Stock return autocorrelations revisited: A quantile regression approach

Dirk Baur (), Thomas Dimpfl and Robert Jung ()

No 24, University of Tübingen Working Papers in Business and Economics from University of Tuebingen, Faculty of Economics and Social Sciences, School of Business and Economics

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 (search for similar items in EconPapers)
JEL-codes: C22 G14 (search for similar items in EconPapers)
Date: 2012
New Economics Papers: this item is included in nep-fmk
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Citations: View citations in EconPapers (99)

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Journal Article: Stock return autocorrelations revisited: A quantile regression approach (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:tuewef:24

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