Efficient Numerical Methods for Stochastic Differential Equations in Computational Finance
Committee membersAlouini, Mohamed-Slim
Gomes, Diogo A.
Embargo End Date2018-10-08
Permanent link to this recordhttp://hdl.handle.net/10754/625924
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Access RestrictionsAt 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.
AbstractStochastic 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.
CitationHappola, J. (2017). Efficient Numerical Methods for Stochastic Differential Equations in Computational Finance. KAUST Research Repository. https://doi.org/10.25781/KAUST-71T82