Nonparametric Option Implied Tail Risk and Market Returns

51 Pages Posted: 19 Sep 2018

See all articles by Conall O'Sullivan

Conall O'Sullivan

University College Dublin (UCD) - Michael Smurfit Graduate School of Business

Yan Wang

University College Dublin (UCD) - Michael Smurfit Graduate School of Business

Date Written: September 11, 2018

Abstract

We propose a non-parametric method based on a model-free formula to evaluate the tails of a risk-neutral distribution using the full cross-section of option prices at a fixed horizon. The method leads to the joint estimation of risk-neutral tail probabilities and tail expectations beyond the minimum and maximum strike prices. We confirm the accuracy of the risk-neutral tail measures using simulated data. We extract time series of left and right option implied tail risk measures from S&P 500 index options. We find the ratio of risk-neutral left tail conditional expectation to a physical measure of tail risk significantly predicts the equity risk premium at longer return horizons of six months to twelve months with a significant improvement in ex- planatory power when compared to using the physical tail risk measure alone. We also find that both the risk-neutral left and right tail conditional expectations significantly predict the one-month ahead variance risk premium.

Suggested Citation

O'Sullivan, Conall and Wang, Yan, Nonparametric Option Implied Tail Risk and Market Returns (September 11, 2018). Michael J. Brennan Irish Finance Working Paper Series Research Paper No. 18-8, Available at SSRN: https://ssrn.com/abstract=3251803 or http://dx.doi.org/10.2139/ssrn.3251803

Conall O'Sullivan (Contact Author)

University College Dublin (UCD) - Michael Smurfit Graduate School of Business ( email )

Blackrock, Co. Dublin
Ireland
+353 1 716 8836 (Phone)
+353 1 283 5482 (Fax)

Yan Wang

University College Dublin (UCD) - Michael Smurfit Graduate School of Business ( email )

Blackrock, Co. Dublin
Ireland

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