Do shortages forecast aggregate and sectoral U.S. stock market realized variance? Evidence from a century of data
利用1900-2024年数据，研究发现非线性随机森林模型下短缺指数能提升美国股市整体与行业已实现方差的预测精度，尤其在1980-1990年代效果更显著。
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.