Application of Adjoint Differentiation (AD) for Calculating Libor Market Model Sensitivities

dc.contributor.advisorMcWalter, Tom
dc.contributor.authorMorley, Niall
dc.date.accessioned2019-02-04T11:18:06Z
dc.date.available2019-02-04T11:18:06Z
dc.date.issued2018
dc.date.updated2019-02-04T08:33:29Z
dc.description.abstractThis dissertation explores a key challenge of the financial industry — the efficient computation of sensitivities of financial instruments. The adjoint approach to solving affine recursion problems (ARPs) is presented as a solution to this challenge. A Monte Carlo setting is adopted and it is illustrated how computational efficiency in sensitivity calculation may be significantly improved via the pathwise derivatives method through adapting an adjoint approach. This is achieved through the reversal of the order of differentiation in the pathwise derivatives algorithm in comparison to the standard, intuitive ‘forward’ approach. The Libor market model (LMM) framework is selected for examples to demonstrate these computational savings, with varying degrees of complexity of the LMM explored, from a one-factor model with constant volatility to a full factor model with time homogeneous volatilities.
dc.identifier.apacitationMorley, N. (2018). <i>Application of Adjoint Differentiation (AD) for Calculating Libor Market Model Sensitivities</i>. (). University of Cape Town ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management. Retrieved from http://hdl.handle.net/11427/29218en_ZA
dc.identifier.chicagocitationMorley, Niall. <i>"Application of Adjoint Differentiation (AD) for Calculating Libor Market Model Sensitivities."</i> ., University of Cape Town ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management, 2018. http://hdl.handle.net/11427/29218en_ZA
dc.identifier.citationMorley, N. 2018. Application of Adjoint Differentiation (AD) for Calculating Libor Market Model Sensitivities. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Morley, Niall AB - This dissertation explores a key challenge of the financial industry — the efficient computation of sensitivities of financial instruments. The adjoint approach to solving affine recursion problems (ARPs) is presented as a solution to this challenge. A Monte Carlo setting is adopted and it is illustrated how computational efficiency in sensitivity calculation may be significantly improved via the pathwise derivatives method through adapting an adjoint approach. This is achieved through the reversal of the order of differentiation in the pathwise derivatives algorithm in comparison to the standard, intuitive ‘forward’ approach. The Libor market model (LMM) framework is selected for examples to demonstrate these computational savings, with varying degrees of complexity of the LMM explored, from a one-factor model with constant volatility to a full factor model with time homogeneous volatilities. DA - 2018 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2018 T1 - Application of Adjoint Differentiation (AD) for Calculating Libor Market Model Sensitivities TI - Application of Adjoint Differentiation (AD) for Calculating Libor Market Model Sensitivities UR - http://hdl.handle.net/11427/29218 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/29218
dc.identifier.vancouvercitationMorley N. Application of Adjoint Differentiation (AD) for Calculating Libor Market Model Sensitivities. []. 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/11427/29218en_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.titleApplication of Adjoint Differentiation (AD) for Calculating Libor Market Model Sensitivities
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMPhil
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