Kalman Filtering and the Estimation of Multi-factor Affine Term Structure Models

dc.contributor.advisorOuwehand, Peter
dc.contributor.authorTokwe,Thabo
dc.date.accessioned2019-02-11T13:09:24Z
dc.date.available2019-02-11T13:09:24Z
dc.date.issued2018
dc.date.updated2019-02-11T11:50:30Z
dc.description.abstractWhen optimising the likelihood function one often encounters various stationary points and sometimes discontinuities in the parameter space (Gupta and Mehra, 1974). This is certainly true for a majority of multi-factor affine term structure models. Practitioners often recover different parameter optimisations depending on the initial parameters. If these parameters result in different option prices, the implications would be severe. This paper examines these implications through numerical experiments on the three-factor Vasicek and Arbitrage-free Nelson-Siegel (AFNS) models. The numerical experiments involve Kalman filtering as well as likelihood optimisation for parameter estimation. It was found that the parameter sets lead to the same short rate process and thus the same model. Moreover, likelihood optimisation in the AFNS does not result in different parameter sets irrespective of the starting point.
dc.identifier.apacitation (2018). <i>Kalman Filtering and the Estimation of Multi-factor Affine Term Structure Models</i>. (). University of Cape Town ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management. Retrieved from http://hdl.handle.net/11427/29465en_ZA
dc.identifier.chicagocitation. <i>"Kalman Filtering and the Estimation of Multi-factor Affine Term Structure Models."</i> ., University of Cape Town ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management, 2018. http://hdl.handle.net/11427/29465en_ZA
dc.identifier.citation 2018. Kalman Filtering and the Estimation of Multi-factor Affine Term Structure Models. . University of Cape Town ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management. http://hdl.handle.net/11427/29465en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Tokwe,Thabo AB - When optimising the likelihood function one often encounters various stationary points and sometimes discontinuities in the parameter space (Gupta and Mehra, 1974). This is certainly true for a majority of multi-factor affine term structure models. Practitioners often recover different parameter optimisations depending on the initial parameters. If these parameters result in different option prices, the implications would be severe. This paper examines these implications through numerical experiments on the three-factor Vasicek and Arbitrage-free Nelson-Siegel (AFNS) models. The numerical experiments involve Kalman filtering as well as likelihood optimisation for parameter estimation. It was found that the parameter sets lead to the same short rate process and thus the same model. Moreover, likelihood optimisation in the AFNS does not result in different parameter sets irrespective of the starting point. DA - 2018 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2018 T1 - Kalman Filtering and the Estimation of Multi-factor Affine Term Structure Models TI - Kalman Filtering and the Estimation of Multi-factor Affine Term Structure Models UR - http://hdl.handle.net/11427/29465 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/29465
dc.identifier.vancouvercitation. Kalman Filtering and the Estimation of Multi-factor Affine Term Structure Models. []. 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/11427/29465en_ZA
dc.language.isoeng
dc.publisher.departmentAfrican Institute of Financial Markets and Risk Management
dc.publisher.facultyFaculty of Commerce
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherMathematical Finance
dc.titleKalman Filtering and the Estimation of Multi-factor Affine Term Structure Models
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMPhil
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