Sequential Calibration of Asset Pricing Models to Option Prices

dc.contributor.advisorOuwehand, Peter
dc.contributor.authorOagile, Joel
dc.date.accessioned2019-03-01T06:31:05Z
dc.date.available2019-03-01T06:31:05Z
dc.date.issued2018
dc.date.updated2019-02-25T11:48:19Z
dc.description.abstractThis paper implements four calibration methods on stochastic volatility models. We estimate the latent state and parameters of the models using three non-linear filtering methods, namely the extended Kalman filter (EKF), iterated extended Kalman filter (IEKF) and the unscented Kalman filter (UKF). A simulation study is performed and the non-linear filtering methods are compared to the standard least square method (LSQ). The results show that both methods are capable of tracking the hidden state and time varying parameters with varying success. The non-linear filtering methods are faster and generally perform better on validation. To test the stability of the parameters, we carry out a delta hedging study. This exercise is not only of interest to academics, but also to traders who have to hedge their positions. Our results do not show any significant benefits resulting from performing delta hedging using parameter estimates obtained from non-linear filtering methods as compared to least square parameter estimates.
dc.identifier.apacitationOagile, J. (2018). <i>Sequential Calibration of Asset Pricing Models to Option Prices</i>. (). University of Cape Town ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management. Retrieved from http://hdl.handle.net/11427/29840en_ZA
dc.identifier.chicagocitationOagile, Joel. <i>"Sequential Calibration of Asset Pricing Models to Option Prices."</i> ., University of Cape Town ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management, 2018. http://hdl.handle.net/11427/29840en_ZA
dc.identifier.citationOagile, J. 2018. Sequential Calibration of Asset Pricing Models to Option Prices. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Oagile, Joel AB - This paper implements four calibration methods on stochastic volatility models. We estimate the latent state and parameters of the models using three non-linear filtering methods, namely the extended Kalman filter (EKF), iterated extended Kalman filter (IEKF) and the unscented Kalman filter (UKF). A simulation study is performed and the non-linear filtering methods are compared to the standard least square method (LSQ). The results show that both methods are capable of tracking the hidden state and time varying parameters with varying success. The non-linear filtering methods are faster and generally perform better on validation. To test the stability of the parameters, we carry out a delta hedging study. This exercise is not only of interest to academics, but also to traders who have to hedge their positions. Our results do not show any significant benefits resulting from performing delta hedging using parameter estimates obtained from non-linear filtering methods as compared to least square parameter estimates. DA - 2018 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2018 T1 - Sequential Calibration of Asset Pricing Models to Option Prices TI - Sequential Calibration of Asset Pricing Models to Option Prices UR - http://hdl.handle.net/11427/29840 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/29840
dc.identifier.vancouvercitationOagile J. Sequential Calibration of Asset Pricing Models to Option Prices. []. 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/11427/29840en_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.titleSequential Calibration of Asset Pricing Models to Option Prices
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMPhil
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