Financial Crisis, VaR Forecasts and the Performance of Time Varying EVT-Copulas

Theo Berger ()
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Theo Berger: University of Bremen

A chapter in Operations Research Proceedings 2012, 2014, pp 35-40 from Springer

Abstract: Abstract We investigate Value-at-Risk (VaR) estimates based on extreme value theory (EVT) models combined with time varying parametric copulas against competing parametric approaches accounting for dynamic conditional correlations feasible to higher order portfolios. Tails of the return distributions are modeled via Generalized Pareto Distribution (GPD) applied to GARCH filtered residuals to capture excess returns, linked via constant and time varying copulas. Drawing on this EVT-GARCH-Copula, we evaluate portfolios consisting of German Stocks, market indices and FX-rates. However, the empirical results support the dynamic EVT-GARCH-Copula approach, as 99 % VaR forecasts clearly outperform parametric estimates stemming from competing dependency approaches.

Keywords: Likelihood Ratio Statistic; Generalize Pareto Distribution; Tail Dependency; Conditional Correlation; Gaussian Copula (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-319-00795-3_6

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DOI: 10.1007/978-3-319-00795-3_6

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