Forecasting Financial Market Vulnerability in the U.S.: A Factor Model Approach

This paper presents a factor-based forecasting model for the financial market vulnerability in the U.S. We estimate latent common factors via the method of the principal components from 170 monthly frequency macroeconomic data to out-of-sample forecast the Cleveland Financial Stress Index. Our factor models outperform both the random walk and the autoregressive benchmark models in out-of-sample predictability for short-term forecast horizons, which is a desirable feature since financial crises often come to a surprise realization. Interestingly, the first common factor, which plays a key role in predicting the financial vulnerability index, seems to be more closely related with real activity variables rather than nominal variables. The recursive and the rolling window approaches with a 50% split point perform similarly well.