Can a Machine Correct Option Pricing Models?

Caio Almeida, Jianqing Fan, Gustavo Freire, Francesca Tang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 (US)
JournalJournal of Business and Economic Statistics
DOIs
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

Keywords

  • Boosting
  • Deep learning
  • Implied volatility
  • Model correction
  • Stochastic volatility

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