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Gaussian Process Regression for Option Pricing and Hedging
Recent literature in the field of quantitative finance has employed machine learning methods to speed up typical numerical calculations including derivative pricing, fitting Greek profiles, constructing volatility surfaces and modelling counterparty credit risk, to name a few. This dissertation aims...
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| Format: | Thesis |
| Language: | English |
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Department of Finance and Tax
2023
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| _version_ | 1867613272919769088 |
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| access_status_str | Open Access |
| author | Patel, Ishani |
| author2 | Ouwehand, Peter |
| author_browse | Ouwehand, Peter Patel, Ishani |
| author_facet | Ouwehand, Peter Patel, Ishani |
| author_sort | Patel, Ishani |
| collection | Thesis |
| description | Recent literature in the field of quantitative finance has employed machine learning methods to speed up typical numerical calculations including derivative pricing, fitting Greek profiles, constructing volatility surfaces and modelling counterparty credit risk, to name a few. This dissertation aims to investigate the accuracy and efficiency of Gaussian process regression (GPR) compared to traditional quantitative pricing algorithms. The GPR algorithm is applied to pricing a down-and-out barrier call option. Notably, Crepey and Dixon ´ (2019) propose an alternative method for computing the Gaussian process Greeks by directly differentiating the GPR option pricing model. Based on their approach, the GPR algorithm is further extended to compute the delta and vega of the option. Numerical experiments display that option pricing accuracy scores are within a tolerable range and demonstrate increased speed of considerable magnitudes with speed-up factors in the 1 000s. Computing the Greeks convey favourable computational properties; however, the GPR model struggles to obtain accurate predictions for the delta and vega. The trade-off between accuracy and speed is further investigated, where the inclusion of additional GPR input parameters hinder performance metrics whilst a larger training data set improves model accuracy. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/37735 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:31.121Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Department of Finance and Tax |
| publisherStr | Department of Finance and Tax |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/37735 Gaussian Process Regression for Option Pricing and Hedging Patel, Ishani Ouwehand, Peter Mathematical Finance Recent literature in the field of quantitative finance has employed machine learning methods to speed up typical numerical calculations including derivative pricing, fitting Greek profiles, constructing volatility surfaces and modelling counterparty credit risk, to name a few. This dissertation aims to investigate the accuracy and efficiency of Gaussian process regression (GPR) compared to traditional quantitative pricing algorithms. The GPR algorithm is applied to pricing a down-and-out barrier call option. Notably, Crepey and Dixon ´ (2019) propose an alternative method for computing the Gaussian process Greeks by directly differentiating the GPR option pricing model. Based on their approach, the GPR algorithm is further extended to compute the delta and vega of the option. Numerical experiments display that option pricing accuracy scores are within a tolerable range and demonstrate increased speed of considerable magnitudes with speed-up factors in the 1 000s. Computing the Greeks convey favourable computational properties; however, the GPR model struggles to obtain accurate predictions for the delta and vega. The trade-off between accuracy and speed is further investigated, where the inclusion of additional GPR input parameters hinder performance metrics whilst a larger training data set improves model accuracy. 2023-04-14T08:51:29Z 2023-04-14T08:51:29Z 2022 2023-04-14T07:21:47Z Master Thesis Masters MPhil http://hdl.handle.net/11427/37735 eng application/pdf Department of Finance and Tax Faculty of Commerce |
| spellingShingle | Mathematical Finance Patel, Ishani Gaussian Process Regression for Option Pricing and Hedging |
| thesis_degree_str | Master's |
| title | Gaussian Process Regression for Option Pricing and Hedging |
| title_full | Gaussian Process Regression for Option Pricing and Hedging |
| title_fullStr | Gaussian Process Regression for Option Pricing and Hedging |
| title_full_unstemmed | Gaussian Process Regression for Option Pricing and Hedging |
| title_short | Gaussian Process Regression for Option Pricing and Hedging |
| title_sort | gaussian process regression for option pricing and hedging |
| topic | Mathematical Finance |
| url | http://hdl.handle.net/11427/37735 |
| work_keys_str_mv | AT patelishani gaussianprocessregressionforoptionpricingandhedging |