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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 t...
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| Format: | Thesis |
| Language: | English |
| Published: |
African Institute of Financial Markets and Risk Management
2019
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| _version_ | 1867613149296852992 |
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| access_status_str | Open Access |
| author | Haussamer, Nicolai Haussamer |
| author_browse | Haussamer, Nicolai Haussamer |
| author_facet | Haussamer, Nicolai Haussamer |
| author_sort | Haussamer, Nicolai Haussamer |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/29451 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:31.816Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | African Institute of Financial Markets and Risk Management |
| publisherStr | African Institute of Financial Markets and Risk Management |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/29451 Model Calibration with Machine Learning Haussamer, Nicolai Haussamer Mathematical Finance 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. 2019-02-08T14:22:24Z 2019-02-08T14:22:24Z 2018 2019-02-07T06:59:30Z Master Thesis Masters MPhil http://hdl.handle.net/11427/29451 eng application/pdf African Institute of Financial Markets and Risk Management Faculty of Commerce University of Cape Town |
| spellingShingle | Mathematical Finance Haussamer, Nicolai Haussamer Model Calibration with Machine Learning |
| thesis_degree_str | Master's |
| title | Model Calibration with Machine Learning |
| title_full | Model Calibration with Machine Learning |
| title_fullStr | Model Calibration with Machine Learning |
| title_full_unstemmed | Model Calibration with Machine Learning |
| title_short | Model Calibration with Machine Learning |
| title_sort | model calibration with machine learning |
| topic | Mathematical Finance |
| url | http://hdl.handle.net/11427/29451 |
| work_keys_str_mv | AT haussamernicolaihaussamer modelcalibrationwithmachinelearning |