Characteristic Portfolios, Conditional Quantile Curves, and the Cross-Section of Option Returns

Abstract

Portfolio sorts and cross-sectional regressions are standard tools to test the pricing of asset characteristics. We propose the alternative use of non-parametric machine learning methods to estimate quantile curves of the characteristic of interest conditional on a set of controls. Building portfolios based on conditional quantile curves yields characteristic portfolios that should only reflect the priced risk associated with the characteristic and does not require any assumption on the functional form of the characteristic-return relation. We apply our procedure to the pricing of volatility risk in the cross-section of option returns. The Sharpe ratio of the resultant characteristic portfolios are up to 30% higher than those of comparable strategies.

Keywords: Option returns, implied volatility, machine learning, realized volatility, Volatility Risk Premium, volatility mispricing.

JEL Classification: G11,G13,C14,C58,C45