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Extreme Risk and Value-at-Risk in the German Stock Market

Author

Listed:
  • Konstantinos Tolikas
  • Athanasios Koulakiotis
  • Richard A. Brown

Abstract

Extreme Value Theory methods are used to investigate the distribution of the extreme minima in the German stock market over the period 1973 to 2001. Innovative aspects of this paper include (i) a wide set of distributions considered, (ii) L-moment diagrams employed to identify the most appropriate distribution/s, (iii) 'probability weighted moments' used to estimate the parameters of these distribution/s and (iv) the Anderson-Darling goodness of fit test employed to test the adequacy of fit. The 'generalized logistic' distribution is found to provide adequate descriptions of the extreme minima of the German stock market over the period studied. VaR analysis results show that the EVT methods used in this study can be particularly useful for market risk measurement since they produce estimates that outperform those derived by traditional methods at high confidence levels.

Suggested Citation

  • Konstantinos Tolikas & Athanasios Koulakiotis & Richard A. Brown, 2007. "Extreme Risk and Value-at-Risk in the German Stock Market," The European Journal of Finance, Taylor & Francis Journals, vol. 13(4), pages 373-395.
    • Handle: RePEc:taf:eurjfi:v:13:y:2007:i:4:p:373-395
    DOI: 10.1080/13518470600763737
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    Cited by:

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    2. Lu, Xinjie & Zeng, Qing & Zhong, Juandan & Zhu, Bo, 2024. "International stock market volatility: A global tail risk sight," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    3. Thomas Windberger & Achim Zeileis, 2011. "Structural Breaks in Inflation Dynamics within the European Monetary Union," Working Papers 2011-12, Faculty of Economics and Statistics, Universität Innsbruck.
    4. Bertrand B. Maillet & Jean-Philippe R. M�decin, 2010. "Extreme Volatilities, Financial Crises and L-moment Estimations of Tail-indexes," Working Papers 2010_10, Department of Economics, University of Venice "Ca' Foscari".
    5. Emmanuel Jurczenko & Bertrand Maillet & Paul Merlin, 2008. "Efficient Frontier for Robust Higher-order Moment Portfolio Selection," Post-Print halshs-00336475, HAL.

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