Monte Carlo (MC) methods have proved flexible, robust and very useful techniques in computational finance. Several studies have investigated ways to achieve greater efficiency for such methods for serial computers.In this paper, we concentrate on the parallelization potentials of the MC methods. While MC is generally thought to be `embarrassingly parallel', the results eventually depend on the quality of the underlying parallel pseudo-random number generators. There are several methods for obtaining pseudo-random numbers on a parallel computer and we briefly present some alternatives. Then, we turn to an application of security pricing where we empirically investigate the pros and cons of the different generators. This also allows us to assess the advantages or inconveniences of parallel MC versus its serial version in the computational finance framework.
