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The Dynamics of Expected Returns: Evidence from Multi-Scale Time Series ModelingDaniele BianchiUniversity of Warwick, Warwick Business School Andrea TamoniLondon School of Economics & Political Science (LSE) July 9, 2016 Abstract: We show that low-order autoregression models for short-term expected returns imply long-term dynamics that have a (too) fast vanishing persistence when compared with the evidence from long-horizon predictive regressions. We then propose a novel modeling framework that exploits the low-frequency information in the predictors as a prior to update the high-frequency distribution of expected returns. Our framework shows that, in order to restore consistency with the empirical evidence from predictive regressions, the short-term dynamics of expected returns need to have long-range dependence. In turn, these long-memory type of dynamics generate first-order effects on forecasting and investment decisions, especially in the long-run. We quantify these effects along several dimensions.
Number of Pages in PDF File: 78 Keywords: Expected Returns, Long-Horizon Predictability, Multi-Scale, Bayesian Methods JEL Classification: G17, G11, C53, C58 Date posted: November 3, 2015 ; Last revised: July 10, 2016Suggested CitationContact Information
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