Deep PPDEs for Rough Local Stochastic Volatility

21 Pages Posted: 17 Jun 2019

See all articles by Antoine (Jack) Jacquier

Antoine (Jack) Jacquier

Imperial College London; The Alan Turing Institute

Mugad Oumgari

Lloyds Banking Group

Date Written: June 6, 2019

Abstract

We introduce the notion of rough local stochastic volatility models, extending the classical concept to the case where volatility is driven by some Volterra process. In this setting, we show that the pricing function is the solution to a path-dependent PDE, for which we develop a numerical scheme based on Deep Learning techniques. Numerical simulations suggest that the latter is extremely efficient, and provides a good alternative to classical Monte Carlo simulations.

Keywords: rough volatility, Deep learning, Path-dependent PDEs

Suggested Citation

Jacquier, Antoine and Oumgari, Mugad, Deep PPDEs for Rough Local Stochastic Volatility (June 6, 2019). Available at SSRN: https://ssrn.com/abstract=3400035 or http://dx.doi.org/10.2139/ssrn.3400035

Antoine Jacquier (Contact Author)

Imperial College London ( email )

South Kensington Campus
London SW7 2AZ, SW7 2AZ
United Kingdom

HOME PAGE: http://wwwf.imperial.ac.uk/~ajacquie/

The Alan Turing Institute ( email )

British Library, 96 Euston Road
London, NW12DB
United Kingdom

Mugad Oumgari

Lloyds Banking Group ( email )

10 Gresham Street
London, EC2V 7AE
United Kingdom

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