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Empirical distributions of stock returns: European securities markets, 1990-95

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  • Felipe Aparicio
  • Javier Estrada
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    Abstract

    The assumption that daily stock returns are normally distributed has long been disputed by the data. In this article the normality assumption is tested (and clearly rejected) using time series of daily stock returns for 13 European securities markets. More importantly, four alternative specifications are fitted to the data, overall support is found for the scaled- t distribution (and partial support for a mixture of two Normal distributions), and the magnitude of the error that stems from predicting returns by using the Normal distribution is quantified. Data also show that normality may be a plausible assumption for monthly (but not for daily) stock returns.

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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal The European Journal of Finance.

    Volume (Year): 7 (2001)
    Issue (Month): 1 ()
    Pages: 1-21

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    Handle: RePEc:taf:eurjfi:v:7:y:2001:i:1:p:1-21

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    Keywords: Distributions Of Stock Returns Non-NORMALITY European Markets;

    References

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    1. S. James Press, 1967. "A Compound Events Model for Security Prices," The Journal of Business, University of Chicago Press, vol. 40, pages 317.
    2. Benoit Mandelbrot, 1963. "The Variation of Certain Speculative Prices," The Journal of Business, University of Chicago Press, vol. 36, pages 394.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, Econometric Society, vol. 59(2), pages 347-70, March.
    4. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
    5. Praetz, Peter D, 1972. "The Distribution of Share Price Changes," The Journal of Business, University of Chicago Press, vol. 45(1), pages 49-55, January.
    6. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, Econometric Society, vol. 41(1), pages 135-55, January.
    7. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, Elsevier, vol. 52(1-2), pages 5-59.
    8. Blattberg, Robert C & Gonedes, Nicholas J, 1974. "A Comparison of the Stable and Student Distributions as Statistical Models for Stock Prices," The Journal of Business, University of Chicago Press, vol. 47(2), pages 244-80, April.
    9. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, Econometric Society, vol. 55(2), pages 391-407, March.
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    Cited by:
    1. Landsman, Zinoviy, 2010. "On the Tail Mean-Variance optimal portfolio selection," Insurance: Mathematics and Economics, Elsevier, vol. 46(3), pages 547-553, June.
    2. Robert S. Chirinko & Hisham Foad, 2007. "Noise vs. News In Equity Returns," Working Papers 0025, San Diego State University, Department of Economics.

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