This dissertation focuses on the application of neural networks to financial model calibration. It provides an introduction to the mathematics of basic neural networks and training algorithms. Two simplified experiments based on the Black-Scholes and constant elasticity of variance models are used to demonstrate the potential usefulness of neural networks in calibration. In addition, the main experiment features the calibration of the Heston model using model-generated data. In the experiment, we show that the calibrated model parameters reprice a set of options to a mean relative implied volatility error of less than one per cent. The limitations and shortcomings of neural networks in model calibration are also investigated and discussed.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/29451 |
| Date | 07 February 2019 |
| Creators | Haussamer, Nicolai Haussamer |
| Publisher | University of Cape Town, Faculty of Commerce, African Institute of Financial Markets and Risk Management |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis, Masters, MPhil |
| Format | application/pdf |
Page generated in 0.0019 seconds