The dynamics of expected returns: evidence from multi-scale time series modelling

Daniele Bianchi and Andrea Tamoni

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Conventional wisdom posits that all the relevant investors' information lies at the highest possible frequency of observation, so that long-run expected returns can be mechanically inferred by a forward aggregation of short-run estimates. We reverse such logic and propose a novel framework to model and extract the dynamics of latent short-term expected returns by coherently combining the lower-frequency information embedded in multiple predictors. We show that the information cascade from low- to high-frequency levels allows to identify long-lasting effects on expected returns that cannot be captured by standard persistent ARMA processes. The empirical analysis demonstrates that the ability of the model to capture simultaneously medium- to long-term fluctuations in the dynamics of expected returns, has first order implications for forecasting and investment decisions.

Keywords: expected returns; long-horizon predictability; multi-scale; Markov chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C53 G11 (search for similar items in EconPapers)
Pages: 70 pages
Date: 2016-03-01
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:118992

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