Deep hedging in incomplete markets

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.

Grantor

Department of Statistical Sciences

Year dc:date.issued

2024

Author dc:creator

• Stangroom, Jake

Advisor dc:contributor.advisor

• Mavuso, Melusi

• Statistical Sciences

Handle dc:identifier.uri

http://hdl.handle.net/11427/40641

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/40641