This paper proposes the use of forecast combination to improve predictive accuracy in forecasting the U.S. business cycle index as published by the Business Cycle Dating Committee of the NBER. It focuses on one-step ahead out-of-sample monthly forecast utilising the well-established coincident...

Using a recursive modeling approach and data from the Euro area, the following paper analyzes the counter-cyclicity, stock price volatility is believed to demonstrate with respect to the state of the economy. It further tests whether such interdependence is exploitable for volatility...

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...

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...