Realized Peaks over Threshold: A Time-Varying Extreme Value Approach with High-Frequency-Based Measures*

Abstract

Recent contributions to the financial econometrics literature exploit high-frequency (HF) data to improve models for daily asset returns.This paper proposes a new class of dynamic extreme value models that profit from HF data when estimating the tails of daily asset returns.Our realized peaks-over-threshold approach provides estimates for the tails of the time-varying conditional return distribution.An in-sample fit to the S&P 500 index returns suggests that HF data convey information on daily extreme returns beyond that included in low frequency (LF) data.Finally, out-ofsample forecasts of conditional risk measures obtained with HF measures outperform those obtained with LF measures.

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