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

Huang Dashan (), Yu Baimin (), Lu Zudi (), Frank Fabozzi (), Focardi Sergio () and Fukushima Masao ()
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Huang Dashan: Washington University in St. Louis
Yu Baimin: University of International Business and Economics
Lu Zudi: The University of Adelaide
Focardi Sergio: EDHEC Business School
Fukushima Masao: Kyoto University

Studies in Nonlinear Dynamics & Econometrics, 2010, vol. 14, issue 2, 26

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.

Date: 2010
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DOI: 10.2202/1558-3708.1805

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