High-Frequency Tail Risk Premium and Stock Return Predictability

Caio Almeida, Kym Ardison, Gustavo Freire, René Garcia, Piotr Orłowski

Research output: Contribution to journalArticlepeer-review

Abstract

We propose a novel measure of the market return tail risk premium based on minimum-distance state price densities recovered from high-frequency data. The tail risk premium extracted from intra-day S&P 500 returns predicts the market equity and variance risk premiums and expected excess returns on a cross section of characteristicssorted portfolios. Additionally, we describe the differential role of the quantity of tail risk, and of the tail premium, in shaping the future distribution of index returns. Our results are robust to controlling for established measures of variance and tail risk, and of risk premiums, in the predictive models.

Original languageEnglish (US)
JournalJournal of Financial and Quantitative Analysis
DOIs
StateAccepted/In press - 2023

All Science Journal Classification (ASJC) codes

  • Accounting
  • Finance
  • Economics and Econometrics

Keywords

  • Expected Shortfall
  • Intra-day Market Returns
  • Return Predictability
  • Risk-Neutral Measure
  • Tail Risk

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