Dissecting the financial cycle with dynamic factor models
基于美国宏观金融大数据，用动态因子模型提取三个合成金融周期成分，发现它们能显著提升经济衰退预测质量，其中与市场不确定性和风险厌恶相关的因子可作为政策制定者的早期预警指标。
The analysis of the financial cycle and its interaction with the macroeconomy has become a central issue for the design of macroprudential policy since the 2007–08 financial crisis. This paper proposes the construction of financial cycle measures for the US based on a large data set of macroeconomic and financial variables. More specifically, we estimate three synthetic financial cycle components that account for the majority of the variation in the data set using a dynamic factor model. We investigate whether these financial cycle components have significant predictive power for economic activity, inflation and short-term interest rates by means of Granger causality tests in a factor-augmented VAR set-up. Further, we analyze whether the synthetic financial cycle components have significant forecasting power for the prediction of economic recessions using dynamic probit models. Our main findings indicate that all financial cycle measures improve the quality of recession forecasts significantly. In particular, the factor related to financial market participants’ uncertainty and risk aversion—related to Rey’s (2013) global financial cycle—seems to serve as an appropriate early warning indicator for policymakers.