Parallel and distributed computing issues in pricing financial derivatives through quasi Monte Carlo

Monte Carlo (MC) techniques are often used to price complex financial derivatives. The computational effort can be substantial when high accuracy is required. However, MC computations are latency tolerant, and are thus easily parallelize even with high communication overheads, such as in a distributed compacting environment. A drawback of MC is its relatively slow convergence rate, which can be overcome through the use of quasi Monte Carlo (QMC) techniques which use low discrepancy sequences. We discuss the issues that arise in parallelizing QMC, especially in a heterogeneous computing environment, and present results of empirical studies on arithmetic Asian options, using three parallel QMC techniques that have recently been proposed. We expect the conclusions to be valid for other applications too.