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The Month-of-the-year Effect: Evidence from GARCH models in Fifty Five Stock Markets

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  • Giovanis, Eleftherios

Abstract

This paper studies the month of the year effect, where January effect presents positive and the highest returns of the other months of the year. In order to investigate the specific calendar effect in global level, fifty five stock market indices from fifty one countries are examined. Symmetric GARCH models are applied and based on asymmetries tests asymmetric GARCH models are estimated. The main findings of this study is that a December effect is found on twenty stock markets, with higher returns on the specific month, while February effect is presented in nine stock markets, followed by January and April effects in seven and six stock markets respectively. These patterns provide positive and highest returns on the mentioned months, while a pattern where a specific month gives a persistence signal of negative returns couldn’t be found.

Suggested Citation

  • Giovanis, Eleftherios, 2009. "The Month-of-the-year Effect: Evidence from GARCH models in Fifty Five Stock Markets," MPRA Paper 22328, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:22328
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    References listed on IDEAS

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    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. T. C. Mills & C. Siriopoulos & R. N. Markellos & D. Harizanis, 2000. "Seasonality in the Athens stock exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 10(2), pages 137-142.
    3. Dimitar Tonchev & Tae-Hwan Kim, 2004. "Calendar effects in Eastern European financial markets: evidence from the Czech Republic, Slovakia and Slovenia," Applied Financial Economics, Taylor & Francis Journals, vol. 14(14), pages 1035-1043.
    4. Wessel Marquering & Johan Nisser & Toni Valla, 2006. "Disappearing anomalies: a dynamic analysis of the persistence of anomalies," Applied Financial Economics, Taylor & Francis Journals, vol. 16(4), pages 291-302.
    5. Choudhry, Taufiq, 2001. "Month of the Year Effect and January Effect in Pre-WWI Stock Returns: Evidence from a Non-linear GARCH Model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(1), pages 1-11, January.
    6. 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.
    7. 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.
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. Zainudin Arsad & J. Andrew Coutts, 1997. "Security price anomalies in the London International Stock Exchange: a 60 year perspective," Applied Financial Economics, Taylor & Francis Journals, vol. 7(5), pages 455-464.
    10. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    11. Aggarwal, Reena & Rivoli, Pietra, 1989. "Seasonal and Day-of-the-Week Effects in Four Emerging Stock Markets," The Financial Review, Eastern Finance Association, vol. 24(4), pages 541-550, November.
    12. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

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    2. Weber Christoph S. & Nickol Philipp, 2016. "More on Calendar Effects on Islamic Stock Markets," Review of Middle East Economics and Finance, De Gruyter, vol. 12(1), pages 65-113, April.

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    More about this item

    Keywords

    seasonality; stock returns; calendar effects; month of the year effect; asymmetric GARCH models; asymmetry tests; January effect;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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