Scenario selection with LASSO regression for the valuation of variable annuity portfolios

Hang Nguyen, Michael Sherris, Andrés M. Villegas and Jonathan Ziveyi

Insurance: Mathematics and Economics, 2024, vol. 116, issue C, 27-43

Abstract: Variable annuities (VAs) are increasingly becoming popular insurance products in many developed countries which provide guaranteed forms of income depending on the performance of the equity market. Insurance companies often hold large VA portfolios and the associated valuation of such portfolios for hedging purposes is a very time-consuming task. There have been several studies focusing on inventing techniques aimed at reducing the computational time including the selection of representative VA contracts and the use of a metamodel to estimate the values of all contracts in the portfolio. In addition to the selection of representative contracts, this paper proposes using LASSO regression to select a set of representative scenarios, which in turn allows for the set of representative contracts to expand without significant increase in computational load. The proposed approach leads to a remarkable improvement in the computational efficiency and accuracy of the metamodel.

Keywords: Variable annuity; LASSO; Linear model; Neural network; Metamodeling (search for similar items in EconPapers)
JEL-codes: C45 C63 G22 G32 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:116:y:2024:i:c:p:27-43

DOI: 10.1016/j.insmatheco.2024.01.006

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