English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

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.

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