AN EFFECTIVE BIAS-CORRECTED BAGGING METHOD FOR THE VALUATION OF LARGE VARIABLE ANNUITY PORTFOLIOS

Hyukjun Gweon, Shu Li and Rogemar Mamon

ASTIN Bulletin, 2020, vol. 50, issue 3, 853-871

Abstract: To evaluate a large portfolio of variable annuity (VA) contracts, many insurance companies rely on Monte Carlo simulation, which is computationally intensive. To address this computational challenge, machine learning techniques have been adopted in recent years to estimate the fair market values (FMVs) of a large number of contracts. It is shown that bootstrapped aggregation (bagging), one of the most popular machine learning algorithms, performs well in valuing VA contracts using related attributes. In this article, we highlight the presence of prediction bias of bagging and use the bias-corrected (BC) bagging approach to reduce the bias and thus improve the predictive performance. Experimental results demonstrate the effectiveness of BC bagging as compared with bagging, boosting, and model points in terms of prediction accuracy.

Date: 2020
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