An efficient algorithm for the calculation of reserves for non-unit linked life policies
Mark Tucker () and
J. Mark Bull
Additional contact information
Mark Tucker: The University of Edinburgh, Postal: EPCC, The University of Edinburgh, King's Buildings, Mayfield Road, Edinburgh, EH9 3JZ, UK.
Algorithmic Finance, 2014, vol. 3, issue 3-4, 143-161
Abstract:
The underlying stochastic nature of the requirements for the Solvency II regulations has introduced significant challenges if the required calculations are to be performed correctly, without resorting to excessive approximations, within practical timescales. It is generally acknowledged by practising actuaries within UK life offices that it is currently impossible to correctly fulfil the requirements imposed by Solvency II using existing computational techniques based on commercially available valuation packages. Our work has already shown that it is possible to perform profitability calculations at a far higher rate than is achievable using commercial packages. One of the key factors in achieving these gains is to calculate reserves using recurrence relations that scale linearly with the number of time steps. Here, we present a general vector recurrence relation which can be used for a wide range of non-unit linked policies that are covered by Solvency II; such contracts include annuities, term assurances, and endowments. Our results suggest that by using an optimised parallel implementation of this algorithm, on an affordable hardware platform, it is possible to perform the ‘brute force’ approach to demonstrating solvency in a realistic timescale (of the order of a few hours).
Keywords: Solvency II; non-unit linked life reserves; brute force; parallel programming; OpenMP (search for similar items in EconPapers)
JEL-codes: C00 (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ris:iosalg:0028
Access Statistics for this article
Algorithmic Finance is currently edited by Phil Maymin
More articles in Algorithmic Finance from IOS Press
Bibliographic data for series maintained by Saskia van Wijngaarden ().