How deep is your model? Network topology selection from a model validation perspective

Published in Journal of Mathematics in Industry

By Nikolai Nowaczyk, Jörg Kienitz, Sarp Kaya Acar & Qian Liang

Journal of Mathematics in Industry volume 12, Article number: 1 (2022)

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.

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