Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-28T21:53:31.607Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access Rights & Permissions [Opens in a new window]

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barton, R.R. (2015) Tutorial: Simulation metamodeling. 2015 Winter Simulation Conference (WSC), pp. 1765–1779.CrossRefGoogle Scholar
Bergstra, J. and Bengio, Y. (2012) Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13, 281–305.Google Scholar
Breiman, L. (1984) Classification and Regression Trees. Boca Raton, FL: Taylor & Francis, LLC.Google Scholar
Breiman, L. (1996) Bagging predictors. Machine Learning, 24(2), 123–140.CrossRefGoogle Scholar
Breiman, L. (2001) Using iterated bagging to debias regressions. Machine Learning, 45, 261–277.CrossRefGoogle Scholar
Chen, T. and Guestrin, C. (2016) Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794, New York, NY, USA: Association for Computing Machinery.CrossRefGoogle Scholar
Freund, Y. and Schapire, R.E. (1997) A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.CrossRefGoogle Scholar
Friedman, J.H. (2001) Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232.CrossRefGoogle Scholar
Friedman, J.H. (2002) Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378.CrossRefGoogle Scholar
Gan, G. (2013) Application of data clustering and machine learning in variable annuity valuation. Insurance: Mathematics and Economics, 53(3), 795–801.Google Scholar
Gan, G. and Huang, J.X. (2017) A data mining framework for valuing large portfolios of variable annuities. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1467–1475, New York, NY, USA: ACM.CrossRefGoogle Scholar
Gan, G., Quan, Z. and Valdez, E.A. (2018) Machine learning techniques for variable annuity valuation. 2018 4th International Conference on Big Data and Information Analytics (BigDIA), pp. 1–6.CrossRefGoogle Scholar
Gan, G. and Valdez, E.A. (2016) An empirical comparison of some experimental designs for the valuation of large variable annuity portfolios. Dependence Modeling, 4(1), 382–400.CrossRefGoogle Scholar
Gan, G. and Valdez, E.A. (2017) Valuation of large variable annuity portfolios: Monte carlo simulation and synthetic datasets. Dependence Modeling, 5(1), 354–374.CrossRefGoogle Scholar
Gan, G. and Valdez, E.A. (2018) Regression modeling for the valuation of large variable annuity portfolios. North American Actuarial Journal, 22(1), 40–54.CrossRefGoogle Scholar
Gan, G. and Valdez, E.A. (2019) Data clustering with actuarial applications. North American Actuarial Journal, 1–19.Google Scholar
Gao, H., Mamon, R. and Liu, X. (2017) Risk measurement of a guaranteed annuity option under a stochastic modelling framework. Mathematics and Computers in Simulation, 132, 100–119.CrossRefGoogle Scholar
Geneva Association, Report (2013) Variable annuities – an analysis of financial stability.Google Scholar
Goffard, P. and Guerrault, X. (2015) Is it optimal to group policyholders by age, gender, and seniority for bel computations based on model points? European Actuarial Journal, 5, 165–180.CrossRefGoogle Scholar
Hardy, M. (2003) Investment Guarantees: Modelling and Risk Management for Equity-Linked Life Insurance. Hoboken, New Jersey: John Wiley & Sons, Inc.Google Scholar
Hastie, T., Tibshirani, R. and Friedman, J. (2017) The Elements of Statistical Learning, 12th edition. New York: springer.Google Scholar
Ledlie, M.C., Corry, D.P., Finkelstein, G.S., Ritchie, A.J., Su, K. and Wilson, D.C.E. (2008) Variable annuities. British Actuarial Journal, 14(2), 327–389.CrossRefGoogle Scholar
Lin, Y. and Jeon, Y. (2006) Random forests and adaptive nearest neighbors. Journal of the American Statistical Association, 101(474), 578–590.CrossRefGoogle Scholar
Minasny, B. and McBratney, A.B. (2006) A conditioned latin hypercube method for sampling in the presence of ancillary information. Computers & Geosciences, 32(9), 1378–1388.CrossRefGoogle Scholar
Nister, D. and Stewenius, H. (2006) Scalable recognition with a vocabulary tree. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 2161–2168, USA: IEEE Computer Society.Google Scholar
Roudier, P. (2011) clhs: A R package for conditioned latin hypercube sampling.Google Scholar
Zhang, G. and Lu, Y. (2012) Bias-corrected random forests in regression. Journal of Applied Statistics, 39(1), 151–160.CrossRefGoogle Scholar
Zhao, Y., Mamon, R. and Gao, H. (2018) A two-decrement model for the valuation and risk measurement of a guaranteed annuity option. Econometrics and Statistics, 8, 231–249.CrossRefGoogle Scholar