Model Calibration with Machine Learning

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.

Grantor dc:publisher.institution

African Institute of Financial Markets and Risk Management

Year dc:date.issued

2018

Author dc:creator

• Haussamer, Nicolai Haussamer

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/29451

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/29451