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Constructing weekly returns based on daily stock market data: A puzzle for empirical research?


  • Baumöhl, Eduard
  • Lyócsa, Štefan


The weekly returns of equities are commonly used in the empirical research to avoid the non-synchronicity of daily data. An empirical analysis is used to show that the statistical properties of a weekly stock returns series strongly depend on the method used to construct this series. Three types of weekly returns construction are considered: (i) Wednesday-to-Wednesday, (ii) Friday-to-Friday, and (iii) averaging daily observations within the corresponding week. Considerable distinctions are found between these procedures using data from the S&P500 and DAX stock market indices. Differences occurred in the unit-root tests, identified volatility breaks, unconditional correlations, ARMA-GARCH and DCC MV-GARCH models as well. Our findings provide evidence that the method employed for constructing weekly stock returns can have a decisive effect on the outcomes of empirical studies.

Suggested Citation

  • Baumöhl, Eduard & Lyócsa, Štefan, 2012. "Constructing weekly returns based on daily stock market data: A puzzle for empirical research?," MPRA Paper 43431, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:43431

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    References listed on IDEAS

    1. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," Review of Economic Studies, Oxford University Press, vol. 61(4), pages 631-653.
    2. Cheung, Yin-Wong & Lai, Kon S, 1995. "Lag Order and Critical Values of a Modified Dickey-Fuller Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 57(3), pages 411-419, August.
    3. Perron, Pierre & Qu, Zhongjun, 2007. "A simple modification to improve the finite sample properties of Ng and Perron's unit root tests," Economics Letters, Elsevier, vol. 94(1), pages 12-19, January.
    4. Engle, Robert F & Sheppard, Kevin K, 2001. "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH," University of California at San Diego, Economics Working Paper Series qt5s2218dp, Department of Economics, UC San Diego.
    5. Engle, Robert F & Ng, Victor K, 1993. " Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    6. Eduard Baumöhl & Tomáš Výrost, 2010. "Stock Market Integration: Granger Causality Testing with Respect to Nonsynchronous Trading Effects," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 60(5), pages 414-425, December.
    7. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    8. Výrost, Tomáš & Baumöhl, Eduard & Lyócsa, Štefan, 2011. "On the relationship of persistence and number of breaks in volatility: new evidence for three CEE countries," MPRA Paper 27927, University Library of Munich, Germany.
    9. Cook, Steven & Manning, Neil, 2004. "Lag optimisation and finite-sample size distortion of unit root tests," Economics Letters, Elsevier, vol. 84(2), pages 267-274, August.
    10. Bart Hobijn & Philip Hans Franses & Marius Ooms, 2004. "Generalizations of the KPSS-test for stationarity," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(4), pages 483-502.
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    Cited by:

    1. Eduard Baum??hl & ??tefan Ly??csa, 2014. "How smooth is the stock market integration of CEE-3?," William Davidson Institute Working Papers Series wp1079, William Davidson Institute at the University of Michigan.
    2. Výrost, Tomáš & Lyócsa, Štefan & Baumöhl, Eduard, 2015. "Granger causality stock market networks: Temporal proximity and preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 262-276.
    3. Výrost, Tomáš, 2012. "Country effects in CEE3 stock market networks: a preliminary study," MPRA Paper 43481, University Library of Munich, Germany.

    More about this item


    stock markets; weekly returns; statistical properties;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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