Publication Type

Journal Article

Version

Publisher’s Version

Publication Date

3-2010

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.

Keywords

CAViaR, Index-exciting CAViaR, Quantile regression, Time-varying model, VaR

Discipline

Finance and Financial Management | Management Sciences and Quantitative Methods

Research Areas

Finance

Publication

Studies in Nonlinear Dynamics and Econometrics

Volume

14

Issue

2

First Page

1

Last Page

24

ISSN

1558-3708

Identifier

10.2202/1558-3708.1805

Publisher

De Gruyter

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.2202/1558-3708.1805

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