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Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective

Marco Bee, Debbie J. Dupuis and Luca Trapin ()

Journal of Empirical Finance, 2016, vol. 36, issue C, 86-99

Abstract: This article applies realized volatility forecasting to Extreme Value Theory (EVT). We propose a two-step approach where returns are first pre-whitened with a high-frequency based volatility model, and then an EVT based model is fitted to the tails of the standardized residuals. This realized EVT approach is compared to the conditional EVT of McNeil & Frey (2000). We assess both approaches' ability to filter the dependence in the extremes and to produce stable out-of-sample VaR and ES estimates for one-day and ten-day time horizons. The main finding is that GARCH-type models perform well in filtering the dependence, while the realized EVT approach seems preferable in forecasting, especially at longer time horizons.

Keywords: Realized volatility; High-frequency data; Extreme Value Theory; Value-at-Risk; Expected Shortfall (search for similar items in EconPapers)
JEL-codes: C4 C5 G1 (search for similar items in EconPapers)
Date: 2016
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Journal of Empirical Finance is currently edited by R. T. Baillie, F. C. Palm, Th. J. Vermaelen and C. C. P. Wolff

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