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Dependence Modeling

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

Guojun Gan / Emiliano A. Valdez
Published Online: 2016-12-14 | DOI: https://doi.org/10.1515/demo-2016-0022

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.

Keywords: Variable annuity; Portfolio valuation; Metamodeling; Generalized beta of the second kind (GB2); Multivariate experimental design; Data clustering; Latin hypercube

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About the article

Received: 2016-08-24

Accepted: 2016-11-26

Published Online: 2016-12-14


Citation Information: Dependence Modeling, Volume 4, Issue 1, ISSN (Online) 2300-2298, DOI: https://doi.org/10.1515/demo-2016-0022.

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© 2016 Guojun Gan and Emiliano A. Valdez. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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