Temporal Coarse-Graining of Multi-Sector Default Count Data Generates Posterior-Implied Copulas

Battle Count: 53

The paper is primarily relevant to credit risk management and portfolio loss modeling rather than direct quantitative trading strategies. However, the dynamic factor structure, posterior-implied copulas, and horizon-dependent dependence modeling are relevant for credit derivatives pricing, CDS portfolio risk, and sector rotation strategies. The forecast evaluation framework (CRPS, log scores, interval coverage) is applicable to any probabilistic forecasting context. The market-wide vs. sector-rotation factor decomposition could inform sector allocation strategies in credit markets.

The implementation requires: (1) Bayesian MCMC sampling via NUTS with multiple chains and tuning iterations, (2) construction of block Toeplitz covariance matrices for temporal coarse-graining, (3) nonlinear survival aggregation maps applied to posterior samples, (4) rank-based copula estimation from posterior samples, (5) rolling-window forecast evaluation with re-estimation of eigenmode loadings at each forecast origin, (6) proper scoring rule computation (CRPS, log scores) over Monte Carlo predictive draws. The model is parsimonious in structure but computationally intensive due to Bayesian inference and the need for posterior predictive simulation across multiple horizons.

Analysis and simulation code is publicly available on GitHub with synthetic data for workflow demonstration. However, the empirical S&P default data are proprietary and cannot be shared. The Bayesian model specification, priors, and MCMC settings are fully documented in the appendices. Reproducibility is limited by data access but the methodology is transparent.