High-Frequency Growth-at-Risk of China : The Role of Macro-Financial Environment

High-frequency macro-financial environment variables provide more useful information and are efficient in predicting the low-frequency GDP growth rate. To this end, we extend the traditional Growth-at-Risk (GaR) into a high-frequency GaR (HF-GaR). In this extension, we construct three high-frequency macro-financial environment indices using a mixed frequency dynamic factor model (MF-DFM), and then use a mixed data sampling-quantile regression (MIDAS-QR) method to measure China's daily GaR from Jan 1, 2000 to Jun 30, 2022. The evidence shows that our HF-GaR has favorable prediction performance, with quantile mean absolute error (QMAE) and quantile root square error (QRMSE) values less than 0.1 and is significantly superior to the traditional GaR at the 1% level for most quantiles. Additionally, HF-GaR can offer early warning of economic downturns, and especially predict China's GDP growth rate at the 5% quantile less than 0 in 2020Q1. Moreover, we construct the quantile impulse response function (QIRF) to explore the impact of macro-financial environment on GDP growth rate. The results show that the macro-financial environment has a greater impact on GDP growth rate at extreme quantile levels