We analyze the recursive, out-of-sample performance of asset allocation decisions based on financial ratio-predictability under single-state linear and regime-switching models. We adopt both a statistical perspective to analyze whether models based on the dividend-price, earning-price, and...

We investigate whether it is possible to find a Stochastic Discount Factor (SDF) that jointly prices the cross-section of eight U.S. portfolios of stocks, Treasuries, corporate bonds, and commodities and replicates their observed moments, and especially correlations. We use the first three...

We investigate the out-of-sample, recursive predictive accuracy for (fully hedged) commodity future returns of two sets of forecasting models, i.e., hidden Markov chain models in which the coefficients of predictive regressions follow a regime switching process and stepwise variable selection...

We test whether three well-known commodity-specific variables (basis, hedgingpressure, and momentum) may improve the predictive power for commodity futuresreturns of models otherwise based on macroeconomic factors. We compute recursive,out-of-sample forecasts for fifteen monthly commodity...

We study the recursive, out-of-sample realized predictive performance of a rich set of predictor choices and models, spanning linear and Markov switching frameworks when the forecast target is represented by excess NCREIF and equity NAREIT returns. We find considerable pockets of predictive...