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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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Empirical Finance.

Volume (Year): 19 (2012)
Issue (Month): 2 ()
Pages: 254-265

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Handle: RePEc:eee:empfin:v:19:y:2012:i:2:p:254-265

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Web page: http://www.elsevier.com/locate/jempfin

Related research

Keywords: Stock return distribution; Quantile autoregression; Overreaction and underreaction;

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References

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  1. Engle, Robert F & Manganelli, Simone, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series qt06m3d6nv, Department of Economics, UC San Diego.
  2. Lo, Andrew W. & Craig MacKinlay, A., 1990. "An econometric analysis of nonsynchronous trading," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 181-211.
  3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  4. Nicholas Barberis & Andrei Shleifer & Robert W. Vishny, 1997. "A Model of Investor Sentiment," NBER Working Papers 5926, National Bureau of Economic Research, Inc.
  5. Chuang, Chia-Chang & Kuan, Chung-Ming & Lin, Hsin-Yi, 2009. "Causality in quantiles and dynamic stock return-volume relations," Journal of Banking & Finance, Elsevier, vol. 33(7), pages 1351-1360, July.
  6. Lingjie Ma & Larry Pohlman, 2008. "Return forecasts and optimal portfolio construction: a quantile regression approach," The European Journal of Finance, Taylor & Francis Journals, vol. 14(5), pages 409-425.
  7. Koenker, Roger & Xiao, Zhijie, 2006. "Quantile Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 980-990, September.
  8. Campbell, Rachel A.J. & Forbes, Catherine S. & Koedijk, Kees G. & Kofman, Paul, 2008. "Increasing correlations or just fat tails?," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 287-309, March.
  9. Jonathan Lewellen, 2002. "Momentum and Autocorrelation in Stock Returns," Review of Financial Studies, Society for Financial Studies, vol. 15(2), pages 533-564, March.
  10. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
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Citations

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Cited by:
  1. Dirk G Baur & Thomas Dimpfl, 2012. "State-dependent Momentum in International Stock Markets," Working Paper Series 169, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
  2. Walid Mensi & Shawkat Hammoudeh & Juan Carlos Reboredo & Duc Khuong Nguyen, 2014. "Do global factors impact BRICS stock markets? A quantile regression approach," Working Papers 2014-159, Department of Research, Ipag Business School.
  3. Adelina Gschwandtner & Michael Hauser, 2013. "Profit Persistence and Stock Returns," Studies in Economics 1320, Department of Economics, University of Kent.
  4. Hendrik Kaufmannz & Robinson Kruse, 2013. "Bias-corrected estimation in potentially mildly explosive autoregressive models," CREATES Research Papers 2013-10, School of Economics and Management, University of Aarhus.
  5. Katarzyna Bien-Barkowska, 2012. ""Does it take volume to move fx rates?" Evidence from quantile regressions," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 12, pages 35-52.
  6. Paulo Sergio Ceretta & Marcelo Brutti Righi & Alexandre Silva Da costa & Fernanda Maria Muller, 2012. "Quantiles autocorrelation in stock markets returns," Economics Bulletin, AccessEcon, vol. 32(3), pages 2065-2075.

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