Risk in a Data-Rich Model

2026-08-06

AI Summary

This study investigates the pervasive asymmetric tail risk and cross-sectional heterogeneity across more than one hundred U.S. macroeconomic and financial variables. Employing a dynamic factor model with stochastic volatility, the paper provides a unified characterization of the common drivers underlying growth, inflation, and sectoral risks, elucidating how time-varying volatility in common factors transmits asymmetry through heterogeneous loadings. The work proposes a single analytical framework to explain the origins of asymmetry across multiple risk categories, demonstrating that factor exposures—particularly to financial conditions and inflation—account for over 50% of the observed heterogeneity. Furthermore, it precisely identifies the dynamic evolution of economic vulnerabilities and the shifting locus of systemic risk over time.

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.

Problem

• asymmetric tail risk
• dynamic factor model
• stochastic volatility
• risk heterogeneity
• financial conditions

Innovation

• asymmetric tail risk
• dynamic factor model
• stochastic volatility
• factor loadings
• financial conditions