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

A large literature has investigated predictability of the conditional mean of low frequency stock returns by macroeconomic and financial variables; however, little is known about predictability of the conditional distribution. We look at one-step-ahead out-of-sample predictability of the...

The inquiries to return predictability are traditionally limited to the first two moments, mean and volatility. Analogously, literature on portfolio selection also stems from a moment-based analysis with up to the fourth moment being considered. This paper develops a distribution-based framework...

An increase in the number of asset pricing models intensifies model uncertainties in assetpricing. While a pure "model selection" (singling out a best model) can result in a loss of usefulinformation, a full “model pooling” may increase the risk of including noisy information.We make a...

We study equity premium out-of-sample predictability by extracting the information contained in a high number of macroeconomic predictors via large dimensional factor models. We compare the well known factor model with a static representation of the common components with a more general model...

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 propose a model that extends the RT-GARCH model by allowing conditional heteroskedasticity in the volatility process. We show we are able to filter and forecast both volatility and volatility of volatility simultaneously in this simple setting. The volatility forecast function follows a...