Tree-based Models for Variable Annuity Valuation: Parameter Tuning and Empirical Analysis

Abstract

Variable annuities have become popular retirement and investment vehicles due to their attractive guarantee features. Nonetheless, managing the financial risks associated with the guarantees poses great challenges for insurance companies. One challenge is risk quantification, which involves frequent valuation of the guarantees. Insurers rely on the use of Monte Carlo simulation as the guarantees are too complicated to be valued by closed-form formulas. Although Monte Carlo simulation is flexible to handle any types of guarantees, it is computationally intensive. Metamodels are increasing in popularity as efficient approaches for addressing the computational issues. In this paper, we empirically explore the use of tree-based models as metamodels for the valuation of the guarantees. In particular, we consider traditional regression trees, tree ensembles, and trees based on unbiased recursive partitioning. We also compare the performance of tree-based models to that of existing models such as ordinary kriging and GB2 regression. Our results show that tree-based models are efficient in producing accurate predictions and the gradient boosting method is considered the most superior.

Zhiyu (Frank) Quan
Zhiyu (Frank) Quan
Assistant Professor of Actuarial Science

My research interests include actuarial science and data science.

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