Title: Implications of Returns Predictability across Horizons for Asset Pricing Models Speaker: Haoxi Yang, Nankai University Host: Mengmeng Guo, Associate professor, RIEM Time: 14:30-16:00, October 16, Friday Venue: Yide H513, Liulin Campus Abstract: We analyze predictors-based variance bounds, i.e. bounds on the variance of the stochastic discount factors (SDFs) that price a given set of returns conditional on the information contained in a vector of return predictors. For an asset pricing model identified by its state variables, information structure and model SDF, we supply a sufficient condition under which our predictors-based bounds constitute legitimate lower bounds on the variance of the SDF of the model. Using our predictors-based bounds we analyze discount factors produced by the long-run risk, the habit and the rare disasters models. We document that consumption-based asset pricing models such as long-run risk and habit models do not produce SDFs volatile enough at the one-year horizon. When we look at long-horizons our evidence shows that it is the habit model, not the long-run risk model, that satisfies our bounds. The rare disasters model satisfies our predictors-based bounds at each horizon. As a consequence, the investment horizon and the use of conditioning information emerge as fundamental ingredients that permit either to set models apart, or to select the common behavior among apparently different models.