Throughout Europe, the introduction of Solvency II is forcing companies in the life assurance and pensions provision markets to change how they estimate their liabilities. Historically, each solvency assessment required that the estimation of liabilities was performed once, using actuaries' views of economic and demographic trends. Solvency II requires that each assessment of solvency implies a 1-in-200 chance of not being able to meet the liabilities. The underlying stochastic nature of these requirements has introduced significant challenges if the required calculations are to be performed correctly, without resorting to excessive approximations, within practical timescales. Currently, practitioners within UK pension provision companies consider the calculations required to meet new regulations to be outside the realms of anything which is achievable. This project brings the calculations within reach: this thesis shows that it is possible to perform the required calculations in manageable time scales, using entirely reasonable quantities of hardware. This is achieved through the use of several techniques: firstly, a new algorithm has been developed which reduces the computational complexity of the reserving algorithm from O(T2) to O(T) for T projection steps, and is sufficiently general to be applicable to a wide range of non unit-linked policies; secondly, efficient ab-initio code, which may be tuned to optimise its performance on many current architectures, has been written; thirdly, approximations which do not change the result by a significant amount have been introduced; and, finally, high performance computers have been used to run the code. This project demonstrates that the calculations can be completed in under three minutes when using 12,000 cores of a supercomputer, or in under eight hours when using 80 cores of a moderately sized cluster.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:743849 |
| Date | January 2018 |
| Creators | Tucker, Mark |
| Contributors | Bull, Mark ; Simpson, Alan |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/31166 |
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