
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    References listed on IDEAS

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    1. Olivier V. Pictet & Michel M. Dacorogna & Ulrich A. Muller, 1996. "Heavy tails in high-frequency financial data," Working Papers 1996-12-11, Olsen and Associates.
    2. Pierre Giot & Sébastien Laurent, 2003. "Value-at-risk for long and short trading positions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(6), pages 641-663.
    3. Olivier V. Pictet & Michel M. Dacorogna & Ulrich A. Muller, 1996. "Hill, Bootstrap and Jackknife Estimators for Heavy Tails," Working Papers 1996-12-10, Olsen and Associates.
    4. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    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. McNeil, Alexander J., 1997. "Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory," ASTIN Bulletin, Cambridge University Press, vol. 27(1), pages 117-137, May.
    7. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
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    Cited by:

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    2. Singh, Abhay K. & Allen, David E. & Robert, Powell J., 2013. "Extreme market risk and extreme value theory," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 310-328.
    3. Cerović Julija & Lipovina-Božović Milena & Vujošević Saša, 2015. "A Comparative Analysis of Value at Risk Measurement on Emerging Stock Markets: Case of Montenegro," Business Systems Research, Sciendo, vol. 6(1), pages 36-55, March.

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    More about this item

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • E00 - Macroeconomics and Monetary Economics - - General - - - General
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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