Model Calibration with Machine Learning
| dc.contributor.author | Haussamer, Nicolai Haussamer | |
| dc.date.accessioned | 2019-02-08T14:22:24Z | |
| dc.date.available | 2019-02-08T14:22:24Z | |
| dc.date.issued | 2018 | |
| dc.date.updated | 2019-02-07T06:59:30Z | |
| dc.description.abstract | 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. | |
| dc.identifier.apacitation | Haussamer, N. H. (2018). <i>Model Calibration with Machine Learning</i>. (). University of Cape Town ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management. Retrieved from http://hdl.handle.net/11427/29451 | en_ZA |
| dc.identifier.chicagocitation | Haussamer, Nicolai Haussamer. <i>"Model Calibration with Machine Learning."</i> ., University of Cape Town ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management, 2018. http://hdl.handle.net/11427/29451 | en_ZA |
| dc.identifier.citation | Haussamer, N. 2018. Model Calibration with Machine Learning. University of Cape Town. | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Haussamer, Nicolai Haussamer AB - 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. DA - 2018 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2018 T1 - Model Calibration with Machine Learning TI - Model Calibration with Machine Learning UR - http://hdl.handle.net/11427/29451 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/29451 | |
| dc.identifier.vancouvercitation | Haussamer NH. Model Calibration with Machine Learning. []. University of Cape Town ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management, 2018 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/29451 | en_ZA |
| dc.language.iso | eng | |
| dc.publisher.department | African Institute of Financial Markets and Risk Management | |
| dc.publisher.faculty | Faculty of Commerce | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject.other | Mathematical Finance | |
| dc.title | Model Calibration with Machine Learning | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MPhil |