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

34 Pages Posted: 13 Apr 2020

See all articles by Yuejiao Duan

Yuejiao Duan

Nankai University - School of Finance

Xiaoyun Fan

Nankai University - School of Finance

Haoran Li

Nankai University - School of Finance

Date Written: March 18, 2020

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.

Keywords: Quantile Regression Forest, Systemic Risk, Macroeconomic Forecast

JEL Classification: G10, E37, C53, C55

Suggested Citation

Duan, Yuejiao and Fan, Xiaoyun and Li, Haoran, Does the Quantile Regression Forest Learn More Information on Chinese Systemic Risk? (March 18, 2020). Available at SSRN: https://ssrn.com/abstract=3556400 or http://dx.doi.org/10.2139/ssrn.3556400

Yuejiao Duan

Nankai University - School of Finance ( email )

38 Tongyan Road, Jinnan District
Tianjin, Tianjin 300350
China

Xiaoyun Fan

Nankai University - School of Finance ( email )

38 Tongyan Road, Jinnan District
Tianjin, Tianjin 300350
China

Haoran Li (Contact Author)

Nankai University - School of Finance ( email )

38 Tongyan Road, Jinnan District
Tianjin, Tianjin 300350
China

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