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Stock return autocorrelations revisited: A quantile regression approach

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  • Baur, Dirk G.
  • Dimpfl, Thomas
  • Jung, Robert C.

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 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 30years 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.

Suggested Citation

  • Baur, Dirk G. & Dimpfl, Thomas & Jung, Robert C., 2012. "Stock return autocorrelations revisited: A quantile regression approach," Journal of Empirical Finance, Elsevier, vol. 19(2), pages 254-265.
    • Handle: RePEc:eee:empfin:v:19:y:2012:i:2:p:254-265
    DOI: 10.1016/j.jempfin.2011.12.002
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    More about this item

    Keywords

    Stock return distribution; Quantile autoregression; Overreaction and underreaction;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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