IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/27314.html
   My bibliography  Save this paper

Improving Portfolio Optimization by DCC And DECO GARCH: Evidence from Istanbul Stock Exchange

Author

Listed:
  • Yilmaz, Tolgahan

Abstract

In this paper, the performance of global minimum variance (GMV) portfolios constructed by DCC and DECO-GARCH are compared to that of GMV portfolios constructed by sample covariance and constant correlation methods in terms of reduced volatility. Also, the performance of GMV portfolios are tested against that of equally weighted and cap weighted portfolios. Portfolios are constructed from the stocks listed in Istanbul Stock Exchange 30 index (hereafter, ISE-30). The results show that GMV portfolios constructed by DCC-GARCH outperformed the other portfolios. In addition, the performance of GMV portfolios estimated by DCC and DECO-GARCH methods are improved by extending calibration period from three years to four years and lowering rolling window term from one week to one day, while the performances of other GMV portfolios decrease. It shows the effect of time varying variance and dynamic correlations on portfolio optimization at Turkish stock market.

Suggested Citation

  • Yilmaz, Tolgahan, 2010. "Improving Portfolio Optimization by DCC And DECO GARCH: Evidence from Istanbul Stock Exchange," MPRA Paper 27314, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:27314
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/27314/1/MPRA_paper_27314.pdf
    File Function: original version
    Download Restriction: no

    References listed on IDEAS

    as
    1. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(01), pages 122-150, February.
    2. Louis K.C. Chan & Jason Karceski & Josef Lakonishok, 1999. "On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model," NBER Working Papers 7039, National Bureau of Economic Research, Inc.
    3. Engle, Robert F & Sheppard, Kevin K, 2001. "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH," University of California at San Diego, Economics Working Paper Series qt5s2218dp, Department of Economics, UC San Diego.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    6. Chan, Louis K C & Karceski, Jason & Lakonishok, Josef, 1999. "On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model," Review of Financial Studies, Society for Financial Studies, vol. 12(5), pages 937-974.
    7. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alexakis, Christos & Pappas, Vasileios & Tsikouras, Alexandros, 2017. "Hidden cointegration reveals hidden values in Islamic investments," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 46(C), pages 70-83.
    2. Habrov, Vladimir, 2012. "Optimization of portfolio management based on vector autoregression models and multivariate volatility models," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 28(4), pages 35-62.

    More about this item

    Keywords

    DCC-GARCH; DECO-GARCH; GMV portfolio;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:27314. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter). General contact details of provider: http://edirc.repec.org/data/vfmunde.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.