High performance computing in quantitative finance: A review from the pseudo-random number generator perspective

Abstract.

The great demand for high computational capabilities is omnipresent in every facet of modern financial activities, ranging from financial product pricing, trading and hedging at the front desk on the one end to risk management activities for in house monitoring and legislative compliance on the other. While this demand is met by scalable high performance computing, along with it come new challenges. As a notable proportion of financial computations involve the use of pseudo-random numbers, the engagement of a large number of parallel threads leads to consumption of large amount of pseudo-random numbers, uncovering potential intra-thread and inter-thread correlation that will lead to bias and loss of efficiency in the computation. This paper reviews, in the setting of derivative instrument pricing, the performance of some commonly used scalable pseudo-random number generators constructed based on different parallelization strategies: (1) parameterization (SPRNG), (2) sequence-splitting (TRNG and RngStream), and (3) cryptography (Random123). In addition, the potential impact of intra-thread and inter-thread correlation in pricing and sensitivity analysis of some common contingent claims via Monte Carlo simulation is examined.