[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/22328.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/22328/1/MPRA_paper_22328.pdf
    File Function: original version
    Download Restriction: no

    File URL: https://mpra.ub.uni-muenchen.de/77633/1/MPRA_paper_22328.pdf
    File Function: revised version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. 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.
    5. 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.
    6. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bakri Abdul Karim & Muhammad Hafiz Mohd Shukri & Sharon Tay Chyu Yuin, 2018. "Weather, Mood and Stock Market Returns in Argentina," Accounting and Finance Research, Sciedu Press, vol. 7(4), pages 159-159, November.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giovanis, Eleftherios, 2009. "Bootstrapping Fuzzy-GARCH Regressions on the Day of the Week Effect in Stock Returns: Applications in MATLAB," MPRA Paper 22326, University Library of Munich, Germany.
    2. Issler, João Victor, 1999. "Estimating and forecasting the volatility of Brazilian finance series using arch models (Preliminary Version)," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 347, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    3. Turan Bali & Panayiotis Theodossiou, 2007. "A conditional-SGT-VaR approach with alternative GARCH models," Annals of Operations Research, Springer, vol. 151(1), pages 241-267, April.
    4. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    5. Bali, Turan G. & Weinbaum, David, 2007. "A conditional extreme value volatility estimator based on high-frequency returns," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 361-397, February.
    6. Ramona Dumitriu & Razvan Stefanescu, 2013. "Gone Fishin’ Effects on the Bucharest Stock Exchange," Annals of the University of Petrosani, Economics, University of Petrosani, Romania, vol. 13(1), pages 107-116.
    7. James W. Taylor, 2005. "Generating Volatility Forecasts from Value at Risk Estimates," Management Science, INFORMS, vol. 51(5), pages 712-725, May.
    8. Debabrata Mukhopadhyay & Nityananda Sarkar, 2013. "Stock Returns Under Alternative Volatility and Distributional Assumptions: The Case for India," International Econometric Review (IER), Econometric Research Association, vol. 5(1), pages 1-19, April.
    9. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.
    10. Kaiser, Thomas, 1996. "One-factor-Garch models for German stocks: Estimation and forecasting," Tübinger Diskussionsbeiträge 87, University of Tübingen, School of Business and Economics.
    11. Carol Alexander & Emese Lazar, 2009. "Modelling Regime‐Specific Stock Price Volatility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 761-797, December.
    12. Xekalaki, Evdokia & Degiannakis, Stavros, 2005. "Evaluating volatility forecasts in option pricing in the context of a simulated options market," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 611-629, April.
    13. Lourdes Uribe & Benjamin Perea & Gerardo Hernández-del-Valle & Oliver Schütze, 2018. "A Hybrid Metaheuristic for the Efficient Solution of GARCH with Trend Models," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 145-166, June.
    14. Per B. Solibakke, 2022. "Step‐ahead spot price densities using daily synchronously reported prices and wind forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 17-42, January.
    15. Stefanescu, Razvan & Dumitriu, Ramona, 2013. "MOY effects in returns and in volatilities of the Romanian capital market," MPRA Paper 52474, University Library of Munich, Germany, revised 28 Oct 2013.
    16. Hira Aftab & A. B. M. Rabiul Alam Beg, 2021. "Does Time Varying Risk Premia Exist in the International Bond Market? An Empirical Evidence from Australian and French Bond Market," IJFS, MDPI, vol. 9(1), pages 1-13, January.
    17. Kim, Dongcheol & Kon, Stanley J., 1999. "Structural change and time dependence in models of stock returns," Journal of Empirical Finance, Elsevier, vol. 6(3), pages 283-308, September.
    18. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, September.
    19. Bekaert, Geert & Engstrom, Eric & Ermolov, Andrey, 2015. "Bad environments, good environments: A non-Gaussian asymmetric volatility model," Journal of Econometrics, Elsevier, vol. 186(1), pages 258-275.
    20. Dumitriu, Ramona & Stefanescu, Razvan, 2013. "Efecte Gone Fishin’ la Bursa de Valori din Bucureşti [Gone Fishin’ Effects on the Bucharest Stock Exchange]," MPRA Paper 52473, University Library of Munich, Germany, revised 28 Sep 2013.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:22328. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.