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.
|Date of Award||Sep 19 2017|
|Original language||English (US)|
- Computer, Electrical and Mathematical Science and Engineering
|Supervisor||Raul Tempone (Supervisor)|
- Stochastic Differential Equations
- Numerical Methods