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Parallel Simulations for Analysing Portfolios of Catastrophic Event Risk

Aman Bahl, Oliver Baltzer, Andrew Rau-Chaplin, Blesson Varghese
Center for Security, Theory and Algorithmic Research, International Institute of Information Technology, Hyderabad, India
arXiv:1308.2066 [cs.DC], (9 Aug 2013)

@article{2013arXiv1308.2066B,

   author={Bahl}, A. and {Baltzer}, O. and {Rau-Chaplin}, A. and {Varghese}, B.},

   title={"{Parallel Simulations for Analysing Portfolios of Catastrophic Event Risk}"},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1308.2066},

   primaryClass={"cs.DC"},

   keywords={Computer Science – Distributed, Parallel, and Cluster Computing, Computer Science – Computational Engineering, Finance, and Science, Computer Science – Performance},

   year={2013},

   month={aug},

   adsurl={http://adsabs.harvard.edu/abs/2013arXiv1308.2066B},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

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At the heart of the analytical pipeline of a modern quantitative insurance/reinsurance company is a stochastic simulation technique for portfolio risk analysis and pricing process referred to as Aggregate Analysis. Support for the computation of risk measures including Probable Maximum Loss (PML) and the Tail Value at Risk (TVAR) for a variety of types of complex property catastrophe insurance contracts including Cat eXcess of Loss (XL), or Per-Occurrence XL, and Aggregate XL, and contracts that combine these measures is obtained in Aggregate Analysis. In this paper, we explore parallel methods for aggregate risk analysis. A parallel aggregate risk analysis algorithm and an engine based on the algorithm is proposed. This engine is implemented in C and OpenMP for multi-core CPUs and in C and CUDA for many-core GPUs. Performance analysis of the algorithm indicates that GPUs offer an alternative HPC solution for aggregate risk analysis that is cost effective. The optimised algorithm on the GPU performs a 1 million trial aggregate simulation with 1000 catastrophic events per trial on a typical exposure set and contract structure in just over 20 seconds which is approximately 15x times faster than the sequential counterpart. This can sufficiently support the real-time pricing scenario in which an underwriter analyses different contractual terms and pricing while discussing a deal with a client over the phone.
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