Efficient Numerical Methods for Stochastic Differential Equations in Computational Finance

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Type
Dissertation
Authors
Happola, Juho
Advisors
Tempone, Raul cc
Committee members
Alouini, Mohamed-Slim cc
Gomes, Diogo A. cc
Djehiche, Boualem
Mordecki, Ernesto
Zubelli, Jorge
Program
Applied Mathematics and Computational Science
KAUST Department
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Date
2017-09-19
Embargo End Date
2018-10-08
Permanent link to this record
http://hdl.handle.net/10754/625924

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At the time of archiving, the student author of this dissertation opted to temporarily restrict access to it. The full text of this dissertation became available to the public after the expiration of the embargo on 2018-10-08.
Abstract
Stochastic Differential Equations (SDE) offer a rich framework to model the probabilistic evolution of the state of a system. Numerical approximation methods are typically needed in evaluating relevant Quantities of Interest arising from such models. In this dissertation, we present novel effective methods for evaluating Quantities of Interest relevant to computational finance when the state of the system is described by an SDE.
Citation
Happola, J. (2017). Efficient Numerical Methods for Stochastic Differential Equations in Computational Finance. KAUST Research Repository. https://doi.org/10.25781/KAUST-71T82
DOI
10.25781/KAUST-71T82
Collections
Applied Mathematics and Computational Science Program; PhD Dissertations; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division