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Achieving Speedup in Aggregate Risk Analysis using Multiple GPUs

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  • A. K. Bahl
  • O. Baltzer
  • A. Rau-Chaplin
  • B. Varghese
  • A. Whiteway

Abstract

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.

Suggested Citation

  • A. K. Bahl & O. Baltzer & A. Rau-Chaplin & B. Varghese & A. Whiteway, 2013. "Achieving Speedup in Aggregate Risk Analysis using Multiple GPUs," Papers 1308.2572, arXiv.org.
  • Handle: RePEc:arx:papers:1308.2572
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    File URL: http://arxiv.org/pdf/1308.2572
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    References listed on IDEAS

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    1. David Cummins & Christopher Lewis & Richard Phillips, 1999. "Pricing Excess-of-Loss Reinsurance Contracts against Cat as trophic Loss," NBER Chapters,in: The Financing of Catastrophe Risk, pages 93-148 National Bureau of Economic Research, Inc.
    2. Woo, G., 2002. "Natural Catastrophe Probable Maximum Loss," British Actuarial Journal, Cambridge University Press, vol. 8(05), pages 943-959, December.
    3. Paul Glasserman & Philip Heidelberger & Perwez Shahabuddin, 2002. "Portfolio Value-at-Risk with Heavy-Tailed Risk Factors," Mathematical Finance, Wiley Blackwell, vol. 12(3), pages 239-269.
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