长期保险中的公平定价：一个统一框架
Fair Pricing in Long-Term Insurance: A Unified Framework
AI总结：
本文提出一种统一框架，将多状态转换模型的估计转化为泊松回归问题，以解决长期保险中公平定价的挑战。
摘要：
现有文献中关于精算情境下公平定价方法的研究主要集中在回归设置上。虽然这些方法适用于短期产品，但不清楚它们如何推广到长期产品，因为长期产品的定价本质上依赖于多状态模型中转换率的估计。为解决这一差距，我们提出了一种统一框架，将任何给定的多状态转换模型的估计重新表述为一组泊松回归问题。这种重新表述使现有公平定价方法能够直接应用，这些方法共同构成了我们提出的方法。作为示例，我们应用该框架对一种简化长期护理保险产品进行公平定价练习，使用密歇根大学健康与退休研究（HRS）的数据，重点是后处理方法。我们进一步解释了该框架如何轻松适应预处理和处理公平方法。
英文摘要：
Extant literature on fair pricing methods for actuarial contexts has primarily focused on the regression setting. While such approaches are well-suited to short-term products, it is unclear how they generalize to long-term products, whose pricing essentially relies on estimating transition rates in multi-state models. To address this gap, we propose a unified framework that recasts the estimation of any given multi-state transition model as a set of Poisson regression problems. This reformulation enables the direct application of existing fair pricing methods, which together constitute our proposed methodology. As an illustration, we apply the framework to a fair pricing exercise for a stylized long-term care insurance product using data from the University of Michigan Health and Retirement Study (HRS), focusing on a post-processing approach. We further explain how the framework readily accommodates pre-processing and in-processing fairness methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
