Risk in a Data-Rich Model

Dario Caldara; Haroon Mumtaz; Molin Zhong

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arXiv · 2608.05676

Risk in a Data-Rich Model

Dario Caldara·Haroon Mumtaz·Molin Zhong

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Abstract

We characterize asymmetric tail risk across over one hundred U.S. macroeconomic and financial variables using a dynamic factor model with stochastic volatility. A single mechanism unifies growth-at-risk, inflation-at-risk, and sectoral risk heterogeneity: common factors and their volatilities move together, while heterogeneous loadings transmit the resulting asymmetry unevenly across variables. We find that asymmetric tail risk is pervasive but heterogeneous. The heterogeneity is systematic: factor exposures, especially to financial conditions and inflation, explain over half of the cross-sectional variation in tail asymmetry across variables. These exposures determine where in the economy vulnerabilities concentrate and how the balance of tail risks shifts over time.

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Dario Caldara, Haroon Mumtaz, Molin Zhong. 2026-08-06. Risk in a Data-Rich Model. https://arxiv.org/abs/2608.05676

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