Access the full text.
Sign up today, get DeepDyve free for 14 days.
AbstractVariable annuities contain complex guarantees, whose fair market value cannot be calculated inclosed form. To value the guarantees, insurance companies rely heavily on Monte Carlo simulation, whichis extremely computationally demanding for large portfolios of variable annuity policies. Metamodeling approacheshave been proposed to address these computational issues. An important step of metamodelingapproaches is the experimental design that selects a small number of representative variable annuity policiesfor building metamodels. In this paper, we compare empirically several multivariate experimental designmethods for the GB2 regression model, which has been recently discovered to be an attractive modelto estimate the fair market value of variable annuity guarantees. Among the experimental design methodsexamined, we found that the data clustering method and the conditional Latin hypercube sampling methodproduce the most accurate results.
Dependence Modeling – de Gruyter
Published: Dec 14, 2016
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.