In this paper, we propose a latent threshold FAVAR model. The novelty is the interpretation of factors by observing how frequently factor loadings fall below estimated thresholds and become irrelevant. The results indicate that we are able to relate the factors to specific categories of the data...

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 yield data. When compared to popular...

The dynamic behavior of the term structure of interest rates is difficult to replicate with models, and even models with a proven track record of empirical performance have underperformed since the early 2000s. On the other hand, survey expectations are accurate predictors of yields, but only...

In this paper we develop new dynamic factor models to forecast multiple yield curves. Our methodology is based on a thorough empirical study of daily tenor-dependent term structures over the time period 2005-2017 which reveals important cross-tenor dependencies of yields. The suggested...

We propose a classical approach to estimate factor-augmented vector autoregressive (FAVAR) models with time variation in the factor loadings, in the factor dynamics, and in the variance-covariance matrix of innovations. When the time-varying FAVAR is estimated using a large quarterly dataset of...