2013/08/12 by A. K. Bahl, O. Baltzer, Bahl, A. K. +7
Computer Science · Economics, Econometrics and Finance · #Computational Engineering #Data Structures and Algorithms (cs.DS) #Distributed #FOS: Computer and information sciences #FOS: Economics and business #Finance #Parallel #Risk Management (q-fin.RM) #and Cluster Computing (cs.DC) #and Science (cs.CE) #cs.CE #cs.DC #cs.DS #q-fin.RM
paper · pdf · doi:10.48550/arxiv.1308.2572
Workshop Proceedings of International Conference on Parallel Processing, Lyon, France, 2013, 8 pages. arXiv admin note: text overlap with arXiv:1308.2066
arxiv created 2013/08/12 · arxiv updated 2013/08/19
Stochastic simulation techniques employed for the analysis of portfolios of insurance/reinsurance risk, often referred to as `Aggregate Risk Analysis', can benefit from exploiting state-of-the-art high-performance computing platforms. In this paper, parallel methods to speed-up aggregate risk analysis for supporting real-time pricing are explored. An algorithm for analysing aggregate risk is proposed and implemented for multi-core CPUs and for many-core GPUs. Experimental studies indicate that GPUs offer a feasible alternative solution over traditional high-performance computing systems. A simulation of 1,000,000 trials with 1,000 catastrophic events per trial on a typical exposure set and contract structure is performed in less than 5 seconds on a multiple GPU platform. The key result is that the multiple GPU implementation can be used in real-time pricing scenarios as it is approximately 77x times faster than the sequential counterpart implemented on a CPU.