The Dynamics of Expected Returns: Evidence from Multi-Scale Time Series Modeling

78 Pages Posted: 3 Nov 2015 Last revised: 10 Jul 2016

See all articles by Daniele Bianchi

Daniele Bianchi

School of Economics and Finance, Queen Mary University of London

Andrea Tamoni

Rutgers, The State University of New Jersey - Rutgers Business School at Newark & New Brunswick

Date Written: 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.

Keywords: Expected Returns, Long-Horizon Predictability, Multi-Scale, Bayesian Methods

JEL Classification: G17, G11, C53, C58

Suggested Citation

Bianchi, Daniele and Tamoni, Andrea, The Dynamics of Expected Returns: Evidence from Multi-Scale Time Series Modeling (July 9, 2016). Available at SSRN: https://ssrn.com/abstract=2684728 or http://dx.doi.org/10.2139/ssrn.2684728

Daniele Bianchi

School of Economics and Finance, Queen Mary University of London ( email )

Mile End Rd
Mile End Road
London, London E1 4NS
United Kingdom

HOME PAGE: http://whitesphd.com

Andrea Tamoni (Contact Author)

Rutgers, The State University of New Jersey - Rutgers Business School at Newark & New Brunswick ( email )

1 Washington Park
Newark, NJ 07102
United States

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