SavedAdd your journal

Journals / Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics / 2026 / Volume: 64 Issue: 1

MODELING DEPENDENT FINANCIAL ASSETS BY DYNAMIC COPULA AND PORTFOLIO OPTIMIZATION BASED ON CVAR

Sibel Açık Kemaloglu
Emel Kızılok Kara
Pages
—
Publication Date
2015-02-01
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The portfolio optimizationbased on CVaR is made for the portfolio created from the specified copula model

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper is concerned with the statistical modeling of the dependence structure of multivariate financial data using copula.
📊
Key Findings & Data:Since financial data is greatly affected by the economic factors, it often varies according to the time.
💡
Conclusion & Impact:The portfolio optimizationbased on CVaR is made for the portfolio created from the specified copula model
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

This paper is concerned with the statistical modeling of the dependence structure of multivariate financial data using copula. Since financial data is greatly affected by the economic factors, it often varies according to the time. Therefore, dynamic copula model is used that takes into account the time-varying. In addition, portfolio optimization based on Mean-CVaR modelis applied with Monte Carlo simulation. As an application, a portfolio withfour different Indexes is constructed from the Turkish financial markets. Themarginal distributions of assets in the portfolio are estimated and parameterestimates are given for the different copula models. The portfolio optimizationbased on CVaR is made for the portfolio created from the specified copula model

🔖

Cite This Article

Kemaloglu, S. A., Kara, E. K. (2015). MODELING DEPENDENT FINANCIAL ASSETS BY DYNAMIC COPULA AND PORTFOLIO OPTIMIZATION BASED ON CVAR. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, Volume: 64 Issue: 1. https://doi.org/10.1501/Commua1_0000000723

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (19)

  1. Markowitz, H. (1952) ‘Portfolio Selection. Journal of Finance’, vol. 25, pp. 77-91.
  2. Rockafellar, R. T. and Ursayev, S. (2002) ‘Optimization of conditional value at risk’, Journal of Risk, vol. 2, pp. 21-40.
  3. Sklar, A. (1959) ‘Functions De Repartition An Dimensions At Leurs Marges’, Publications
  4. De L’ınstitut De Statistique De L’université De Paris, vol. 8, pp. 229-231. He, H. and Li, P. (2011) ‘Dynamic Asset Allocation Based on Copula and CVaR’, IEEE.
  5. Cherubini, U., Luciano, E. and Vecchiato, W. (2004) Copula Methods in Finance. John Wiley and Sons, New York.
  6. Joe, H.and Xu, J.J. (1996) The estimation method of inference functions for margins for multivariate models. Technical Report 166. Department of Statistics, University of British Columbia.
  7. Nelsen, R.B. (2006) An Introduction to Copulas, (second ed) Springer, New York.
  8. Emrechts, P., McNeil, A. and Straumann D. (2002). ‘Correlation and dependence in risk management: properties and pitfalls’, In Risk Management Value at Risk and Beyond (Edited by M. Dempster), pp. 176-223, Cambridge University Press.
  9. Rockinger, M. and Jondeau, E. (2006) ‘The Coplua-GARCH model of conditional dependen- cies: An international stock market application’, Journal of International Money and Finance, vol. 25(3), pp. 827-853.
  10. Wei, Y. and Zhang, S. (2007) ‘Multivariate Copula-GARCH Model and Its Applications in
  11. Financial Risk Analysis [J].’, Application of Statistics and Management,vol. 3, pp. 008. Ozun, A., Cifter, A. (2007) ‘Portfolio value-at-risk with time-varying copula: Evidence from the Americans’, Marmara University. MPRA Paper No. 2711.
  12. Huang, J. J., Lee, K. J., Liang, H. and Lin, W.F. (2009) ‘Estimating value at risk of portfolio by conditional copula-GARCH method’, Insurance: Mathematics and Economics, vol. 45, pp. 315- 324.
  13. Wu, Z.X., Chen, M. and Ye, W.Y. (2006) ‘Risk Analysis of Portfolio by Copula-GARCH’
  14. Journal of Systems Engineering Theory and Practice, vol. 2(8), pp. 45–52. Wang, Z. R., Chen, X. H., Jin, Y. B., Zhou, Y.J. (2010) ‘Estimating risk of foreign exchange portfolio: Using VaR and CVaR based on GARCH-EVT-Copula model’, Physica A, vol. 389, pp. 4918-4928.
  15. Patton, A.J. (2006) ‘Modelling asymmetric exchange rate dependence’, International Eco- nomic Review, vol. 47, pp. 527-556.
  16. Bollerslev, T. (1986) ‘Generalized autoregressive conditional heteroskedasticity’, Journal of econometrics, vol. 31 (3), pp. 307-327.
  17. Akaike, H. (1974) ‘A new look at the statistical model identi…cation’, Automatic Control
  18. IEEE Transactions on, vol. 19, pp. 716–723. Schwarz, G. (1978) ‘Estimating the dimension of a model’, Annals of Statistics, vol. 6, pp. –464.
  19. Cryer, C.D. and Chan, K.S. (2008). Time Series Analysis with Applications in R, (second ed.) Springer, New York. http://borsaistanbul.com/veriler/verileralt/hisse-senetleri-piyasasi-verileri/endeks-verileri 08.2013]. http://evds.tcmb.gov.tr/cbt.html [02.08.2013].