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Conditional skewness modelling for stock returns

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  • Kurt Brannas
  • Niklas Nordman

Abstract

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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Bibliographic Info

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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References

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  1. Kurt Brännäs & Niklas Nordman, 2001. "An Alternative Conditional Asymmetry Specification for Stock Returns," CESifo Working Paper Series 448, CESifo Group Munich.
  2. Hansen, B.E., 1992. "Autoregressive Conditional Density Estimation," RCER Working Papers 322, University of Rochester - Center for Economic Research (RCER).
  3. 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.
  4. 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.
  5. Kurt Brännäs & Jan G. de Gooijer, 2000. "Asymmetries in Conditional Mean and Variance: Modelling Stock Returns by asMA-asQGARCH," Tinbergen Institute Discussion Papers 00-049/4, Tinbergen Institute.
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Cited by:
  1. Changli He & Annastiina Silvennoinen & Timo Teräsvirta, 2005. "Parameterizing Unconditional Skewness in Models for Financial Time Series," Research Paper Series 169, Quantitative Finance Research Centre, University of Technology, Sydney.
  2. Kurt Brannas & Albina Soultanaeva, 2011. "Influence of news from Moscow and New York on returns and risks of Baltic States’ stock markets," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 11(1), pages 109-124, July.
  3. Lai, Jing-yi, 2012. "Shock-dependent conditional skewness in international aggregate stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(1), pages 72-83.
  4. Brännäs, Kurt, 2003. "Temporal Aggregation of the Returns of a Stock Index Series," UmeÃ¥ Economic Studies 614, Umeå University, Department of Economics.
  5. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2002. "Mixed normal conditional heteroskedasticity," CFS Working Paper Series 2002/10, Center for Financial Studies (CFS).
  6. Brännäs, Kurt & Soultanaeva, Albina, 2006. "Influence of News in Moscow and New York on Returns and Risks on Baltic State Stock Indices," UmeÃ¥ Economic Studies 696, Umeå University, Department of Economics.
  7. Fabio Pizzutilo, 2013. "The Distribution of the Returns of Japanese Stocks and Portfolios," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 3(9), pages 1249-1259, September.
  8. Bruno Feunou & Mohammad R. Jahan-Parvar & Roméo Tedongap, 2013. "Which Parametric Model for Conditional Skewness?," Working Papers 13-32, Bank of Canada.
  9. Stavros Stavroyiannis & Leonidas Zarangas, 2013. "Out of Sample Value-at-Risk and Backtesting with the Standardized Pearson Type-IV Skewed Distribution," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 60(2), pages 231-247, April.

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