Two-step methods in VaR prediction and the importance of fat tails
DOI: 10.1080/14697688.2014.942230
Title: Two-step methods in VaR prediction and the importance of fat tails
Journal Title: Quantitative Finance
Volume: pages 1-18
Issue: pages 1-18
Publication Date: pages 1-18
Start Page: pages1-18
End Page: pages1-18
ISSN: 1469-7688
Author: Ibrahim Ergena*
Affiliations:
a Supervision Regulation and Credit, Risk and Policy Analysis, Federal Reserve Bank of Richmond, 502 S. Sharp Street, Baltimore, MD 21201, USA.
Abstract: This paper proposes a Two-step methodology for Value-at-Risk prediction. The first step involves estimation of a GARCH model using quasi-maximum likelihood estimation and the second step uses model filtered returns with the skewed t distribution of Azzalini and Capitanio [J. R. Stat. Soc. B, 2003, 65, 367–389]. The predictive performance of this method is compared to the single-step joint estimation of the same data generating process, to the well-known GARCH-Evt model and to a comprehensive set of other market risk models. Backtesting results show that the proposed Two-step method outperforms most benchmarks including the classical joint estimation method of same data generating process and it performs competitively with respect to the GARCH-Evt model. This paper recommends two robust models to risk managers of emerging market stock portfolios. Both models are estimated in two steps: the GJR-GARCH-Evt model and the Two-step GARCH-St model proposed in this study.
Accepted: 6 Jun 2014

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