Efficient valuation of variable annuity portfolios with dynamic programming
提出递归动态规划方法，能在几秒内高精度估值大型异质变额年金组合，适用于准备金、资本要求和套期保值计算，并自然融入最优保单持有人行为。
Abstract The valuation of variable annuity portfolios presents major challenges for US life insurers. Recent studies propose machine learning and metamodeling techniques based on selecting a few representative guarantees. However, these methods face a critical trade‐off between speed and accuracy. In contrast, I propose a recursive dynamic programming approach and demonstrate its ability to value a large and highly heterogeneous variable annuity portfolio with a high degree of accuracy and within a few seconds—even under stochastic interest rates and volatility—since the heavy computational burden can be fully front‐loaded (in a one‐time effort at the guarantee's pricing stage). This makes the dynamic programming approach ideally suited for all variable annuity applications, including the computation of reserves and capital requirements and to determine the insurer's hedging position. Moreover, dynamic programming can naturally incorporate optimal policyholder behavior into the insurer's valuation.