Forecasting Financial Vulnerability in the US: A Factor Model Approach

This paper presents a factor-based forecasting model for the financial market vulnerability, measured by changes in the Cleveland Financial Stress Index (CFSI). We estimate latent common factors via the method of the principal components from 170 monthly frequency macroeconomic data in order to out-of-sample forecast the CFSI. Our factor models outperform both the random walk and the autoregressive benchmark models in out-of-sample predictability at least for the 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. We also present a binary choice version factor model that estimates the probability of the high stress regime successfully.