Most factor-based forecasting models for the term structure of interest rates depend on a fixed number of factor loading functions that have to be specified in advance. In this study, we relax this assumption by building a yield curve forecasting model that learns new factor decompositions...

This paper examines the usefulness of shadow rates to measure the monetary policy stance by comparing them to the official policy rates and those implied by three types of Taylor rules in both inflation targeting countries (the UK, Canada, Australia and New Zealand) and others that have only...

This paper examines the usefulness of shadow rates to measure the monetary policy stance by comparing them to the official policy rates and those implied by three types of Taylor rules in both inflation targeting countries (the UK, Canada, Australia and New Zealand) and others that have only...

Equity basket correlation is an important risk factor. It characterizes the strength of linear dependence between assets and thus measures the degree of portfolio diversification. It can be estimated both under the physical measure from return series, and under the risk neutral measure from...

This paper documents that factors extracted from a large set of macroeconomic variables bear useful information for predicting monthly US excess stock returns and volatility over the period 1980-2005. Factor-augmented predictive regression models improve upon both benchmark models that only...

Real-time macroeconomic data refl ect the information available to market participants, whereas fi nal data-containing revisions and released with a delay-overstate the information set available to them. We document that the in-sample and out-of-sample Treasury return predictability is signifi...

Dimension reduction techniques for functional data analysis model and approximate smooth random functions by lower dimensional objects. In many applications the focus of interest lies not only in dimension reduction but also in the dynamic behaviour of the lower dimensional objects. The most...