A low-rank dynamic factor model with AR(1) latent states and binomial observations, when aggregated over time, generates horizon-dependent posterior-implied copulas that reproduce annual eigenvalue amplification on S&P sector default data and improve some forecast scores.
Tilmann Gneiting and Matthias Katzfuss
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Temporal Coarse-Graining of Multi-Sector Default Count Data Generates Posterior-Implied Copulas
A low-rank dynamic factor model with AR(1) latent states and binomial observations, when aggregated over time, generates horizon-dependent posterior-implied copulas that reproduce annual eigenvalue amplification on S&P sector default data and improve some forecast scores.