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.
Yale School of Management
EDHEC Business School
Washington University in St. Louis
School of Mathematical Sciences,The University of Adelaide
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.
|Research Cluster :||Finance|