Black-Box Model Risk in Finance

31 Pages Posted: 19 Mar 2021

See all articles by Samuel N. Cohen

Samuel N. Cohen

University of Oxford - Mathematical Institute; The Alan Turing Institute

Derek Snow

The Alan Turing Institute

Lukasz Szpruch

University of Edinburgh - School of Mathematics

Date Written: February 9, 2021

Abstract

Machine learning models are increasingly used in a wide variety of financial settings. The difficulty of understanding the inner workings of these systems, combined with their wide applicability, has the potential to lead to significant new risks for users; these risks need to be understood and quantified. In this sub-chapter, we will focus on a well studied application of machine learning techniques, to pricing and hedging of financial options. Our aim will be to highlight the various sources of risk that the introduction of machine learning emphasises or de-emphasises, and the possible risk mitigation and management strategies that are available.

Keywords: Neural Networks, derivative pricing, hedging, model risk, data cleaning, quantitative finance, data-driven models, market generators, uncertainty, reinforcement learning, sensitivity, adversarial attacks, robustness, expert design

JEL Classification: D81, C6

Suggested Citation

Cohen, Samuel N. and Snow, Derek and Szpruch, Lukasz, Black-Box Model Risk in Finance (February 9, 2021). Available at SSRN: https://ssrn.com/abstract=3782412 or http://dx.doi.org/10.2139/ssrn.3782412

Samuel N. Cohen

University of Oxford - Mathematical Institute ( email )

Woodstock Road
Oxford, Oxfordshire OX26GG
United Kingdom

The Alan Turing Institute ( email )

British Library
96 Euston Road
London, NW1 2DB
United Kingdom

Derek Snow

The Alan Turing Institute ( email )

British Library, 96 Euston Rd
London, NW1 2DB
United Kingdom

HOME PAGE: http://www.turing.ac.uk/

Lukasz Szpruch (Contact Author)

University of Edinburgh - School of Mathematics ( email )

James Clerk Maxwell Building
Peter Guthrie Tait Rd
Edinburgh, EH9 3FD
United Kingdom

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