Index-Exciting CAViaR: A New Empirical Time-Varying Risk Model (original) (raw)

Instead of assuming the distribution of return series, 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 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. * We thank the editor (Bruce Mizrach) and the three anonymous referees for constructive suggestions that guided us in improving the paper. We are also grateful to Simone Manganelli for providing his CAViaR codes.