Title

Nonparametric Option Pricing with Generalized Entropic Estimators

Author(s)

Caio Almeida Caio Almeida (Princeton University)

Gustavo Freire Gustavo Freire (Erasmus School of Economics)

Rafael Azevedo Rafael Azevedo (Getulio Vargas Foundation (FGV))

Kym Ardison Kym Ardison (Getulio Vargas Foundation (FGV))

Abstract

We propose a family of nonparametric estimators for an option price that require only the use of underlying return data, but can also easily incorporate information from observed option prices. Each estimator comes from a risk-neutral measure minimizing generalized entropy according to a different Cressie-Read discrepancy. We apply our method to price S&P 500 options and the cross-section of individual equity options, using distinct amounts of option data in the estimation. Estimators incorporating mild nonlinearities produce optimal pricing accuracy within the Cressie-Read family and outperform several benchmarks such as the Black-Scholes and different GARCH option pricing models. Overall, we provide a powerful option pricing technique suitable for scenarios of limited option data availability.

Creation Date

2022-05

Section URL ID

Paper Number

2022-25

URL

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2535790

File Function

Jel

C14, C58, G13

Keyword(s)

Risk-Neutral Measure, Option Pricing, Nonparametric Estimation, Generalized Entropy, Cressie-Read Discrepancies

Suppress

false

Series

13