This thesis deals with the simulation of the Wishart stochastic volatility model (WSVM) which is a matrix generalization of the famous Heston model. Lately, an exact sampling scheme has been introduced. Its theoretical foundations are given in two papers. First, Ahdida and Alfonsi [2] find a methodology to simulate exactly the Wishart process for a general parameter space. Second, Kang and Kang [22] complete the scheme by proposing an expression for the conditional Laplace transform of the risky asset given the final state of the variance process. The thesis has two principle goals. First, we merge the theoretical foundations necessary to understand the exact sampling methodology and collect the corresponding proofs. Thereby, we build the basics for consecutive theoretical work, especially with respect to a necessary discussion of the numerical properties of the model. Second, we provide a prototype computational implementation. This implementation intends to be a first Monte Carlo framework for numerical experiments, testing purposes and further algorithmic improvements. It provides the tool to address future computation related research tasks. The current version is written in the MATLAB language m, and C.