Latent State and Parameter Estimation of Stochastic Volatility/Jump Models via Particle Filtering
| dc.contributor.author | Soane, Andrew | |
| dc.date.accessioned | 2019-02-04T11:25:03Z | |
| dc.date.available | 2019-02-04T11:25:03Z | |
| dc.date.issued | 2018 | |
| dc.date.updated | 2019-02-04T08:03:17Z | |
| dc.description.abstract | Particle filtering in stochastic volatility/jump models has gained significant attention in the last decade, with many distinguished researchers adding their contributions to this new field. Golightly (2009), Carvalho et al. (2010), Johannes et al. (2009) and Aihara et al. (2008) all attempt to extend the work of Pitt and Shephard (1999) and Liu and Chen (1998) to adapt particle filtering to latent state and parameter estimation in stochastic volatility/jump models. This dissertation will review their extensions and compare their accuracy at filtering the Bates stochastic volatility model. Additionally, this dissertation will provide an overview of particle filtering and the various contributions over the last three decades. Finally, recommendations will be made as to how to improve the results of this paper and explore further research opportunities. | |
| dc.identifier.apacitation | Soane, A. (2018). <i>Latent State and Parameter Estimation of Stochastic Volatility/Jump Models via Particle Filtering</i>. (). University of Cape Town ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management. Retrieved from http://hdl.handle.net/11427/29223 | en_ZA |
| dc.identifier.chicagocitation | Soane, Andrew. <i>"Latent State and Parameter Estimation of Stochastic Volatility/Jump Models via Particle Filtering."</i> ., University of Cape Town ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management, 2018. http://hdl.handle.net/11427/29223 | en_ZA |
| dc.identifier.citation | Soane, A. 2018. Latent State and Parameter Estimation of Stochastic Volatility/Jump Models via Particle Filtering. University of Cape Town. | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Soane, Andrew AB - Particle filtering in stochastic volatility/jump models has gained significant attention in the last decade, with many distinguished researchers adding their contributions to this new field. Golightly (2009), Carvalho et al. (2010), Johannes et al. (2009) and Aihara et al. (2008) all attempt to extend the work of Pitt and Shephard (1999) and Liu and Chen (1998) to adapt particle filtering to latent state and parameter estimation in stochastic volatility/jump models. This dissertation will review their extensions and compare their accuracy at filtering the Bates stochastic volatility model. Additionally, this dissertation will provide an overview of particle filtering and the various contributions over the last three decades. Finally, recommendations will be made as to how to improve the results of this paper and explore further research opportunities. DA - 2018 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2018 T1 - Latent State and Parameter Estimation of Stochastic Volatility/Jump Models via Particle Filtering TI - Latent State and Parameter Estimation of Stochastic Volatility/Jump Models via Particle Filtering UR - http://hdl.handle.net/11427/29223 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/29223 | |
| dc.identifier.vancouvercitation | Soane A. Latent State and Parameter Estimation of Stochastic Volatility/Jump Models via Particle Filtering. []. University of Cape Town ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management, 2018 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/29223 | en_ZA |
| dc.language.iso | eng | |
| dc.publisher.department | African Institute of Financial Markets and Risk Management | |
| dc.publisher.faculty | Faculty of Commerce | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject.other | Mathematical Finance | |
| dc.title | Latent State and Parameter Estimation of Stochastic Volatility/Jump Models via Particle Filtering | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MPhil |