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Index-Exciting CAViaR: A New Empirical Time-Varying Risk Model

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Author Info

  • Huang Dashan

    ()
    (Washington University in St. Louis)

  • Yu Baimin

    ()
    (University of International Business and Economics)

  • Lu Zudi

    ()
    (The University of Adelaide)

  • Fabozzi Frank J.

    ()
    (Yale School of Management)

  • Focardi Sergio

    ()
    (EDHEC Business School)

  • Fukushima Masao

    ()
    (Kyoto University)

Abstract

Instead of assuming the distribution of return series, Engle and Manganelli (2004) propose a new Value-at-Risk (VaR) modeling approach, Conditional Autoregressive Value-at-Risk (CAViaR), to directly compute the quantile of an individual asset's returns which performs better in many cases than those that invert a return distribution. In this paper we explore more flexible CAViaR models that allow VaR prediction to depend upon a richer information set involving returns on an index. Specifically, we formulate a time-varying CAViaR model whose parameters vary according to the evolution of the index. The empirical evidence reported in this paper suggests that our time-varying CAViaR models can do a better job for VaR prediction when there are spillover effects from one market or market segment to other markets or market segments.

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Bibliographic Info

Article provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 14 (2010)
Issue (Month): 2 (March)
Pages: 1-26

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Handle: RePEc:bpj:sndecm:v:14:y:2010:i:2:n:1

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Web page: http://www.degruyter.com

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Cited by:
  1. Matteo Grigoletto & Francesco Lisi, 2011. "Practical implications of higher moments in risk management," Statistical Methods and Applications, Springer, vol. 20(4), pages 487-506, November.

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