Abstract:
We analyse the out-of-sample forecasting ability of a time-varying metric of risk aversion for the entire term structure of US Treasury securities as reflected by the three latent factors, level, slope and curvature. Daily data cover the out-of-sample period 22nd June 1988 to 3rd September 2020 within a quantiles-based framework. The results show statistically significant forecasting gains emanating from the inclusion of risk aversion for the tails of the conditional distributions of the quantiles-based models of the level, slope and curvature factors. The forecasting gains are shown in lower mean squared forecast errors at horizons of one-day, one-week, and one-month-ahead.
