We introduce the scaled principal component analysis (sPCA) method to forecast oil volatility, and compare it with two commonly used dimensionality reduction methods: principal component analysis (PCA) and partial least squares (PLS). By combining the simple autoregressive model with the three dimensionality reduction methods, we obtain several interesting and notable findings. First, the model with the sPCA diffusion index performs substantially better than the competing models based on the out-of-sample R_oos² test. Moreover, the model with the sPCA diffusion index consistently demonstrates superior forecasting power compared with the other models under different macroeconomic conditions (e.g., business cycle recessions and expansions, high- and low-volatility levels, and the COVID-19 pandemic). Furthermore, the findings of our study are strongly robust to various robustness tests, such as alternative forecasting window sizes and different lags of model selection.

我们引入标度主成分分析（sPCA）方法来预测石油波动，并将其与两种常用的降维方法进行比较：主成分分析（PCA）和偏最小二乘法（PLS）。通过将简单的自回归模型与三个降维方法相结合，我们获得了几个有趣且值得注意的发现。首先，具有 sPCA 扩散指数的模型的性能明显优于基于样本外R _oos ²的竞争模型测试。此外，在不同宏观经济条件下（例如，商业周期衰退和扩张、高低波动率水平以及 COVID-19 大流行），具有 sPCA 扩散指数的模型始终表现出优于其他模型的预测能力。此外，我们的研究结果对各种稳健性测试具有很强的稳健性，例如替代预测窗口大小和模型选择的不同滞后。