METAMODELING FOR VARIABLE ANNUITY VALUATION: 10 YEARS BEYOND KRIGING

Variable annuities are retirement insurance products created by insurance companies that contain financial guarantees. To mitigate the financial risks associated with these guarantees, insurance companies have adopted dynamic hedging, which is a risk management technique. However, dynamic hedging is associated with computationally intensive valuations of variable annuity policies. Recently, metamodeling approaches have been developed to address the computational problems. A typical metamodeling approach consists of two components: an experimental design method and a metamodel. In this paper, we give a survey of metamodeling approaches developed in the past ten years. For each metamodeling approach, we will describe the experimental design method and the metamodel.

[1]  Guojun Gan,et al.  Variable annuity pricing, valuation, and risk management: a survey , 2022, Scandinavian Actuarial Journal.

[2]  Shu Li,et al.  Batch mode active learning framework and its application on valuing large variable annuity portfolios , 2021, Insurance: Mathematics and Economics.

[3]  Rogemar Mamon,et al.  AN EFFECTIVE BIAS-CORRECTED BAGGING METHOD FOR THE VALUATION OF LARGE VARIABLE ANNUITY PORTFOLIOS , 2020 .

[4]  Jiayi Zheng,et al.  Efficient Simulation Designs for Valuation of Large Variable Annuity Portfolios , 2020 .

[5]  Kai Liu,et al.  Real-Time Valuation of Large Variable Annuity Portfolios: A Green Mesh Approach , 2020 .

[6]  Gang Li,et al.  Fast Valuation of Large Portfolios of Variable Annuities via Transfer Learning , 2019, PRICAI.

[7]  Guojun Gan,et al.  Metamodeling for Variable Annuities , 2019 .

[8]  Emiliano A. Valdez,et al.  Valuation of Large Variable Annuity Portfolios with Rank Order Kriging , 2019, North American Actuarial Journal.

[9]  Andrea Molent,et al.  Gaussian process regression for pricing variable annuities with stochastic volatility and interest rate , 2019, Decisions in Economics and Finance.

[10]  X. Lin,et al.  Fast and Efficient Nested Simulation for Large Variable Annuity Portfolios: A Surrogate Modeling Approach , 2019, Insurance: Mathematics and Economics.

[11]  Big Data and Information Analytics , 2018, 2018 4th International Conference on Big Data and Information Analytics (BigDIA).

[12]  Guojun Gan,et al.  Machine Learning Techniques for Variable Annuity Valuation , 2018, 2018 4th International Conference on Big Data and Information Analytics (BigDIA).

[13]  Emiliano A. Valdez,et al.  Tree-based models for variable annuity valuation: parameter tuning and empirical analysis , 2018, Annals of Actuarial Science.

[14]  Guojun Gan,et al.  Nested Stochastic Valuation of Large Variable Annuity Portfolios: Monte Carlo Simulation and Synthetic Datasets , 2018, Data.

[15]  Guojun Gan,et al.  Valuation of Large Variable Annuity Portfolios Using Linear Models with Interactions , 2018, Risks.

[16]  Thomas F. Coleman,et al.  Moment matching machine learning methods for risk management of large variable annuity portfolios , 2018 .

[17]  Emiliano A. Valdez,et al.  Valuation of large variable annuity portfolios: Monte Carlo simulation and synthetic datasets , 2017 .

[18]  M. Feng,et al.  Efficient Nested Simulation for Conditional Tail Expectation of Variable Annuities , 2017, North American Actuarial Journal.

[19]  Xiangji Huang,et al.  A Data Mining Framework for Valuing Large Portfolios of Variable Annuities , 2017, KDD.

[20]  Guojun Gan,et al.  A Spatial Interpolation Framework for Efficient Valuation of Large Portfolios of Variable Annuities , 2017, 1701.04134.

[21]  Guojun Gan,et al.  Efficient Greek Calculation of Variable Annuity Portfolios for Dynamic Hedging: A Two-Level Metamodeling Approach , 2017 .

[22]  Guojun Gan,et al.  Modeling Partial Greeks of Variable Annuities with Dependence , 2016 .

[23]  Guojun Gan,et al.  An empirical comparison of some experimental designs for the valuation of large variable annuity portfolios , 2016 .

[24]  Guojun Gan,et al.  Regression Modeling for the Valuation of Large Variable Annuity Portfolios , 2016 .

[25]  Kenneth R. Jackson,et al.  A Neural Network Approach to Efficient Valuation of Large Portfolios of Variable Annuities , 2016, 1606.07831.

[26]  Guojun Gan,et al.  Application of metamodeling to the valuation of large variable annuity portfolios , 2015, 2015 Winter Simulation Conference (WSC).

[27]  Guojun Gan,et al.  Valuation of Large Variable Annuity Portfolios Under Nested Simulations: A Functional Data Approach , 2013 .

[28]  Guojun Gan,et al.  Application of Data Clustering and Machine Learning in Variable Annuity Valuation , 2013 .

[29]  A. McNeil,et al.  CALCULATING VARIABLE ANNUITY LIABILITY “GREEKS” USING MONTE CARLO SIMULATION , 2011, ASTIN Bulletin.

[30]  Mary R. Hardy,et al.  Investment guarantees : modeling and risk management for equity-linked life insurance , 2003 .

[31]  Phelim P. Boyle,et al.  Reserving for maturity guarantees: Two approaches , 1997 .

[32]  Gang Li,et al.  Deep Neighbor Embedding for Evaluation of Large Portfolios of Variable Annuities , 2019, KSEM.

[33]  R. Rosen,et al.  How Much Risk Do Variable Annuity Guarantees Pose to Life Insurers , 2017 .

[34]  Rada Y. Chirkova,et al.  Synthetic Datasets , 2011 .

[35]  Lars Pralle,et al.  Variable Annuities , 2010 .

[36]  A. Kleyner,et al.  Monte Carlo Simulation , 2011, Encyclopedia of GIS.