How Deep are Financial Models?

13 Pages Posted: 16 Jul 2020

See all articles by Nikolai Nowaczyk

Nikolai Nowaczyk

Quaternion Risk Management

Joerg Kienitz

University of Wuppertal - Applied Mathematics; University of Cape Town (UCT); Quaternion Risk Management

Sarp Kaya Acar

Quaternion Risk Management

Qian Liang

Quaternion Risk Management

Date Written: June 23, 2020

Abstract

Deep learning is a powerful tool, which is becoming increasingly popular in financial modeling. However, model validation requirements such as SR 11-7 pose a significant obstacle to the deployment of neural networks in a bank's production system. Their typically high number of (hyper-)parameters poses a particular challenge to model selection, benchmarking and documentation. We present a simple grid based method together with an open source implementation and show how this pragmatically satisfies model validation requirements. We illustrate the method by learning the option pricing formula in the Black-Scholes and the Heston model.

Keywords: neural networks, model validation, SR 11-7, derivatives, risk management, pricing

JEL Classification: G13, C10, C45

Suggested Citation

Nowaczyk, Nikolai and Kienitz, Joerg and Acar, Sarp Kaya and Liang, Qian, How Deep are Financial Models? (June 23, 2020). Available at SSRN: https://ssrn.com/abstract=3634232 or http://dx.doi.org/10.2139/ssrn.3634232

Nikolai Nowaczyk (Contact Author)

Quaternion Risk Management ( email )

54 Fitzwilliam Square North
Dublin, D02X308
Ireland

Joerg Kienitz

University of Wuppertal - Applied Mathematics ( email )

Gaußstraße 20
42097 Wuppertal
Germany

University of Cape Town (UCT) ( email )

Private Bag X3
Rondebosch, Western Cape 7701
South Africa

Quaternion Risk Management ( email )

54 Fitzwilliam Square North
Dublin, D02X308
Ireland

Sarp Kaya Acar

Quaternion Risk Management ( email )

54 Fitzwilliam Square North
Dublin, D02X308
Ireland

Qian Liang

Quaternion Risk Management ( email )

54 Fitzwilliam Square North
Dublin, D02X308
Ireland

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