Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
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 compariso...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | Eng |
| Published: |
Department of Statistical Sciences
2024
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|