Implications of Return Predictability across Horizons for Asset Pricing Models
报告人： Haoxi Yang, Nankai University
时间：2016-05-05 14:00 ~ 15:00
地点：Room 217, Guanghua Building 2
In this paper we show how the evidence on predictability of returns at different horizons can be used to discriminate among competing asset pricing models. 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. In particular, we show that the evidence on predictability of raw returns and downward sloping term structure of conditional variances of returns at different horizons translates into bounds on the variance of the SDFs that are much tighter than the respective unconditional bounds. Importantly, we also show that these predictors-based bounds constitute a legitimate lower bound on the variance of the SDF of a given asset pricing model, as long as the predictability of model-discounted returns is rejected. We then examine three leading classes of asset pricing models: external habit formation, rare disaster, and long-run risk. We show that for all these three models the hypothesis of absence of predictability of discounted returns cannot be rejected, while predictors-based bounds allow us to assess the performance of these models at long and short horizons and to discriminate between them.
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