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Robust Portfolio Optimization Based on Semi-Parametric ARMA-TGARCH-EVT Model with Mixed Copula Using WCVaR

Author

Listed:
  • Xue Deng

    (South China University of Technology)

  • Ying Liang

    (South China University of Technology)

Abstract

Portfolio returns generally follow multivariate distribution, whose effectiveness depends not only on the correct estimation of marginal distributions, but also on the accurate capture of the interdependent structure among them. To effectively estimate the marginal distribution and improve the accuracy, we present a hybrid ARMA-TGARCH-EVT model, which considers the leverage effect, thick tail and heteroscedasticity of the financial asset return series. This model utilizes the extreme value theory to process the tail data and uses the kernel regression estimation to process the intermediate data to make the marginal distribution smooth, natural and regular. Furthermore, a novel semi-parametric ARMA-TGARCH-EVT- Copula portfolio model is proposed to achieve the robustness of minimizing worst-case conditional value-at-risk (WCVaR). In the model, a mixed copula set is presented by t-copula and Archimedean copula to cover the wide joint dependence among logarithmic daily returns. To verify the effectiveness and practicality of our proposed model, a static numerical example and a dynamic portfolio based on the historical index data of two stock crash periods are given. The results show that the new model is superior in terms of daily average logarithmic return, cumulative logarithmic return and sharp ratio.

Suggested Citation

  • Xue Deng & Ying Liang, 2023. "Robust Portfolio Optimization Based on Semi-Parametric ARMA-TGARCH-EVT Model with Mixed Copula Using WCVaR," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 267-294, January.
    • Handle: RePEc:kap:compec:v:61:y:2023:i:1:d:10.1007_s10614-021-10207-5
    DOI: 10.1007/s10614-021-10207-5
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    References listed on IDEAS

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    1. Lorán Chollete & Andréas Heinen & Alfonso Valdesogo, 2009. "Modeling International Financial Returns with a Multivariate Regime-switching Copula," Journal of Financial Econometrics, Oxford University Press, vol. 7(4), pages 437-480, Fall.
    2. Bali, Turan G. & Neftci, Salih N., 2003. "Disturbing extremal behavior of spot rate dynamics," Journal of Empirical Finance, Elsevier, vol. 10(4), pages 455-477, September.
    3. Sahamkhadam, Maziar & Stephan, Andreas & Östermark, Ralf, 2018. "Portfolio optimization based on GARCH-EVT-Copula forecasting models," International Journal of Forecasting, Elsevier, vol. 34(3), pages 497-506.
    4. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    5. Kakouris, Iakovos & Rustem, Berç, 2014. "Robust portfolio optimization with copulas," European Journal of Operational Research, Elsevier, vol. 235(1), pages 28-37.
    6. Laurent El Ghaoui & Maksim Oks & Francois Oustry, 2003. "Worst-Case Value-At-Risk and Robust Portfolio Optimization: A Conic Programming Approach," Operations Research, INFORMS, vol. 51(4), pages 543-556, August.
    7. Ang, Andrew & Chen, Joseph, 2002. "Asymmetric correlations of equity portfolios," Journal of Financial Economics, Elsevier, vol. 63(3), pages 443-494, March.
    8. Jondeau, Eric & Rockinger, Michael, 2006. "The Copula-GARCH model of conditional dependencies: An international stock market application," Journal of International Money and Finance, Elsevier, vol. 25(5), pages 827-853, August.
    9. Han, Yingwei & Li, Ping & Xia, Yong, 2017. "Dynamic robust portfolio selection with copulas," Finance Research Letters, Elsevier, vol. 21(C), pages 190-200.
    10. Shushang Zhu & Masao Fukushima, 2009. "Worst-Case Conditional Value-at-Risk with Application to Robust Portfolio Management," Operations Research, INFORMS, vol. 57(5), pages 1155-1168, October.
    11. A. L. Soyster, 1973. "Technical Note—Convex Programming with Set-Inclusive Constraints and Applications to Inexact Linear Programming," Operations Research, INFORMS, vol. 21(5), pages 1154-1157, October.
    12. Ling Hu, 2006. "Dependence patterns across financial markets: a mixed copula approach," Applied Financial Economics, Taylor & Francis Journals, vol. 16(10), pages 717-729.
    13. A. Ben-Tal & A. Nemirovski, 1998. "Robust Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 769-805, November.
    14. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
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