Risk in a data-rich model

We characterize asymmetric tail risk across over one hundred U.S. macroeconomic and financial variables using a dynamic factor model with stochastic volatility. The model unifies growth-at-risk, inflation-at-risk, and sectoral heterogeneity through common factors whose volatility responds endogenously to shocks, combined with heterogeneous factor loadings. We find that asymmetric tail risk is pervasive and heterogeneous: some sectors exhibit severe asymmetry while others show minimal asymmetry, with variation across activity, price, and financial variables. The framework disentangles supply- and demand-driven tail risk dynamics, revealing how the balance of risks shifts across episodes, and identifies where vulnerabilities concentrate across the economy.