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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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  • Kurt Brannas & Niklas Nordman, 2003. "Conditional skewness modelling for stock returns," Applied Economics Letters, Taylor & Francis Journals, vol. 10(11), pages 725-728.
    • Handle: RePEc:taf:apeclt:v:10:y:2003:i:11:p:725-728
    DOI: 10.1080/1350485032000139015
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    Cited by:

    1. Dark Jonathan Graeme, 2010. "Estimation of Time Varying Skewness and Kurtosis with an Application to Value at Risk," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(2), pages 1-50, March.
    2. Wei Kuang, 2021. "Dynamic VaR forecasts using conditional Pearson type IV distribution," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 500-511, April.
    3. W. D. Walls, 2005. "Modelling heavy tails and skewness in film returns," Applied Financial Economics, Taylor & Francis Journals, vol. 15(17), pages 1181-1188.
    4. Eugenia Sanin, María & Violante, Francesco & Mansanet-Bataller, María, 2015. "Understanding volatility dynamics in the EU-ETS market," Energy Policy, Elsevier, vol. 82(C), pages 321-331.
    5. N. Bhattacharya & T. A. Garrett, 2008. "Why people choose negative expected return assets - an empirical examination of a utility theoretic explanation," Applied Economics, Taylor & Francis Journals, vol. 40(1), pages 27-34.
    6. Lin, Chu-Hsiung & Changchien, Chang-Cheng & Kao, Tzu-Chuan & Kao, Wei-Shun, 2014. "High-order moments and extreme value approach for value-at-risk," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 421-434.
    7. Sylvia J. Soltyk & Felix Chan, 2023. "Modeling time‐varying higher‐order conditional moments: A survey," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 33-57, February.
    8. 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.
    9. Changli He & Annastiina Silvennoinen & Timo Teräsvirta, 2008. "Parameterizing Unconditional Skewness in Models for Financial Time Series," Journal of Financial Econometrics, Oxford University Press, vol. 6(2), pages 208-230, Spring.
    10. F. Pizzutilo, 2012. "The behaviour of the distributions of stock returns: an analysis of the European market using the Pearson system of continuous probability distributions," Applied Financial Economics, Taylor & Francis Journals, vol. 22(20), pages 1743-1752, October.
    11. 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.
    12. Bruno Feunou & Mohammad R. Jahan-Parvar & Roméo Tédongap, 2016. "Which parametric model for conditional skewness?," The European Journal of Finance, Taylor & Francis Journals, vol. 22(13), pages 1237-1271, October.
    13. Sree Vinutha Venkataraman & S. V. D. Nageswara Rao, 2016. "Estimation of dynamic VaR using JSU and PIV distributions," Risk Management, Palgrave Macmillan, vol. 18(2), pages 111-134, August.
    14. Sung Y. Park & Sang Hyuck Kim, 2016. "Determinants of systematic risk in the US Restaurant industry," Tourism Economics, , vol. 22(3), pages 621-628, June.
    15. Brännäs, Kurt & Soultanaeva, Albina, 2006. "Influence of News in Moscow and New York on Returns and Risks on Baltic State Stock Indices," Umeå Economic Studies 696, Umeå University, Department of Economics.
    16. Bouri, Elie & Jalkh, Naji, 2023. "Spillovers of joint volatility-skewness-kurtosis of major cryptocurrencies and their determinants," International Review of Financial Analysis, Elsevier, vol. 90(C).
    17. Brännäs, Kurt, 2003. "Temporal Aggregation of the Returns of a Stock Index Series," Umeå Economic Studies 614, Umeå University, Department of Economics.
    18. Stavroyiannis, S. & Makris, I. & Nikolaidis, V. & Zarangas, L., 2012. "Econometric modeling and value-at-risk using the Pearson type-IV distribution," International Review of Financial Analysis, Elsevier, vol. 22(C), pages 10-17.
    19. 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.
    20. Markus Haas, 2004. "Mixed Normal Conditional Heteroskedasticity," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 211-250.
    21. Doaa Akl Ahmed & Mamdouh M. Abdelsalam, 2015. "Modelling the Density of Egyptian Quarterly CPI Inflation," Working Papers 936, Economic Research Forum, revised Aug 2015.
    22. David Ashton & Mark Tippett, 2006. "Mean Reversion and the Distribution of United Kingdom Stock Index Returns," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 33(9‐10), pages 1586-1609, November.
    23. John Douglas (J.D.) Opdyke, 2007. "Comparing Sharpe ratios: So where are the p-values?," Journal of Asset Management, Palgrave Macmillan, vol. 8(5), pages 308-336, December.
    24. Stavros Stavroyiannis, 2016. "Value-at-Risk and backtesting with the APARCH model and the standardized Pearson type IV distribution," Papers 1602.05749, arXiv.org.
    25. 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.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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