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Option pricing and machine learning: a comparison of black-scholes, bachelier, and artificial neural networks
Practitioners and academics alike have applied the Black-Scholes model (or derivatives thereof) when pricing options practically since the introduction of the model in 1973. The recent coronavirus pandemic and the oil futures price crash of April 2020 have caused major markets to briefly switch to t...
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| Format: | Thesis |
| Language: | English |
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School of Management Studies
2023
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| _version_ | 1867614486433628160 |
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| access_status_str | Open Access |
| author | Gross, Eden |
| author2 | Kruger, Ryan |
| author_browse | Gross, Eden Kruger, Ryan |
| author_facet | Kruger, Ryan Gross, Eden |
| author_sort | Gross, Eden |
| collection | Thesis |
| description | Practitioners and academics alike have applied the Black-Scholes model (or derivatives thereof) when pricing options practically since the introduction of the model in 1973. The recent coronavirus pandemic and the oil futures price crash of April 2020 have caused major markets to briefly switch to the less widely-known Bachelier model to price derivatives, as the model allows for negative strikes on the underlying. This study evaluates the predictive ability and accuracy of both the Bachelier model and the Black-Scholes model when pricing European call options on the Standard & Poor's (S&P) 500 Index using five different volatility estimation methods. Moreover, it then compares the forecasts of the two parametrised models to a deep feed-forward artificial neural network which is also used to price such options. Overall, the artificial neural network is statistically superior in its predictive ability relative to both of the parameterised models, and the Black-Scholes model is statistically superior in its predictive ability to the Bachelier model. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/37156 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:52:48.660Z |
| 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 | School of Management Studies |
| publisherStr | School of Management Studies |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/37156 Option pricing and machine learning: a comparison of black-scholes, bachelier, and artificial neural networks Gross, Eden Kruger, Ryan Actuarial Science Practitioners and academics alike have applied the Black-Scholes model (or derivatives thereof) when pricing options practically since the introduction of the model in 1973. The recent coronavirus pandemic and the oil futures price crash of April 2020 have caused major markets to briefly switch to the less widely-known Bachelier model to price derivatives, as the model allows for negative strikes on the underlying. This study evaluates the predictive ability and accuracy of both the Bachelier model and the Black-Scholes model when pricing European call options on the Standard & Poor's (S&P) 500 Index using five different volatility estimation methods. Moreover, it then compares the forecasts of the two parametrised models to a deep feed-forward artificial neural network which is also used to price such options. Overall, the artificial neural network is statistically superior in its predictive ability relative to both of the parameterised models, and the Black-Scholes model is statistically superior in its predictive ability to the Bachelier model. 2023-03-02T11:47:43Z 2023-03-02T11:47:43Z 2022 2023-02-20T12:49:22Z Master Thesis Masters MCom http://hdl.handle.net/11427/37156 eng application/pdf School of Management Studies Faculty of Commerce |
| spellingShingle | Actuarial Science Gross, Eden Option pricing and machine learning: a comparison of black-scholes, bachelier, and artificial neural networks |
| thesis_degree_str | Master's |
| title | Option pricing and machine learning: a comparison of black-scholes, bachelier, and artificial neural networks |
| title_full | Option pricing and machine learning: a comparison of black-scholes, bachelier, and artificial neural networks |
| title_fullStr | Option pricing and machine learning: a comparison of black-scholes, bachelier, and artificial neural networks |
| title_full_unstemmed | Option pricing and machine learning: a comparison of black-scholes, bachelier, and artificial neural networks |
| title_short | Option pricing and machine learning: a comparison of black-scholes, bachelier, and artificial neural networks |
| title_sort | option pricing and machine learning a comparison of black scholes bachelier and artificial neural networks |
| topic | Actuarial Science |
| url | http://hdl.handle.net/11427/37156 |
| work_keys_str_mv | AT grosseden optionpricingandmachinelearningacomparisonofblackscholesbachelierandartificialneuralnetworks |