Title

Can a Machine Correct Option Pricing Models?

Author(s)

Caio Almeida Caio Almeida (Princeton University)

Jianqing Fan Jianqing Fan (Princeton University)

Francesca Tang Francesca Tang (Princeton University)

Abstract

We introduce a novel approach to capture implied volatility smiles. Given any parametric option pricing model used to fit a smile, we train a deep feedforward neural network on the model’s orthogonal residuals to correct for potential mispricings and boost performance. Using a large number of recent S&P500 options, we compare our hybrid machine-corrected model to several standalone parametric models ranging from ad-hoc corrections of Black-Scholes to more structural noarbitrage stochastic volatility models. Empirical results based on out-of-sample fitting errors - in cross-sectional and time-series dimensions - consistently confirm that a machine can in fact correct existing models without overfitting. Moreover, we find that our two-step technique is relatively indiscriminate: regardless of the bias or structure of the original parametric model, our boosting approach is able to correct it to approximately the same degree. Hence, our methodology is adaptable and versatile in its application to a large range of parametric option pricing models. As an overarching theme, machine corrected methods, guided by an implied volatility model as a template, outperform pure machine learning methods.

Creation Date

2021-05

Section URL ID

Paper Number

2021-44

URL

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

File Function

Jel

E37

Keyword(s)

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

Suppress

false

Series

13