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

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.

Author(s):

Frank J. Fabozzi

Yale School of Management

Sergio Focardi

EDHEC Business School

Masao Fukushima

Kyoto University

Dashan Huang

Washington University in St. Louis

Zudi Lu

School of Mathematical Sciences,The University of Adelaide

Baimin Yu

School of International Tradeand Economics, University of International Business and Economics

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 timevarying 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.

Type: Working paper
Date: le 10/09/2008
Research Cluster : Finance

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