Gaussian process regression approach to pricing multi-asset American options
| dc.contributor.advisor | Ouwehand, Peter | |
| dc.contributor.author | Mokone, Christoffel Maboe | |
| dc.date.accessioned | 2022-07-04T18:16:03Z | |
| dc.date.available | 2022-07-04T18:16:03Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2022-07-04T13:21:48Z | |
| dc.description.abstract | This dissertation explores the problem of pricing American options in high dimensions using machine learning. In particular, the Gaussian Process Regression Monte Carlo (GPR-MC) algorithm developed by Goudenege et al (2019). is explored, and ` its performance, i.e., its accuracy and efficiency, is benchmarked against the Least Squares Regression Method (LSM) developed by Carriere (1996) and popularised by Longstaff and Schwartz (2001). In this dissertation, American options are approximated by Bermudan options due to limited computing power. To test the performance of GPR-MC, an American geometric mean basket put option, an American arithmetic mean basket put option and an American maximum call option are priced under the multi-asset Black-Scholes and Heston models, using both GPRMC and LSM. The algorithms are run a 100 times to obtain mean option values, 95% confidence intervals about the means, and average computational times. Numerical results show that the efficiency of GPR-MC is independent of the number of underlying assets, in contrast to the LSM method which is not. At 10 underlying assets, GPR-MC is shown to be more efficient than LSM. Moreover, GPR-MC is reasonably accurate, producing relative errors that are within reasonable bounds. | |
| dc.identifier.apacitation | Mokone, C. M. (2022). <i>Gaussian process regression approach to pricing multi-asset American options</i>. (). ,Faculty of Commerce ,Department of Finance and Tax. Retrieved from http://hdl.handle.net/11427/36605 | en_ZA |
| dc.identifier.chicagocitation | Mokone, Christoffel Maboe. <i>"Gaussian process regression approach to pricing multi-asset American options."</i> ., ,Faculty of Commerce ,Department of Finance and Tax, 2022. http://hdl.handle.net/11427/36605 | en_ZA |
| dc.identifier.citation | Mokone, C.M. 2022. Gaussian process regression approach to pricing multi-asset American options. . ,Faculty of Commerce ,Department of Finance and Tax. http://hdl.handle.net/11427/36605 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Mokone, Christoffel Maboe AB - This dissertation explores the problem of pricing American options in high dimensions using machine learning. In particular, the Gaussian Process Regression Monte Carlo (GPR-MC) algorithm developed by Goudenege et al (2019). is explored, and ` its performance, i.e., its accuracy and efficiency, is benchmarked against the Least Squares Regression Method (LSM) developed by Carriere (1996) and popularised by Longstaff and Schwartz (2001). In this dissertation, American options are approximated by Bermudan options due to limited computing power. To test the performance of GPR-MC, an American geometric mean basket put option, an American arithmetic mean basket put option and an American maximum call option are priced under the multi-asset Black-Scholes and Heston models, using both GPRMC and LSM. The algorithms are run a 100 times to obtain mean option values, 95% confidence intervals about the means, and average computational times. Numerical results show that the efficiency of GPR-MC is independent of the number of underlying assets, in contrast to the LSM method which is not. At 10 underlying assets, GPR-MC is shown to be more efficient than LSM. Moreover, GPR-MC is reasonably accurate, producing relative errors that are within reasonable bounds. DA - 2022 DB - OpenUCT DP - University of Cape Town KW - finance KW - tax LK - https://open.uct.ac.za PY - 2022 T1 - Gaussian process regression approach to pricing multi-asset American options TI - Gaussian process regression approach to pricing multi-asset American options UR - http://hdl.handle.net/11427/36605 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/36605 | |
| dc.identifier.vancouvercitation | Mokone CM. Gaussian process regression approach to pricing multi-asset American options. []. ,Faculty of Commerce ,Department of Finance and Tax, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/36605 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Finance and Tax | |
| dc.publisher.faculty | Faculty of Commerce | |
| dc.subject | finance | |
| dc.subject | tax | |
| dc.title | Gaussian process regression approach to pricing multi-asset American options | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MPhil |