, without the need to explicitly model it. We document that anchoring delivers large and significant gains in forecast accuracy …

We suggest using "realized volatility" as a volatility proxy to aid in model-based multivariate bond yield density … translate into superior predictive performance. We explore this conjecture in the context of density prediction of U.S. bond …

This paper examines the importance of realized volatility in bond yield density prediction. We incorporate realized … volatility into a Dynamic Nelson-Siegel (DNS) model with stochastic volatility and evaluate its predictive performance on US bond …

We suggest using "realized volatility" as a volatility proxy to aid in model-based multivariate bond yield density … translate into superior predictive performance. We explore this conjecture in the context of density prediction of U.S. bond …

types of restrictions are considered: positivity of the regression coefficient and positivity of the forecast. Bagging … bagging. Monte Carlo simulations show that forecast gains can be achieved in realistic sample sizes for the stock return … can improve the forecast performance further by smoothing the restriction through bagging. -- Constraints on predictive …

Common statistical measures of bond risk premia are volatile and countercyclical. This paper uses survey data on … interest rate forecasts to construct subjective bond risk premia. Subjective premia are less volatile and not very cyclical …

In this paper we develop new dynamic factor models to forecast multiple yield curves. Our methodology is based on a …