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Modeling Multivariate Extreme Events Using Self-Exciting Point Processes

  • Oliver Grothe

    (Department of Economic and Social Statistics, University of Cologne)

  • Volodymyr Korniichuk

    (CGS, University of Cologne)

  • Hans Manner

    (Department of Economic and Social Statistics, University of Cologne)

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    We propose a new model that can capture the typical features of multivariate extreme events observed in financial time series, namely clustering behavior in magnitudes and arrival times of multivariate extreme events, and time-varying dependence. The model is developed in the framework of the peaks-over-threshold approach in extreme value theory and relies on a Poisson process with self-exciting intensity. We discuss the properties of the model, treat its estimation, deal with testing goodness-of-fit, and develop a simulation algorithm. The model is applied to return data of two stock markets and four major European banks.

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    Paper provided by Cologne Graduate School in Management, Economics and Social Sciences in its series Cologne Graduate School Working Paper Series with number 03-06.

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    Date of creation: 27 Jun 2012
    Date of revision: 20 Jun 2013
    Handle: RePEc:cgr:cgsser:03-06
    Contact details of provider: Postal: 0221 / 470 5607
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    Web page: http://www.cgs.uni-koeln.de/
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    1. Yacine Aït-Sahalia & Julio Cacho-Diaz & Roger J.A. Laeven, 2010. "Modeling Financial Contagion Using Mutually Exciting Jump Processes," NBER Working Papers 15850, National Bureau of Economic Research, Inc.
    2. Clive Bowsher, 2002. "Modelling Security Market Events in Continuous Time: Intensity based, Multivariate Point Process Models," Economics Papers 2002-W22, Economics Group, Nuffield College, University of Oxford.
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