Can a Machine Correct Option Pricing Models?

35 Pages Posted: 4 May 2021 Last revised: 28 Dec 2022

See all articles by Caio Almeida

Caio Almeida

Princeton University - Bendheim Center for Finance

Jianqing Fan

Princeton University - Bendheim Center for Finance

Gustavo Freire

Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE); Tinbergen Institute; Erasmus Research Institute of Management (ERIM)

Francesca Tang

Princeton University - Department of Political Science

Date Written: July 5, 2022

Abstract

We introduce a novel two-step approach to predict implied volatility surfaces. Given any fitted parametric option pricing model, we train a feedforward neural network on the model-implied pricing errors to correct for mispricing and boost performance. Using a large dataset of S&P 500 options, we test our nonparametric correction on several parametric models ranging from ad-hoc Black-Scholes to structural stochastic volatility models and demonstrate the boosted performance for each model. Out-of-sample prediction exercises in the cross-section and in the option panel show that machine-corrected models always outperform their respective original ones, often by a large extent. Our method is relatively indiscriminate, bringing pricing errors down to a similar magnitude regardless of the misspecification of the original parametric model. Even so, correcting models that are less misspecified usually leads to additional improvements in performance and also outperforms a neural network fitted directly to the implied volatility surface.

Keywords: Deep Learning, Boosting, Implied Volatility, Stochastic Volatility, Model Correction

Suggested Citation

Almeida, Caio and Fan, Jianqing and Freire, Gustavo and Tang, Francesca, Can a Machine Correct Option Pricing Models? (July 5, 2022). Almeida, Fan, Freire, Tang (2022), Available at SSRN: https://ssrn.com/abstract=3835108 or http://dx.doi.org/10.2139/ssrn.3835108

Caio Almeida (Contact Author)

Princeton University - Bendheim Center for Finance ( email )

20 Washington Rd
Princeton, NJ Mercer 08540
United States

Jianqing Fan

Princeton University - Bendheim Center for Finance ( email )

26 Prospect Avenue
Princeton, NJ 08540
United States
609-258-7924 (Phone)
609-258-8551 (Fax)

HOME PAGE: http://orfe.princeton.edu/~jqfan/

Gustavo Freire

Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE) ( email )

P.O. Box 1738
3000 DR Rotterdam, NL 3062 PA
Netherlands

HOME PAGE: http://https://www.eur.nl/people/gustavo-freire

Tinbergen Institute ( email )

Burg. Oudlaan 50
Rotterdam, 3062 PA
Netherlands

HOME PAGE: http://https://tinbergen.nl/person/2029/gustavo-freire

Erasmus Research Institute of Management (ERIM) ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands

HOME PAGE: http://https://www.erim.eur.nl/people/gustavo-freire/

Francesca Tang

Princeton University - Department of Political Science ( email )

Corwin Hall
Princeton, NJ 08544-1013
United States

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