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Quantiles autocorrelation in stock markets returns


  • Paulo Sergio Ceretta

    () (Federal University of Santa Maria)

  • Marcelo Brutti Righi

    () (Federal University of Santa Maria)

  • Alexandre Silva Da costa

    () (Federal University of Santa Maria)

  • Fernanda Maria Muller

    () (Federal University of Santa Maria)


Knowledge of dependence pattern in stock market has paramount importance for both theoretical and practical in financial markets. Their usefulness is wide, can be used in portfolio predictability (of portfolio) and risk management. The aim of this paper is to investigate the autoregressive dependence under the alternative perspective of quantile regression. Our study investigates a period from 2001 until 2012 daily returns of twenty stock markets in Latin America, Europe, USA and Asia-Pacific. Our results emphasize that the estimates obtained by quantile regression are different and more consistent than those by AR-GARCH. We conclude also that there is an asymmetric behavior of the investor, in association the quantiles with bear and bull markets.

Suggested Citation

  • 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.
  • Handle: RePEc:ebl:ecbull:eb-12-00469

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    References listed on IDEAS

    1. 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.
    2. Baba, Naohiko & Packer, Frank, 2009. "From turmoil to crisis: Dislocations in the FX swap market before and after the failure of Lehman Brothers," Journal of International Money and Finance, Elsevier, vol. 28(8), pages 1350-1374, December.
    3. 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.
    4. Cai, Zongwu & Xiao, Zhijie, 2012. "Semiparametric quantile regression estimation in dynamic models with partially varying coefficients," Journal of Econometrics, Elsevier, vol. 167(2), pages 413-425.
    5. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    6. 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.
    7. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    8. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    9. Marcelo Brutti Righi & Paulo Sergio Ceretta, 2012. "Analysis of the Tail Dependence Structure in the Global Markets: A Pair Copula Construction Approach," Economics Bulletin, AccessEcon, vol. 32(2), pages 1151-1161.
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    Cited by:

    1. Gębka, Bartosz & Wohar, Mark E., 2013. "The determinants of quantile autocorrelations: Evidence from the UK," International Review of Financial Analysis, Elsevier, vol. 29(C), pages 51-61.

    More about this item


    Autocorrelation; Quantile regression; Stock returns.;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables


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