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

• We use Extreme Value Theory and high-frequency data to model the asset returns' tails.
• We pre-whiten the returns and then model the tails of the standardized residuals.
• We compare our approach to the conditional EVT approach along two dimensions.
• As of pre-whitening the returns, the conditional EVT performs slightly better.
• From a risk management perspective, our approach seems to be preferable.

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.