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Dependence Analysis of the ISE100 Banking Sector Using Vine Copula

Author

Listed:
  • Bukre Yildirim Kulekci

    (Technical University of Kaiserslautern, Department of Mathematics, Kaiserslautern, Germany)

  • Gulden Poyraz

    (Bandirma Onyedi Eylul University, Foreign Department of Commerce, Balikesir, Turkiye)

  • Ismail Gur

    (Dr. Student, Hacettepe University, Actuary Department of Sciences, Ankara, Turkiye)

  • Ozan Evkaya

    (Edinburgh, University of Edinburgh, Department of Mathematics, Edinburgh, England)

Abstract

The frequently observed time-varying trends and dependence in recent years within financial markets have been essential for modeling and pricing. This study aims to analyze the dependence structure of banking sector stocks traded on the ISE100 index using time series and regular vine (R-vine) copula models. The study calculates the risk measures of value-at-risk (VaR) and expected shortfall (ES) and tests with backtesting methods for the portfolio that are constructed by equally weighting the banking stocks. This study’s findings on banking stocks specifically indicate that the application of the R-vine copula combined with the generalized auto-regressive conditional heteroskedasticity (GARCH) model improved the VaR and ES estimates compared to traditional GARCH-based approaches..

Suggested Citation

  • Bukre Yildirim Kulekci & Gulden Poyraz & Ismail Gur & Ozan Evkaya, 2023. "Dependence Analysis of the ISE100 Banking Sector Using Vine Copula," Istanbul Journal of Economics-Istanbul Iktisat Dergisi, Istanbul University, Faculty of Economics, vol. 73(73-1), pages 55-81, June.
    • Handle: RePEc:ist:journl:v:73:y:2023:i:1:p:55-81
    DOI: 10.26650/ISTJECON2022-1229039
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    References listed on IDEAS

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    More about this item

    Keywords

    Vine copula; Financial market; Risk measures JEL Classification: G32 ; C32 ; C58;
    All these keywords.

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • 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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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