Extracting Tail Risk from High-Frequency S&P 500 Returns

Caio Almeida, Kym Ardison, René Garcia and Piotr OrÅ‚owski
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Caio Almeida: Princeton University
Kym Ardison: FGV EPGE, Rio de Janeiro, Brazil
Piotr Orłowski: HEC Montréal

Working Papers from Princeton University. Economics Department.

Abstract: This paper proposes to extract tail risk from a risk-neutral mean-adjusted expected shortfall of high-frequency stock returns. Risk adjustment is based on a nonparametric estimator of the state price density that does not use option prices and relies solely on a stock index returns. This makes the measure methodology applicable to many financial markets with illiquid or nonexistent options. Empirically, the tail risk factor extracted from S\&P 500 returns has a 90% correlation with the options-based VIX index and predicts well realized jumps in the stock market index at various frequencies. We document a persistent negative relation between tail risk and one-day ahead returns of several assets classes. Consistent with the crash-insurance property of put options, tail risk predicts positive one-day ahead returns for portfolios long out-of-the-money, short in-the-money put options. An analysis of equity portfolios sorted on exposure to tail risk reveals a premium for bearing such a risk, even after controlling for known and established factors related to cross-sectional variability. This cross-sectional analysis is robust to the inclusion of uncertainty indexes, as well as macroeconomic and volatility measures.

Keywords: Tail Risk; Risk-Neutral Measure; Expected Shortfall; Intra-day Market Returns (search for similar items in EconPapers)
JEL-codes: G12 G13 G17 (search for similar items in EconPapers)
Date: 2020-01
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Persistent link: https://EconPapers.repec.org/RePEc:pri:econom:2020-78

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