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

Luca Trapin, Marco Bee, Debbie J. Dupuis

Risultato della ricerca: Contributo in rivistaArticolo in rivista

13 Citazioni (Scopus)

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.
Lingua originaleEnglish
pagine (da-a)86-99
Numero di pagine14
RivistaJournal of Empirical Finance
Volume36
DOI
Stato di pubblicazionePubblicato - 2016

Keywords

  • Economics and Econometrics
  • Expected Shortfall
  • Extreme Value Theory
  • Finance
  • High-frequency data
  • Realized volatility
  • Value-at-Risk

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