Title

Can a Machine Correct Option Pricing Models?

Author(s)

Caio Almeida Caio Almeida (Princeton University)

Jianqing Fan Jianqing Fan (Princeton University)

Gustavo Freire Gustavo Freire (Erasmus School of Economics)

Francesca Tang Francesca Tang (Princeton University)

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.

Creation Date

2022-07

Section URL ID

Paper Number

2022-9

URL

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3835108

File Function

Jel

C45, C58, G13

Keyword(s)

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

Suppress

false

Series

13