Do shortages forecast aggregate and sectoral U.S. stock market realized variance?
Evidence from a century of data
Publication date
2026-04-21
Document type
Forschungsartikel
Author
Organisational unit
Scopus ID
Publisher
Elsevier
Series or journal
Journal of Empirical Finance
ISSN
Periodical volume
86
Article ID
101726
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
Forecasting
Realized volatility
Shortages
Statistical learning
Stock market
Abstract
Recent global economic and political events have made clear that shortages are a key factor driving macroeconomic and financial market developments. Against this backdrop, we studied the forecasting value of shortages for monthly U.S. stock market realized variance (RV) at the aggregate and sectoral level using data spanning the period 1900−2024 and 1926−2023 (for most sectors), respectively. To this end, we considered linear and non-linear statistical learning estimators. When we used linear estimators (OLS and shrinkage estimators), we did not find evidence that aggregate and disaggregate shortage indexes have predictive value for subsequent market or sectoral RVs. In contrast, when we used random forests, a nonlinear nonparametric estimator, we detected that aggregate and disaggregate shortage indexes improve forecast accuracy of market and sectoral RVs after controlling for realized moments (realized leverage, realized skewness, realized kurtosis, realized tail risks). We then decomposed RV into a high, medium, and low frequency component and found that the shortages indexes are correlated mainly with the medium and low frequencies of RV. Finally, we found that the predictive value of shortages for RV was larger in the 1980s and 1990s than in later parts of our sample period.
Description
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).
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Published version
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