Stock Return Autocorrelations Revisited: A Quantile Regression Approach

25 Pages Posted: 20 Dec 2011 Last revised: 9 Oct 2012

See all articles by Dirk G. Baur

Dirk G. Baur

University of Western Australia - Business School; Financial Research Network (FIRN)

Thomas Dimpfl

University of Tuebingen - Department of Statistics and Econometrics

Robert Jung

University of Hohenheim - Institute of Economics

Date Written: October 29, 2011

Abstract

The aim of this study is to provide a comprehensive description of the dependence pattern of stock returns by studying a range of quantiles of the conditional return distribution using quantile autoregression. This enables us in particular to study the behavior of extreme quantiles associated with large positive and negative returns in contrast to the central quantile which is closely related to the conditional mean in the least-squares regression framework. Our empirical results are based on 30 years of daily, weekly and monthly returns of the stocks comprised in the Dow Jones Stoxx 600 index. We find that lower quantiles exhibit positive dependence on past returns while upper quantiles are marked by negative dependence. This pattern holds when accounting for stock specific characteristics such as market capitalization, industry, or exposure to market risk.

Keywords: stock return distribution, quantile autoregression, overreaction and underreaction

JEL Classification: C22, G14

Suggested Citation

Baur, Dirk G. and Dimpfl, Thomas and Jung, Robert C., Stock Return Autocorrelations Revisited: A Quantile Regression Approach (October 29, 2011). Journal of Empirical Finance, Vol. 19, Issue 2, pp. 251-265, March 2012, Available at SSRN: https://ssrn.com/abstract=1974854 or http://dx.doi.org/10.2139/ssrn.1974854

Dirk G. Baur

University of Western Australia - Business School ( email )

School of Business
35 Stirling Highway
Crawley, Western Australia 6009
Australia

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

HOME PAGE: http://www.firn.org.au

Thomas Dimpfl (Contact Author)

University of Tuebingen - Department of Statistics and Econometrics ( email )

Germany

Robert C. Jung

University of Hohenheim - Institute of Economics ( email )

Schloss-Mittelhof (Ost)
70593 Stuttgart
Germany

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