ABSTRACT
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 Roos2 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.