Can a Machine Correct Option Pricing Models?

Caio Almeida*, Jianqing Fan, Gustavo Freire, Francesca Tang

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1-15
JournalJournal of Business and Economic Statistics
Volume0
Issue number0
DOIs
Publication statusAccepted/In press - 2022

Bibliographical note

Publisher Copyright:
© 2022 American Statistical Association.

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