Efficient Estimation of Pricing Kernels and Market-Implied Densities

37 Pages Posted: 26 May 2021 Last revised: 27 May 2021

See all articles by Jeroen Dalderop

Jeroen Dalderop

University of Notre Dame - Department of Economics

Date Written: May 25, 2021

Abstract

This paper studies the nonparametric identification and estimation of projected pricing kernels implicit in European option prices and underlying asset returns using conditional moment restrictions. The proposed series estimator avoids computing ratios of estimated risk-neutral and physical densities. Instead, we consider efficient estimation based on the conditional Euclidean empirical likelihood or continuously-updated GMM criterion, which takes into account the informativeness of option prices of varying strike prices beyond observed conditioning variables. In a second step, we convert the implied probabilities into predictive densities by matching the informative part of cross-sections of option prices. Empirically, pricing kernels tend to be U-shaped in the S&P 500 index return given high levels of the VIX, and call and ATM options are more informative about their payoff than put and OTM options.

Keywords: Option Prices, Risk Aversion, Density Forecasting, Empirical Likelihood

JEL Classification: C14, G13

Suggested Citation

Dalderop, Jeroen, Efficient Estimation of Pricing Kernels and Market-Implied Densities (May 25, 2021). Available at SSRN: https://ssrn.com/abstract=3853347 or http://dx.doi.org/10.2139/ssrn.3853347

Jeroen Dalderop (Contact Author)

University of Notre Dame - Department of Economics ( email )

Notre Dame, IN 46556
United States

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