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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.

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File URL: http://mpra.ub.uni-muenchen.de/22328/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 22328.

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Date of creation: 2009
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Handle: RePEc:pra:mprapa:22328

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Related research

Keywords: seasonality; stock returns; calendar effects; month of the year effect; asymmetric GARCH models; asymmetry tests; January effect;

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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, vol. 48(5), pages 1749-78, December.
  2. 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.
  3. 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.
  4. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  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. 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 542-47, August.
  7. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
  8. 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.
  9. 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.
  10. 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.
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