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.

中文翻译：

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使用动态规划对可变年金投资组合进行有效估值

可变年金投资组合的估值对美国人寿保险公司提出了重大挑战。最近的研究提出了基于选择一些有代表性的保证的机器学习和元建模技术。然而，这些方法面临速度和准确性之间的关键权衡。相比之下，我提出了递归动态规划接近并证明其能够在几秒钟内高度准确地对大型且高度异质的可变年金投资组合进行估值——即使在随机利率和波动性的情况下——因为沉重的计算负担可以完全预先加载（在一个- 在保证定价阶段的时间努力）。这使得动态规划方法非常适合所有可变年金应用，包括准备金和资本要求的计算以及确定保险公司的对冲头寸。此外，动态规划可以自然地将最佳投保人行为纳入保险公司的估值中。