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A Data-Analytic Look at Skewness and Elongation in Common-Stock-Return Distributions

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  • Badrinath, S G
  • Chatterjee, Sangit

Abstract

This article explores the nature of skewness and elongation in daily common-stock-return distributions of individual firms using estimates of g (for skewness) and h (for elongation) obtained form Turkey's g and h distributions. Both parametric and nonparametric (bootstrap) estimates of standard errors of the g estimates are computed and compared. Daily return distributions are first examined cross-sectionally over a large sample of firms. The estimates of the skewness parameter exhibit variation across individual firms, but some general trends are evident across industry groups and firm sizes. Return distributions typically seem to be more elongated than the Gaussian distribution. From a time series perspective, both skewness and elongation are persistent in the return distributions of individual firms and vary over a finite range. First-order autocorrelation coefficients of monthly g and h estimates are large and suggest a certain degree of predictability.

Suggested Citation

  • Badrinath, S G & Chatterjee, Sangit, 1991. "A Data-Analytic Look at Skewness and Elongation in Common-Stock-Return Distributions," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(2), pages 223-233, April.
  • Handle: RePEc:bes:jnlbes:v:9:y:1991:i:2:p:223-33
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    Cited by:

    1. Lakshman A. Alles & John L. Kling, 1994. "Regularities In The Variation Of Skewness In Asset Returns," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 17(3), pages 427-438, September.
    2. Kabir K. Dutta & David F. Babbel, 2005. "Extracting Probabilistic Information from the Prices of Interest Rate Options: Tests of Distributional Assumptions," The Journal of Business, University of Chicago Press, vol. 78(3), pages 841-870, May.
    3. Albrecht, Peter & Schwake, Edmund & Winter, Peter, 2007. "Quantifizierung operationeller Risiken: Der Loss Distribution Approach," German Risk and Insurance Review (GRIR), University of Cologne, Department of Risk Management and Insurance, vol. 3(1), pages 1-45.
    4. Xu, Yihuan & Iglewicz, Boris & Chervoneva, Inna, 2014. "Robust estimation of the parameters of g-and-h distributions, with applications to outlier detection," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 66-80.
    5. Kabir K. Dutta & David F. Babbel, 2002. "On Measuring Skewness and Kurtosis in Short Rate Distributions: The Case of the US Dollar London Inter Bank Offer Rates," Center for Financial Institutions Working Papers 02-25, Wharton School Center for Financial Institutions, University of Pennsylvania.
    6. Kemal Eyuboglu & Sinem Eyuboglu & Rahmi Yamak, 2016. "Predicting Intra-Day and Day of the Week Anomalies in Turkish Stock Market," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 18(59), pages 73-94, March.

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