发表机构
Banca Monte dei Paschi di Siena（锡耶纳蒙特帕斯奇银行）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出了信息几何框架的隐状态EM扩展，构建了恢复信用风险分析中借款人层面后验信念的隐状态框架，通过模拟实验验证了其在估计结构参数、后验信念及违约概率方面的有效性，为信用风险相关研究提供了受控基准。
AI 中文摘要
本文开发了一种用于恢复信用风险分析中借款人层面后验信念的隐状态框架。信用可靠性和财务脆弱性被表示为隐维度，而观测到的借款人评分服从有限高斯混合模型，违约则取决于隐特征。通过对隐状态后验分布上的特征特定违约概率求平均，得到借款人特定的违约概率。采用联合期望-最大化（EM）程序，从观测到的评分-违约对中估计混合结构和特征特定违约概率。估计完成后，仅使用观测到的评分计算预测后验信念，从而保留违约实现前可用的信息。一项受控模拟实验评估了结构参数、后验信念和借款人层面违约概率的恢复情况。后验分布被解释为概率单纯形上的点，其恢复情况同时使用常规误差度量和信息几何散度进行评估。研究结果为研究隐经济结构、后验不确定性与信用风险预测之间的相互作用提供了受控基准。
英文摘要
This paper develops a latent-state framework for recovering borrower-level posterior beliefs in credit-risk analysis. Creditworthiness and financial fragility are represented as latent dimensions, while observed borrower scores follow a finite Gaussian mixture model and default depends on the latent profile. Borrower-specific probabilities of default are obtained by averaging profile-specific default probabilities over the posterior distribution of latent states. A joint Expectation--Maximization procedure is used to estimate the mixture structure and the profile-specific default probabilities from observed score--default pairs. After estimation, predictive posterior beliefs are computed using the observed scores alone, thereby preserving the information available before default realization. A controlled simulation experiment evaluates the recovery of structural parameters, posterior beliefs, and borrower-level probabilities of default. Posterior distributions are interpreted as points on the probability simplex, and their recovery is assessed using both conventional error measures and information-geometric divergences. The results provide a controlled benchmark for studying the interaction between latent economic structure, posterior uncertainty, and credit-risk prediction.
