We use a flexible Bayesian model averaging method to estimate a factor pricing model characterized by structural uncertainty and instability in macro-financial factor loadings and idiosyncratic risks. We propose such a framework to investigate key differences in the pricing mechanism that...

We propose a Markov Switching Graphical Seemingly Unrelated Regression (MS-GSUR) model to investigate time-varying systemic risk based on a range of multi-factor asset pricing models. Methodologically, we develop a Markov Chain Monte Carlo (MCMC) scheme in which latent states are identified on...

Using five alternative data sets and a range of specifications concerning the underlying linear predictability models, we study whether long-run dynamic optimizing portfolio strategies may actually outperform simpler benchmarks in out-of-sample tests. The dynamic portfolio problems are solved...

I use Bayesian tools to develop a dynamic testing methodology for conditional factor pricing models, in which time-varying betas, idiosyncratic risks, and factors risk premia are jointly estimated in a single step. Based on this framework, I test over fifty years of post-war monthly data some of...

We study the effect of the predictability of order imbalance on market quality. We measure the degree of predictability by using the predictive likelihood from a dynamic linear model where the dependent variable is the day-ahead order imbalance. Empirically, we show that increasing order...

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...