Publication Type
Journal Article
Version
publishedVersion
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
Citation
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.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/4781
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.2202/1558-3708.1805
Included in
Finance and Financial Management Commons, Management Sciences and Quantitative Methods Commons