No. 997: Risk in a Data-Rich Model

Dario Caldara Federal Reserve Board Haroon Mumtaz School of Economics & Finance, Queen Mary University of London Molin Zhong Federal Reserve Board

March 9, 2026

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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.
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.

J.E.L classification codes: C11; C32; C38; E32; E44.

Keywords: Dynamic Factor Model; Tail Risk; Stochastic Volatility; Leverage Effect; Growth-at-Risk; Sectoral Heterogeneity.