Forecasting Tail Risks

This paper presents an early warning system as a set of multi-period forecasts of indicators of tail real and financial risks obtained using a large database of monthly US data for the period 1972:1-2014:12. Pseudo-real-time forecasts are generated from: (a) sets of autoregressive and factor-augmented vector autoregressions (VARs), and (b) sets of autoregressive and factor-augmented quantile projections. Our key finding is that forecasts obtained with AR and factor-augmented VAR forecasts significantly underestimate tail risks, while quantile projections deliver fairly accurate forecasts and reliable early warning signals for tail real and financial risks up to a 1-year horizon.