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How deep was the September 2001 stock market crisis?: putting recent events on the American and French markets into perspective with an index of market shocks

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  • Maillet, Bertrand
  • Michel, Thierry

Abstract

Markets reacted strongly to the World Trade Center attacks both in Europe and in the United States. The extent of this crisis was difficult to assess at the time, underlining the need for a specific tool to measure the magnitude of financial crises. A first measure was recently proposed and applied to the foreign exchange market by Zumbach et al (2000-a and 2000-b). Their measure relies on an analogy with geophysics; the related Index of Market Shocks (IMS) that we propose here is also the counterpart of the Richter scale used for earthquakes. We apply this measure on the French and the American stock markets to put recent market events into perspective. The crisis triggered by the September attacks was actually the worst since 1987, and the 9th when compared to major historical ones.

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  • Maillet, Bertrand & Michel, Thierry, 2002. "How deep was the September 2001 stock market crisis?: putting recent events on the American and French markets into perspective with an index of market shocks," LSE Research Online Documents on Economics 24936, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:24936
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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. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001. "The distribution of realized stock return volatility," Journal of Financial Economics, Elsevier, vol. 61(1), pages 43-76, July.
    3. Jansen, Dennis W & de Vries, Casper G, 1991. "On the Frequency of Large Stock Returns: Putting Booms and Busts into Perspective," The Review of Economics and Statistics, MIT Press, vol. 73(1), pages 18-24, February.
    4. repec:bla:jfinan:v:53:y:1998:i:1:p:219-265 is not listed on IDEAS
    5. Gerhard, Frank & Hautsch, Nikolaus, 2002. "Volatility estimation on the basis of price intensities," Journal of Empirical Finance, Elsevier, vol. 9(1), pages 57-89, January.
    6. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    7. Dacorogna, Michael M. & Muller, Ulrich A. & Nagler, Robert J. & Olsen, Richard B. & Pictet, Olivier V., 1993. "A geographical model for the daily and weekly seasonal volatility in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 12(4), pages 413-438, August.
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    Cited by:

    1. Thierry Chauveau & Sylvain Friederich & Jérôme Héricourt & Emmanuel Jurczenko & Catherine Lubochinsky & Bertrand Maillet & Christophe Moussu & Bogdan Négréa & Hélène Raymond-Feingold, 2004. "La volatilité des marchés augmente-t-elle ?," Revue d'Économie Financière, Programme National Persée, vol. 74(1), pages 17-44.

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    More about this item

    Keywords

    financial crises; volatility; risk measurement; heterogeneity of economic agents;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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