 Can a Machine Correct Option Pricing Models?Caio Almeida, Jianqing Fan, Gustavo Freire and Francesca Tang Journal of Business & Economic Statistics, 2023, vol. 41, issue 3, 995-1009 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. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:41:y:2023:i:3:p:995-1009 Ordering information: This journal article can be ordered from DOI: 10.1080/07350015.2022.2099871 Access Statistics for this article Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals |
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