Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective |
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| Institution: | 1. Department of Economics and Management, University of Trento, via Inama, 5-38122 Trento, Italy;2. Department of Decision Sciences, HEC Montreal, 3000, chemin de la Côte-Sainte-Catherine, Montréal, Québec H3T 2A7, Canada;3. IMT Institute for Advanced Studies Lucca, Italy;1. Department of Mathematics, Stockholm University, Roslagsvagen 101, SE-10691 Stockholm, Sweden;2. Department of Statistics and Operations Research, University of Vienna as well as Center for Financial Studies (CFS), Frankfurt, Germany;1. J. Mack Robinson College of Business, Georgia State University, 35 Broad Street, Suite 1234, Atlanta, GA 30303, USA;2. University of Mannheim, L9, 1-2. 68161 Mannheim, Germany;3. University of St. Gallen, Swiss Institute of Banking and Finance, Unterer Graben 21, 9000?St. Gallen, Switzerland |
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| 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. |
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