We implement a long-horizon static and dynamic portfolio allocation involving a risk-free and a risky asset. This model is calibrated at a quarterly frequency for ten European countries. We also use maximum-likelihood estimates and Bayesian estimates to account for parameter uncertainty. We find...

We propose a new asset-pricing framework in which all securities' signals are used to predict each individual return. While the literature focuses on each security's own- signal predictability, assuming an equal strength across securities, our framework is flexible and includes...

Predictions of asset returns and volatilities are heavily discussed and analyzed in the finance research literature. In this paper, we compare linear and nonlinear predictions for stock- and bond index returns and their covariance matrix. We show in-sample and out-of-sample prediction accuracy...

Many financial decisions such as portfolio allocation, risk management, option pricing and hedge strategies are based on forecasts of the conditional variances, covariances and correlations of financial returns. The paper shows an empirical comparison of several methods to predict one-step-ahead...

In this supplementary material we discuss the results corresponding to the case without short-selling constraints of the empirical application in the paper of Trucíos et al. (2019). These results are given in Tables 9-16

We investigate the out-of-sample forecasting ability of the HML, SMB, momentum, short-term and long-term reversal factors along with their size and value decompositions on U.S. bond and stock returns for a variety of horizons ranging from the short run (1 month) to the long run (2 years). Our...

I compute economic gains for a power utility investor from taking business cycle dependent return predictability into account. Recent studies show that stock returns are only predictable in recessions, and bond returns are only predictable in expansions. I examine whether this finding can be...

We find out-of-sample predictability of commodity futures excess returns using forecast combinations of 28 potential predictors. Such gains in forecast accuracy translate into economically significant improvements in certainty equivalent returns and Sharpe ratios for a mean-variance investor....

We explore the performance of mixed-frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a constrained parameter learning approach for sequential estimation allowing for belief revisions. Empirically, we find that mixed-frequency models...

This paper studies dividend growth predictability without restricting conditioning information set to dividend yield alone. We highlight that predictability crucially hinges on how dividend growth is constructed. Dividend growth without reinvestment is significantly predictable both in-sample...