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Portfolio Optimization of Stock, Oil and Gold Returns: A Mixed Copula-Based Approach

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Structural Changes and their Econometric Modeling (TES 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 808))

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Abstract

This paper applies the concept of copula based GARCH to the problem of finding the efficient frontier associated with the risk-return portfolio optimization model. Various families of the mixed copula structures, both Elliptical and Archimedean copulas, are proposed to deal with the complicated dependence structure in the financial portfolio. We then apply these mixed copula models to investigate the portfolio risk composed of stock, oil and gold returns and also compare the mixed copula with the conventional copula in order to show the better performance of the mixed copula in terms of the lowest AIC and BIC. The empirical results show that, compared with traditional methods, the mixed Gaussian and Student-t copula model describes the dependence structure of the portfolio return series more successfully. Finally, we apply the best fitted model to do the Monte Carlo simulation for constructing the efficient frontier and find the optimal investment using the minimized variance and tangency portfolio approaches.

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References

  1. Agarwal, A., Hazan, E., Kale, S., Schapire, R.E.: Algorithms for portfolio management based on the Newton method. In: Proceedings of the 23rd International Conference on Machine learning, pp. 9-16. ACM, June, 2006

    Google Scholar 

  2. Ang, A., Chen, J.: Asymmetric correlations of equity portfolios. J. Financ. Econ. 63(3), 443–494 (2002)

    Article  Google Scholar 

  3. Arratia, A.: Computational finance. An Introductory Course with R, Atlantis Studies in Computational Finance and Financial Engineering, p. 1 (2014)

    Book  Google Scholar 

  4. Beine, M., Cosma, A., Vermeulen, R.: The dark side of global integration: increasing tail dependence. J. Bank. Financ. 34(1), 184–192 (2010)

    Article  Google Scholar 

  5. Braun, V.: Dynamic copulas for finance: an application to portfolio risk calculation, Vol. 166 (2011). BoD-Books on Demand

    Google Scholar 

  6. Brooks, C., Burke, S.P., Heravi, S., Persand, G.: Autoregressive conditional kurtosis. J. Financ. Econ. 3(3), 399–421 (2005)

    Google Scholar 

  7. Dias, A., Embrechts, P.: Modeling exchange rate dependence dynamics at different time horizons. J. Int. Money Financ. 29(8), 1687–1705 (2010)

    Article  Google Scholar 

  8. Embrechts, P., McNeil, A., Straumann, D.: Correlation: Pitfalls and alternatives a short. RISK Magazine, 69-71(1999)

    Google Scholar 

  9. Embrechts, P., McNeil, A., Straumann, D.: Correlation and dependence in risk management: properties and pitfalls. Risk Management: Value at Risk and Beyond, pp. 176–223. Cambridge University Press, Cambridge (2002)

    Book  Google Scholar 

  10. Forbes, K.J., Rigobon, R.: No contagion, only interdependence: measuring stock market comovements. J. Financ. 57(5), 2223–2261 (2002)

    Article  Google Scholar 

  11. Goodman, S.N.: Toward evidence-based medical statistics. 1: the P value fallacy. Ann. Intern. Med. 130(12), 995–1004 (1999)

    Article  Google Scholar 

  12. Hartmann, P., Straetmans, S., Vries, C.D.: Asset market linkages in crisis periods. Rev. Econ. Stat. 86(1), 313–326 (2004)

    Article  Google Scholar 

  13. Harvey, C.R., Siddique, A.: Autoregressive conditional skewness. J. Financ. Quant. Anal. 34(4), 465–487 (1999)

    Article  Google Scholar 

  14. Held, L., Ott, M.: How the maximal evidence of p-values against point null hypotheses depends on sample size. Am. Stat. 70(4), 335–341 (2016)

    Article  MathSciNet  Google Scholar 

  15. Hu, L.: Dependence patterns across financial markets: a mixed copula approach. Appl. Financ. Econ. 16(10), 717–729 (2006)

    Article  Google Scholar 

  16. Huang, J.J., Lee, K.J., Liang, H., Lin, W.F.: Estimating value at risk of portfolio by conditional copula-GARCH method. Insur. Math. Econ. 45(3), 315–324 (2009)

    Article  MathSciNet  Google Scholar 

  17. Jondeau, E., Rockinger, M.: Conditional volatility, skewness, and kurtosis: existence, persistence, and comovements. J. Econ. Dyn. Control 27(10), 1699–1737 (2003)

    Article  MathSciNet  Google Scholar 

  18. Lu, J., Tian, W.J., Zhang, P.: The extreme value copulas analysis of the risk dependence for the foreign exchange data. In: 4th International Conference on Wireless Communications, Networking and Mobile Computing. WiCOM 2008, pp. 1-6. IEEE, October 2008

    Google Scholar 

  19. Markowitz, H.: Portfolio selection. J. Financ. 7(1), 77–91 (1952)

    Google Scholar 

  20. Markowitz, H., Selection, P.: Efficient Diversification of Investments. 12, 26–31. Wiley, Chichester (1959)

    Google Scholar 

  21. Maneejuk, P., Yamaka, W., Sriboonchitta, S.: Mixed-Copulas approach in Examining the Relationship Between Oil Prices and ASEAN’s Stock Markets. In: International Econometric Conference of Vietnam, pp. 531-541. Springer, Cham, January 2018

    Google Scholar 

  22. Nelsen, R.B.: An Introduction to Copulas, Lecture Notes in Statistics, vol. 139 (1999)

    Chapter  Google Scholar 

  23. Nelsen, R.B.: An Introduction to Copulas. Springer, New York (2007)

    Google Scholar 

  24. Nguyen, C., Bhatti, M.I.: Copula model dependency between oil prices and stock markets: evidence from China and Vietnam. J. Int. Financ. Mark. Inst. Money 22(4), 758–773 (2012)

    Article  Google Scholar 

  25. Nguyen, C., Bhatti, M.I., Komorníková, M., Komorník, J.: Gold price and stock markets nexus under mixed-copulas. Econ. Model. 58, 283–292 (2016)

    Article  Google Scholar 

  26. Pastpipatkul, P., Maneejuk, P., Sriboonchitt, S.: The best copula modeling of dependence structure among Gold, Oil Prices, and US Currency. In: International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, pp. 493-507. Springer, Cham, November, 2016

    Google Scholar 

  27. Poon, S. H., Rockinger, M., Tawn, J.: Modelling extreme-value dependence in international stock markets. Statistica Sinica, 929-953 (2003)

    Google Scholar 

  28. Ruenzi, S., Weigert, F., Chabi-Yo, F.: Crash sensitivity and the cross-section of expected stock returns. Working papers on finance/University of St. Gallen, School of Finance Research Paper, 13 (2015)

    Google Scholar 

  29. Schepsmeier, U., Stöber, J.: Derivatives and fisher information of bivariate copulas. Stat. Pap. 55(2), 525–542 (2014)

    Article  MathSciNet  Google Scholar 

  30. Sklar, M.: Fonctions de repartition an dimensions et leurs marges. Publ. Inst. Statist. Univ. Paris 8, 229–231 (1959)

    MathSciNet  MATH  Google Scholar 

  31. Weiss, G.N.: Copula parameter estimation: numerical considerations and implications for risk management. J. Risk 13(1), 17–53 (2010)

    Article  Google Scholar 

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Acknowledgements

The authors are grateful to Puay Ungphakorn Centre of Excellence in Econometrics, Faculty of Economics, Chiang Mai University for the financial support.

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Authors and Affiliations

  1. Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Chiang Mai, 50200, Thailand

    Sukrit Thongkairat & Woraphon Yamaka

  2. International College of Digital Innovation, Chiang Mai University, Chiang Mai, 50200, Thailand

    Sukrit Thongkairat & Nopasit Chakpitak

Authors
  1. Sukrit Thongkairat
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  2. Woraphon Yamaka
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  3. Nopasit Chakpitak
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Corresponding author

Correspondence to Sukrit Thongkairat .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

  2. Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand

    Songsak Sriboonchitta

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Thongkairat, S., Yamaka, W., Chakpitak, N. (2019). Portfolio Optimization of Stock, Oil and Gold Returns: A Mixed Copula-Based Approach. In: Kreinovich, V., Sriboonchitta, S. (eds) Structural Changes and their Econometric Modeling. TES 2019. Studies in Computational Intelligence, vol 808. Springer, Cham. https://doi.org/10.1007/978-3-030-04263-9_37

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