Title

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

Author(s)

Caio Almeida Caio Almeida (Princeton University)

Kym Ardison Kym Ardison (FGV EPGE, Rio de Janeiro, Brazil)

René Garcia René Garcia (Université de Montréal)

Piotr Orłowski Piotr Orłowski (HEC Montréal)

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.

Creation Date

2020-01

Section URL ID

Paper Number

2020-78

URL

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3211954

File Function

Jel

G12, G13, G17

Keyword(s)

Tail Risk, Risk-Neutral Measure, Expected Shortfall, Intra-day Market Returns

Suppress

false

Series

13