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Breaking down the non-normality of stock returns


  • Michail Karoglou


This paper investigates whether the non-normality typically observed in daily stock-market returns could arise because of the joint existence of breaks and GARCH effects. It proposes a data-driven procedure to credibly identify the number and timing of breaks and applies it on the benchmark stock-market indices of 27 OECD countries. The findings suggest that a substantial element of the observed deviations from normality might indeed be due to the co-existence of breaks and GARCH effects. However, the presence of structural changes is found to be the primary reason for the non-normality and not the GARCH effects. Also, there is still some remaining excess kurtosis that is unlikely to be linked to the specification of the conditional volatility or the presence of breaks. Finally, an interesting sideline result implies that GARCH models have limited capacity in forecasting stock-market volatility.

Suggested Citation

  • Michail Karoglou, 2010. "Breaking down the non-normality of stock returns," The European Journal of Finance, Taylor & Francis Journals, vol. 16(1), pages 79-95.
  • Handle: RePEc:taf:eurjfi:v:16:y:2010:i:1:p:79-95 DOI: 10.1080/13518470902872343

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    References listed on IDEAS

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    Cited by:

    1. Efe Çağlar Çağli & Pinar Evrim Mandaci & Pinar Hakan Kahyaoğlu, 2011. "Volatility Shifts and Persistence in Variance: Evidence from the Sector Indices of Istanbul Stock Exchange," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Eastern Macedonia and Thrace Institute of Technology (EMATTECH), Kavala, Greece, vol. 4(3), pages 119-140, December.
    2. Michail Karoglou & Bruce Morley & Dennis Thomas, 2013. "Risk and Structural Instability in US House Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 46(3), pages 424-436, April.
    3. Karanasos, Menelaos & Paraskevopoulos, Alexandros G. & Menla Ali, Faek & Karoglou, Michail & Yfanti, Stavroula, 2014. "Modelling stock volatilities during financial crises: A time varying coefficient approach," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 113-128.
    4. Menelaos Karanasos & Alexandros Paraskevopoulos & Faek Menla Ali & Michail Karoglou & Stavroula Yfanti, 2014. "Modelling Returns and Volatilities During Financial Crises: a Time Varying Coefficient Approach," Papers 1403.7179,
    5. Karanasos, Menelaos & Yfanti, Stavroula & Karoglou, Michail, 2016. "Multivariate FIAPARCH modelling of financial markets with dynamic correlations in times of crisis," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 332-349.
    6. Marcus Davidsson, 2014. "Tactic Asset Allocation and Conditional Return Expectations," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 3(2), pages 1-1.
    7. Bartosz Gębka & Michail Karoglou, 2013. "Is there life in the old dogs yet? Making break-tests work on financial contagion," Review of Quantitative Finance and Accounting, Springer, vol. 40(3), pages 485-507, April.
    8. Michail Karoglou & Panicos Demetriades & Siong Law, 2011. "One date, one break?," Empirical Economics, Springer, vol. 41(1), pages 7-24, August.
    9. S Coleman & M Karoglou, 2010. "Monetary Variability and Monetary Variables in the Franc Zone," Economic Issues Journal Articles, Economic Issues, vol. 15(2), pages 17-48, September.


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