Temporal Coarse-Graining Explains Effective Default Correlation in Corporate Defaults
Researchers demonstrate that aggregating short-horizon default probabilities into long-horizon estimates mechanically generates apparent default correlation, even when underlying monthly defaults are conditionally independent. The finding challenges standard credit risk models that attribute long-horizon overdispersion to contagion or common asset factors. This has implications for how financial institutions calibrate and interpret default correlation parameters in regulatory and portfolio risk models.
A new preprint posted to arXiv by Shintaro Mori presents a theoretical and empirical argument that effective default correlation in credit portfolios can arise purely from temporal coarse-graining of a latent default-probability path, without requiring genuine contagion or common-factor dependence. Using an Ornstein-Uhlenbeck–Binomial baseline model, the study shows that monthly defaults can be conditionally independent yet still produce overdispersion, autocorrelation, and apparent correlation when aggregated to longer horizons. The authors test this framework against corporate default-count data and compare it to Davis–Lo contagion and Vasicek common-factor extensions. When standard models are fit directly at each aggregation scale, they increasingly assign variance to instantaneous dependence mechanisms, but at the cost of worse predictive performance. By contrast, first coarse-graining monthly posterior latent paths and then estimating residual dependence parameters yields smaller residual covariance contributions and improved predictive density, suggesting that much previously attributed contagion or asset correlation may be a statistical artifact of aggregation. The results have practical relevance for credit risk measurement, stress testing, and the calibration of Basel-type models that rely on default correlation estimates.
What's missing
The study is a preprint and has not yet undergone peer review. Key limitations include reliance on corporate default-count data whose source, time period, and sectoral composition are not specified in the abstract, making it difficult to assess generalizability. The framework assumes a specific parametric form (OU process) for the latent path; robustness to alternative latent dynamics is an open question. The paper does not address how the proposed coarse-graining correction would be implemented operationally within existing regulatory capital frameworks.
What different sources said
- arXiv physicsCenter
Temporal Coarse-Graining of Latent Default-Probability Paths Generates Effective Default Correlation
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