Do Shortages Forecast Aggregate and Sectoral U.S. Stock Market Realized Variance? Evidence from a Century of Data

Matteo Bonato (), Rangan Gupta and Christian Pierdzioch
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Matteo Bonato: Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France

No 202450, Working Papers from University of Pretoria, Department of Economics

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 nonlin- ear 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.

Keywords: Shortages; Stock market; Realized volatility; Statistical learning; Forecasting (search for similar items in EconPapers)
JEL-codes: C22 C53 E23 G10 G17 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2024-11
New Economics Papers: this item is included in nep-fdg, nep-fmk and nep-rmg
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