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

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. Recommended Citation Dashan Huang, Baimin Yu, Zudi Lu, Frank J. Fabozzi, Sergio Focardi, and Masao Fukushima (2010) "Index-Exciting CAViaR: A New Empirical Time-Varying Risk Model", Studies in Nonlinear Dynamics & Econometrics: Vol. 14: No. 2, Article 1. http://www.bepress.com/snde/vol14/iss2/art1 Related Files huang_datacode.zip (170 kB) Data and code