We show that low-order autoregression models for short-term expected returns imply long-term dynamics that have a (too) fast vanishing persistence when compared with the evidence from long-horizon predictive regressions. We then propose a novel modeling framework that exploits the low-frequency...

We provide a measure of sparsity for expected returns within the context of classical factor models. Our measure is inversely related to the percentage of active predictors. Empirically, sparsity varies over time and displays an apparent countercyclical behavior. Proxies for financial conditions...

We show that machine learning methods, in particular extreme trees and neural networks (NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts based on macroeconomic and yield information translate into economic gains that are larger than those obtained...

We develop a new variational Bayes estimation method for large-dimensional sparse vector autoregressive models with exogenous predictors. Unlike existing Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms, our approach is not based on a structural form representation of the...

A recent literature has shown that REIT returns contain strong evidence of bull and bear dynamic regimes that may be best captured using nonlinear econometric models of the Markov switching type. In fact, REIT returns would display regime shifts that are more abrupt and persistent than in the...

We study whether a large set of financial ratios provides valuable information about future excess stock returns. Confronted with a data-rich environment, we propose a novel ``divide and conquer" methodology that allows to efficiently retain all of the information available to investors. In...

We study the cross-sectional correlation between the returns on stablecoins and major cryptocurrency pairs. In doing so, we contribute to a growing literature that aims to understand the role of digital assets as an investment. Methodologically, we propose a large-scale Bayesian Vector...

We investigate the performance of funds that specialise in cryptocurrency markets. In doing so, we contribute to a growing literature that aims to understand the role of digital assets as an investment. Methodologically, we implement a novel panel bootstrap approach that samples jointly the...