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...

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...

We propose a joint modeling strategy for timing the joint distribution of the returns and their volatility. We do this by incorporating the potentially asymmetric links into the system of 'independent' predictive regressions of returns and volatility, allowing for asymmetric cross-correlations,...

This paper documents the fact that the factors extracted from a large set of macroeconomic variables contain information that can be useful for predicting monthly US excess stock returns over the period 1975 – 2014. Factor-augmented predictive regression models improve upon benchmark models...

This paper examines the lead/lag relations between size-sorted portfolio returns through the lens of financial cycles governing these returns using a novel econometric methodology. Specifically, we develop a Markov-switching vector autoregressive model that allows for imperfect synchronization...