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.
CAViaR, Index-exciting CAViaR, Quantile regression, Time-varying model, VaR
Finance and Financial Management | Management Sciences and Quantitative Methods
Studies in Nonlinear Dynamics and Econometrics
Dashan HUANG; YU, Baimin; LU, Zudi; FOCARDI, Sergio; FABOZZI, Frank; and FUKUSHIMA, Masao.
Index-Exciting CAViaR: A New Empirical Time-Varying Risk Model. (2010). Studies in Nonlinear Dynamics and Econometrics. 14, (2), 1-24. Research Collection Lee Kong Chian School Of Business.
Available at: http://ink.library.smu.edu.sg/lkcsb_research/4781
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