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An empirical comparison of some experimental designs for the valuation of large variable annuity portfolios

An empirical comparison of some experimental designs for the valuation of large variable annuity... Abstract Variable annuities contain complex guarantees, whose fair market value cannot be calculated in closed form. To value the guarantees, insurance companies rely heavily on Monte Carlo simulation, which is extremely computationally demanding for large portfolios of variable annuity policies. Metamodeling approaches have been proposed to address these computational issues. An important step of metamodeling approaches is the experimental design that selects a small number of representative variable annuity policies for building metamodels. In this paper, we compare empirically several multivariate experimental design methods for the GB2 regression model, which has been recently discovered to be an attractive model to estimate the fair market value of variable annuity guarantees. Among the experimental design methods examined, we found that the data clustering method and the conditional Latin hypercube sampling method produce the most accurate results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Dependence Modeling de Gruyter

An empirical comparison of some experimental designs for the valuation of large variable annuity portfolios

Gan, Guojun; Valdez, Emiliano A.
Dependence Modeling , Volume (1) – Dec 14, 2016
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19 pages

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Publisher
de Gruyter
Copyright
Copyright © 2016 by the
ISSN
2300-2298
eISSN
2300-2298
DOI
10.1515/demo-2016-0022
Publisher site
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Abstract

Abstract Variable annuities contain complex guarantees, whose fair market value cannot be calculated in closed form. To value the guarantees, insurance companies rely heavily on Monte Carlo simulation, which is extremely computationally demanding for large portfolios of variable annuity policies. Metamodeling approaches have been proposed to address these computational issues. An important step of metamodeling approaches is the experimental design that selects a small number of representative variable annuity policies for building metamodels. In this paper, we compare empirically several multivariate experimental design methods for the GB2 regression model, which has been recently discovered to be an attractive model to estimate the fair market value of variable annuity guarantees. Among the experimental design methods examined, we found that the data clustering method and the conditional Latin hypercube sampling method produce the most accurate results.

Journal

Dependence Modeling – de Gruyter

Published: Dec 14, 2016

References

  • Calculating variable annuity liability greeks using Monte Carlo simulation ASTIN
    Cathcart
  • Efficient space - filling and non - collapsing sequential design strategies for simulation - based modeling Eur
    Crombecq
  • Response surface methodology current status and future directions Qual
    Myers
  • A census of small latin hypercubes SIAM J Discrete
    McKay
  • Choosing the sample size of a computer experiment : A practical guide
    Loeppky
  • Design issues for generalized linear models : a review
    Khuri