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

Published online by Cambridge University Press:  08 September 2020

Hyukjun Gweon Open the ORCID record for Hyukjun Gweon [Opens in a new window] ,
Shu Li  and
Rogemar Mamon
Hyukjun Gweon*
Affiliation:
Department of Statistical and Actuarial Sciences, Western University, London, Ontario, Canada
Shu Li
Affiliation:
Department of Statistical and Actuarial Sciences, Western University, London, Ontario, Canada
Rogemar Mamon
Affiliation:
Department of Statistical and Actuarial Sciences, Western University, London, Ontario, Canada
*
E-Mail: hgweon@uwo.ca
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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.

Keywords

Variable annuityportfolio valuationmetamodelingbootstrap aggregatingregression treeC14C40C53G22
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 50 , Issue 3 , September 2020 , pp. 853 - 871
Copyright
© 2020 by Astin Bulletin. All rights reserved

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