arXiv 4 Apr 2026 · Econometrics
arXiv:2604.03681 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a dynamic factor model in which common level and volatility factors evolve jointly, allowing conditional means and variances to interact endogenously within a large-information setting. The joint evolution of these factors provides a tractable framework for modeling risk, as fluctuations in volatility affect both the dispersion and the location of outcomes, generating state-dependent and asymmetric tail risks in predictive distributions. Volatility is captured by latent common factors that drive co-movement in second moments across a large panel, while heavy-tailed idiosyncratic shocks absorb transitory outliers and isolate persistent uncertainty dynamics. The framework embeds these interactions directly within a factor structure, allowing risk to arise endogenously from the joint dynamics of the system rather than being imposed through reduced-form approaches. Empirically, the model delivers systematic improvements in density forecast accuracy, particularly in the tails of the predictive distribution and at medium horizons. An application to international inflation highlights a dominant global level component in advanced economies and stronger regional and volatility contributions in emerging and developing economies, pointing to substantial heterogeneity in the role of uncertainty across countries.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Castelnuovo, E., K. Tuzcuoglu, and L. Uzeda (2025) Sectoral Uncertainty: A Hierarchical-Volatility Approach | 1.000 | 6 | 3 | 100% |
| 2 | Caldara, D., C. Scotti, and M. Zhong (2021) Macroeconomic and Financial Risks: A Tale of Mean and Volatility | 0.843 | 4 | 3 | 75% |
| 3 | Carriero, A., T. E. Clark, and M. Marcellino (2018) Measuring Uncertainty and Its Impact on the Economy | 0.843 | 4 | 3 | 75% |
| 4 | Mumtaz, H (2018) A generalised stochastic volatility in mean VAR self | 0.843 | 4 | 3 | 75% |
| 5 | Jurado, K., S. C. Ludvigson, and S. Ng (2015) Measuring uncertainty | 0.737 | 3 | 2 | 100% |
| 6 | Carriero, A., T. E. Clark, and M. Marcellino (2024) Capturing macro-economic tail risks with Bayesian vector autoregressions | 0.693 | 5 | 1 | 100% |
| Carriero | unmatched citation key Carriero | 0.644 | 4 | 1 | 100% |
| Clark | unmatched citation key Clark | 0.644 | 4 | 1 | 100% |
| Mumtaz | unmatched citation key Mumtaz | 0.644 | 4 | 1 | 100% |
| 10 | Geweke, J (1993) Bayesian Treatment of the Independent Student-t Linear Model | 0.644 | 3 | 2 | 67% |
Showing the top 10 of 96 scored citations. 3 of these could not be matched to a bibliography entry, so only the citation key is shown.