Can a Machine Correct Option Pricing Models?

Caio Almeida, Jianqing Fan, Gustavo Freire and Francesca Tang
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Caio Almeida: Princeton University
Gustavo Freire: Erasmus School of Economics
Francesca Tang: Princeton University

Working Papers from Princeton University. Economics Department.

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 (search for similar items in EconPapers)
JEL-codes: C45 C58 G13 (search for similar items in EconPapers)
Date: 2022-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for and nep-rmg
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https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3835108

Related works:
Journal Article: Can a Machine Correct Option Pricing Models? (2023)
Working Paper: Can a Machine Correct Option Pricing Models? (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:pri:econom:2022-9

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