Recovering Posterior Beliefs in Credit Risk: A Latent-State EM Extension of the Information-Geometric Framework

Battle Count: 55

The paper is primarily focused on credit risk modeling and posterior belief recovery for financial intermediaries (banks). While credit risk is a component of broader financial risk management relevant to trading desks, the methodology is not directly applicable to trading strategy development, market microstructure, or algorithmic execution. The information-geometric framework could potentially inform portfolio risk monitoring, but the paper's scope is limited to borrower-level credit assessment.

The EM algorithm for a finite Gaussian mixture with additional Bernoulli default parameters is moderately complex. Requires understanding of multivariate Gaussian densities, responsibility-weighted updates, multiple EM initializations (20 runs with K-means + perturbation), label alignment via permutation search, and information-geometric divergence computations. The R code is provided but the full ancillary script is not included. Numerical stability considerations (log-sum-exp, ridge regularization, probability bounds) add implementation detail.

Complete R code listings provided for data generation, posterior computation, and parameter specification. Fixed random seed (1234). All true parameters explicitly stated. Ancillary script mentioned implementing log-scale Gaussian densities, log-sum-exp normalization, full EM pipeline, and validation. However, the ancillary script itself is not included in the extract, and no GitHub URL is provided.