Modelling probabilities of corporate default
| dc.contributor.advisor | Mahomed, Obeid | |
| dc.contributor.author | Van Jaarsveldt, Cole | |
| dc.date.accessioned | 2020-02-25T12:05:21Z | |
| dc.date.available | 2020-02-25T12:05:21Z | |
| dc.date.issued | 2019 | |
| dc.date.updated | 2020-02-25T08:39:13Z | |
| dc.description.abstract | This dissertation follows, scrupulously, the probability of default model used by the National University of Singapore Risk Management Institute (NUS-RMI). Any deviations or omissions are noted with reasons related to the scope of this study on modelling probabilities of corporate default of South African firms. Using our model, we simulate defaults and subsequently, infer parameters using classical statistical frequentist likelihood estimation and one-world-view pseudo-likelihood estimation. We improve the initial estimates from our pseudo-likelihood estimation by using Sequential Monte Carlo techniques and pseudo-Bayesian inference. With these techniques, we significantly improve upon our original parameter estimates. The increase in accuracy is most significant when using few samples which mimics real world data availability | |
| dc.identifier.apacitation | Van Jaarsveldt, C. (2019). <i>Modelling probabilities of corporate default</i>. (). ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management. Retrieved from http://hdl.handle.net/11427/31331 | en_ZA |
| dc.identifier.chicagocitation | Van Jaarsveldt, Cole. <i>"Modelling probabilities of corporate default."</i> ., ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management, 2019. http://hdl.handle.net/11427/31331 | en_ZA |
| dc.identifier.citation | Van Jaarsveldt, C. 2019. Modelling probabilities of corporate default. | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Van Jaarsveldt, Cole AB - This dissertation follows, scrupulously, the probability of default model used by the National University of Singapore Risk Management Institute (NUS-RMI). Any deviations or omissions are noted with reasons related to the scope of this study on modelling probabilities of corporate default of South African firms. Using our model, we simulate defaults and subsequently, infer parameters using classical statistical frequentist likelihood estimation and one-world-view pseudo-likelihood estimation. We improve the initial estimates from our pseudo-likelihood estimation by using Sequential Monte Carlo techniques and pseudo-Bayesian inference. With these techniques, we significantly improve upon our original parameter estimates. The increase in accuracy is most significant when using few samples which mimics real world data availability DA - 2019 DB - OpenUCT DP - University of Cape Town KW - Mathematical Finance LK - https://open.uct.ac.za PY - 2019 T1 - Modelling probabilities of corporate default TI - Modelling probabilities of corporate default UR - http://hdl.handle.net/11427/31331 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/31331 | |
| dc.identifier.vancouvercitation | Van Jaarsveldt C. Modelling probabilities of corporate default. []. ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management, 2019 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/31331 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | African Institute of Financial Markets and Risk Management | |
| dc.publisher.faculty | Faculty of Commerce | |
| dc.subject | Mathematical Finance | |
| dc.title | Modelling probabilities of corporate default | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MPhil |