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Conditional skewness modelling for stock returns

  • Kurt Brannas
  • Niklas Nordman

Two approaches to modelling conditional skewness in a nonlinear model for stock returns are studied. It is found that a normal distribution can be rejected. A log-generalized gamma distribution with one time-varying density parameter, and a Pearson IV specification with three parameters are better supported by data. While the log-generalized gamma indicates that time-varying skewness is an important feature of the daily composite returns of NYSE, the Pearson IV model suggests that excess kurtosis rather than skewness should be accounted for.

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Article provided by Taylor & Francis Journals in its journal Applied Economics Letters.

Volume (Year): 10 (2003)
Issue (Month): 11 ()
Pages: 725-728

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Handle: RePEc:taf:apeclt:v:10:y:2003:i:11:p:725-728
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  1. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
  2. Brännäs, Kurt & de Gooijer, Jan G., 2000. "ASYMMETRIES IN CONDITIONAL MEAN AND VARIANCE: MODELLING STOCK RETURNS BY asMA-asQGARCH," Umeå Economic Studies 535, Umeå University, Department of Economics.
  3. Kurt Brannas & Niklas Nordman, 2003. "An alternative conditional asymmetry specification for stock returns," Applied Financial Economics, Taylor & Francis Journals, vol. 13(7), pages 537-541.
  4. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-30, August.
  5. Harvey, Campbell R. & Siddique, Akhtar, 1999. "Autoregressive Conditional Skewness," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(04), pages 465-487, December.
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