Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/195863 
Authors: 
Year of Publication: 
2018
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 6 [Issue:] 3 [Publisher:] MDPI [Place:] Basel [Year:] 2018 [Pages:] 1-19
Publisher: 
MDPI, Basel
Abstract: 
A variable annuity is a popular life insurance product that comes with financial guarantees. Using Monte Carlo simulation to value a large variable annuity portfolio is extremely time-consuming. Metamodeling approaches have been proposed in the literature to speed up the valuation process. In metamodeling, a metamodel is first fitted to a small number of variable annuity contracts and then used to predict the values of all other contracts. However, metamodels that have been investigated in the literature are sophisticated predictive models. In this paper, we investigate the use of linear regression models with interaction effects for the valuation of large variable annuity portfolios. Our numerical results show that linear regression models with interactions are able to produce accurate predictions and can be useful additions to the toolbox of metamodels that insurance companies can use to speed up the valuation of large VA portfolios.
Subjects: 
variable annuity
portfolio valuation
linear regression
group-lasso
interaction effect
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Creative Commons License: 
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Document Type: 
Article
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