JOURNAL ARTICLE
The Real Response to Uncertainty Shocks: The Risk Premium Channel.
Published In: Management Science (INFORMS), 2023, v. 69, n. 1. P. 119 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Bretscher, Lorenzo; Hsu, Alex; Tamoni, Andrea 3 of 3
Abstract
The article focuses on the interaction between uncertainty shocks and time-varying risk aversion (RA) and its effects on macroeconomic dynamics and asset pricing. Empirical evidence shows that positive uncertainty shocks lead to larger and more prolonged declines in output, consumption, investment, and equity prices when RA is high, a phenomenon termed the "risk premium channel." This channel is further supported by cross-sectional stock return patterns, where conditional exposures to uncertainty and RA explain significant risk premia, unlike unconditional exposures. The authors develop a New-Keynesian dynamic stochastic general equilibrium model incorporating Epstein–Zin recursive preferences with external habit to generate endogenous time-varying RA, successfully replicating empirical findings. The model highlights that habit-induced countercyclical RA amplifies uncertainty shocks by increasing precautionary savings and equity risk premia, causing firms to reduce investment and production, thereby deepening economic downturns. These results underscore the importance of accounting for state-dependent risk aversion in understanding the macroeconomic impact of uncertainty and in designing monetary and fiscal policies.
Additional Information
- Source:Management Science (INFORMS). 2023/01, Vol. 69, Issue 1, p119
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2023
- ISSN:0025-1909
- DOI:10.1287/mnsc.2022.4335
- Accession Number:161519085
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.