A Neural Network Approach to Understanding Implied Volatility Movements

29 Pages Posted: 12 Dec 2018 Last revised: 13 Apr 2020

See all articles by Jay Cao

Jay Cao

University of Toronto

Jacky Chen

University of Toronto

John C. Hull

University of Toronto - Rotman School of Management

Date Written: January 1, 2019

Abstract

We employ neural networks to understand volatility surface movements. We first use daily data on options on the S&P 500 index to derive a relationship between the expected change in implied volatility and three variables: the return on the index, the moneyness of the option, and the remaining life of the option. This model provides an improvement of 10.72% compared with a simpler analytic model. We then enhance the model with an additional feature: the level of the VIX index prior to the return being observed. This produces a further improvement of 62.12% and shows that the expected response of the volatility surface to movements in the index is quite different in high and low volatility environments.

Keywords: options, implied volatility movements, neural networks, deep learning

JEL Classification: G13

Suggested Citation

Cao, Jay and Chen, Jacky and Hull, John C., A Neural Network Approach to Understanding Implied Volatility Movements (January 1, 2019). Available at SSRN: https://ssrn.com/abstract=3288067 or http://dx.doi.org/10.2139/ssrn.3288067

Jay Cao

University of Toronto ( email )

Toronto, Ontario M5S 3G8
Canada

Jacky Chen

University of Toronto ( email )

Toronto, Ontario M5S 3G8
Canada

John C. Hull (Contact Author)

University of Toronto - Rotman School of Management ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada
(416) 978-8615 (Phone)
416-971-3048 (Fax)

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