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The magnitude of a market crash can be predicted

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  • Novak, S.Y.
  • Beirlant, J.

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  • Novak, S.Y. & Beirlant, J., 2006. "The magnitude of a market crash can be predicted," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 453-462, February.
    • Handle: RePEc:eee:jbfina:v:30:y:2006:i:2:p:453-462
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    References listed on IDEAS

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    1. Danielsson, J. & de Haan, L. & Peng, L. & de Vries, C. G., 2001. "Using a Bootstrap Method to Choose the Sample Fraction in Tail Index Estimation," Journal of Multivariate Analysis, Elsevier, vol. 76(2), pages 226-248, February.
    2. Ferreira, A., 2002. "Statistics of extremes : Estimation and optimality," Other publications TiSEM d20ada07-4fb3-4e89-aa2e-f, Tilburg University, School of Economics and Management.
    3. Matthys, Gunther & Delafosse, Emmanuel & Guillou, Armelle & Beirlant, Jan, 2004. "Estimating catastrophic quantile levels for heavy-tailed distributions," Insurance: Mathematics and Economics, Elsevier, vol. 34(3), pages 517-537, June.
    4. Basrak, Bojan & Davis, Richard A. & Mikosch, Thomas, 2002. "Regular variation of GARCH processes," Stochastic Processes and their Applications, Elsevier, vol. 99(1), pages 95-115, May.
    5. Evis Këllezi & Manfred Gilli, 2000. "Extreme Value Theory for Tail-Related Risk Measures," FAME Research Paper Series rp18, International Center for Financial Asset Management and Engineering.
    6. Enrico Capobianco, 2002. "Multiresolution approximation for volatility processes," Quantitative Finance, Taylor & Francis Journals, vol. 2(2), pages 91-110.
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    Cited by:

    1. Schluter, Christian & Trede, Mark, 2008. "Identifying multiple outliers in heavy-tailed distributions with an application to market crashes," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 700-713, September.
    2. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    3. Brée, David S. & Joseph, Nathan Lael, 2013. "Testing for financial crashes using the Log Periodic Power Law model," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 287-297.

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