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Do portfolio investors need to consider the asymmetry of returns on the Russian stock market?

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  • Lakshina, Valeriya

Abstract

This paper, following (Ghalanos, 2012), uses the simple method of embedding skewness in asset allocation based on the Taylor expansion of the investor utility function up to the third term and maximizing it by portfolio weights. This approach also enables us to consider investor risk aversion. Time-dependent multivariate asset moments are obtained via the GO-GARCH volatility model with a normal-inverse Gaussian distribution for the error term. By means of parma package (Ghalanos & Pfaff, 2016) we explore the performance of the usual 2 moment utility and its 3 moment counterpart for a portfolio consisted of twenty assets traded on the Russian stock market. The results demonstrate that the 3 moment utility significantly outperforms the 2 moment utility by SD, MAD and CVaR for low levels of absolute risk aversion and by portfolio returns and investor utility level during the whole forecast period.

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  • Lakshina, Valeriya, 2020. "Do portfolio investors need to consider the asymmetry of returns on the Russian stock market?," The Journal of Economic Asymmetries, Elsevier, vol. 21(C).
    • Handle: RePEc:eee:joecas:v:21:y:2020:i:c:s170349491930091x
    DOI: 10.1016/j.jeca.2019.e00152
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    1. Walter Briec & Kristiaan Kerstens & Octave Jokung, 2007. "Mean-Variance-Skewness Portfolio Performance Gauging: A General Shortage Function and Dual Approach," Management Science, INFORMS, vol. 53(1), pages 135-149, January.
    2. Mencía, Javier & Sentana, Enrique, 2009. "Multivariate location-scale mixtures of normals and mean-variance-skewness portfolio allocation," Journal of Econometrics, Elsevier, vol. 153(2), pages 105-121, December.
    3. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    4. Weide, R. van der, 2002. "Generalized Orthogonal GARCH. A Multivariate GARCH model," CeNDEF Working Papers 02-02, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    5. Eric Jondeau & Michael Rockinger, 2006. "Optimal Portfolio Allocation under Higher Moments," European Financial Management, European Financial Management Association, vol. 12(1), pages 29-55, January.
    6. Kerstens, Kristiaan & Mounir, Amine & Van de Woestyne, Ignace, 2011. "Geometric representation of the mean-variance-skewness portfolio frontier based upon the shortage function," European Journal of Operational Research, Elsevier, vol. 210(1), pages 81-94, April.
    7. Luis Zuluaga & Samuel Cox, 2010. "Improving Skewness of Mean-Variance Portfolios," North American Actuarial Journal, Taylor & Francis Journals, vol. 14(1), pages 59-67.
    8. de Athayde, Gustavo M. & Flores, Renato Jr., 2004. "Finding a maximum skewness portfolio--a general solution to three-moments portfolio choice," Journal of Economic Dynamics and Control, Elsevier, vol. 28(7), pages 1335-1352, April.
    9. Fogler, H. Russell & Groves, William A. & Richardson, James G., 1977. "Bond Portfolio Strategies, Returns, and Skewness: A Note," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 12(1), pages 127-140, March.
    10. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    11. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    12. Akbar, Muhammad & Nguyen, Thuy Thu, 2016. "The explanatory power of higher moment capital asset pricing model in the Karachi stock exchange," Research in International Business and Finance, Elsevier, vol. 36(C), pages 241-253.
    13. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    14. Roy van der Weide, 2002. "GO-GARCH: a multivariate generalized orthogonal GARCH model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 549-564.
    15. Piotr Jaworski & Marcin Pitera, 2014. "On spatial contagion and multivariate GARCH models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 30(3), pages 303-327, May.
    16. Li, Xiang & Qin, Zhongfeng & Kar, Samarjit, 2010. "Mean-variance-skewness model for portfolio selection with fuzzy returns," European Journal of Operational Research, Elsevier, vol. 202(1), pages 239-247, April.
    17. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    18. John R. Birge & Luis Chavez-Bedoya, 2016. "Portfolio optimization under a generalized hyperbolic skewed t distribution and exponential utility," Quantitative Finance, Taylor & Francis Journals, vol. 16(7), pages 1019-1036, July.
    19. Kane, Alex, 1982. "Skewness Preference and Portfolio Choice," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 17(1), pages 15-25, March.
    20. Campbell Harvey & John Liechty & Merrill Liechty & Peter Muller, 2010. "Portfolio selection with higher moments," Quantitative Finance, Taylor & Francis Journals, vol. 10(5), pages 469-485.
    21. Kim, Woo Chang & Fabozzi, Frank J. & Cheridito, Patrick & Fox, Charles, 2014. "Controlling portfolio skewness and kurtosis without directly optimizing third and fourth moments," Economics Letters, Elsevier, vol. 122(2), pages 154-158.
    22. Ole E. Barndorff-Nielsen, 1997. "Processes of normal inverse Gaussian type," Finance and Stochastics, Springer, vol. 2(1), pages 41-68.
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    2. Ahmed, Bouteska, 2020. "Understanding the impact of investor sentiment on the price formation process: A review of the conduct of American stock markets," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).

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    More about this item

    Keywords

    Portfolio optimization; Asymmetry of returns; Risk aversion; GO-GARCH; Normal-inverse Gaussian distribution; Utility approach;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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