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

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  • Baumöhl, Eduard
  • Lyócsa, Štefan

Abstract

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.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 43431.

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Date of creation: 26 Dec 2012
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Handle: RePEc:pra:mprapa:43431

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Keywords: stock markets; weekly returns; statistical properties;

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References

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  1. Engle, Robert F & Ng, Victor K, 1993. " Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, American Finance Association, vol. 48(5), pages 1749-78, December.
  2. Newey, W.K. & West, K.D., 1992. "Automatic Lag Selection in Covariance Matrix Estimation," Working papers, Wisconsin Madison - Social Systems 9220, Wisconsin Madison - Social Systems.
  3. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, Econometric Society, vol. 69(6), pages 1519-1554, November.
  4. 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, Charles University Prague, Faculty of Social Sciences, vol. 60(5), pages 414-425, December.
  5. Cook, Steven & Manning, Neil, 2004. "Lag optimisation and finite-sample size distortion of unit root tests," Economics Letters, Elsevier, Elsevier, vol. 84(2), pages 267-274, August.
  6. Pierre Perron & Zhongjun Qu, 2006. "A Simple Modification to Improve the Finite Sample Properties of Ng and Perron’s Unit Root Tests," Boston University - Department of Economics - Working Papers Series, Boston University - Department of Economics WP2006-010, Boston University - Department of Economics.
  7. Bart Hobijn & Philip Hans Franses & Marius Ooms, 2004. "Generalizations of the KPSS-test for stationarity," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, Netherlands Society for Statistics and Operations Research, vol. 58(4), pages 483-502.
  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. Robert F. Engle & Kevin Sheppard, 2001. "Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH," NBER Working Papers 8554, National Bureau of Economic Research, Inc.
  10. 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 411-19, August.
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Cited by:
  1. Tom\'a\v{s} V\'yrost & \v{S}tefan Ly\'ocsa & Eduard Baum\"ohl, 2014. "Granger Causality Stock Market Networks: Temporal Proximity and Preferential Attachment," Papers 1408.2985, arXiv.org.
  2. Výrost, Tomáš, 2012. "Country effects in CEE3 stock market networks: a preliminary study," MPRA Paper 43481, University Library of Munich, Germany.

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