Deep hedging in incomplete markets

Stangroom, Jake

Deep hedging in incomplete markets

Thesis / Dissertation

2024

Abstract

This dissertation presents an extensive analysis of the neural network approximation of mean-variance hedging with a comparison between the current neural network approaches and the theoretical solutions. These theoretical solutions provide a simulation-based performance benchmark for this comparison. Furthermore, this dissertation implements a financial market generator which allows for a realistic performance analysis based on both real and pseudo-real data; whereby, deep hedging is shown to offer highly competitive industry performance. Finally, the dissertation shows that deep hedging is effective for other quadratic criterion such as those similar to local risk-minimisation techniques.

Keywords

Statistical Sciences

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