Does the Quantile Regression Forest Learn More Information on Chinese Systemic Risk?

Abstract

This article applies the quantile regression forest (QRF), which is an improved method for predicting future monetary policy and macroeconomic downside risks in China. The information used to forecast is derived from Chinese systemic risk. We construct two Chinese systemic risk information sets, one is the old information set with 12 indexes, the other is our information set with 19 indexes added. We also applied two methods to learn systemic risk information, including multiple regression and principal component analysis (PCA). We show that the multiple quantile regression forest (MQRF) and the principal component quantile regression forest (PCQRF) exhibit a superior out-of-sample forecasting ability when compared to alternative forecasting models, such as the multiple quantile regression (MQR) and the principal component quantile regression (PCQR). Furthermore, our systemic risk information set has good economic implications in predicting China’s monetary policy and macroeconomic downside risks.