| Summary: | 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.
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