Realized peaks over threshold: A time-varying extreme value approach with high-frequency-based measures

Luca Trapin, Marco Bee, Debbie J. Dupuis

Risultato della ricerca: Contributo in rivistaArticolo in rivista

2 Citazioni (Scopus)

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-of-sample forecasts of conditional risk measures obtained with HF measures outperform those obtained with LF measures.
Lingua originaleEnglish
pagine (da-a)254-283
Numero di pagine30
RivistaJournal of Financial Econometrics
Volume17
DOI
Stato di pubblicazionePubblicato - 2019

Keywords

  • Conditional risk measures
  • Forecasting
  • Peaks-over-threshold
  • Realized volatility
  • Tail risk

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