Risk in a Data-Rich Model

Dario Caldara, Haroon Mumtaz () and Molin Zhong

No 1435, International Finance Discussion Papers from Board of Governors of the Federal Reserve System (U.S.)

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.

Keywords: Business fluctuations and cycles; Econometric modeling; Risk analysis; Volatility (search for similar items in EconPapers)
JEL-codes: C11 C32 C38 E32 E44 (search for similar items in EconPapers)
Pages: 72 p.
Date: 2026-03-30
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgif:102988

DOI: 10.17016/IFDP.2026.1435

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