BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi
Yıl: 2023 Cilt: 73 Sayı: 1 Sayfa Aralığı: 55 - 81 Metin Dili: Türkçe DOI: 10.26650/ISTJECON2022-1229039 İndeks Tarihi: 20-07-2023
BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi
Öz: Son yıllarda sıklıkla gözlemlenen finansal piyasalar arasındaki bağımlılık ve zamana bağlı görülen değişim, modelleme ve fiyatlama açısından önem taşımaktadır. Bu çalışmada, BIST100’de işlem gören bankacılık sektörüne ait hisselerin arasındaki bağımlılık yapısının, zaman serileri ve kurallı asma (R-Vine) kopula modeli ile incelenmesi amaçlanmaktadır. Bankacılık hisselerinden eşit ağırlıklandırılarak oluşturulan portföy için, riske maruz değer (VaR) ve beklenen kayıp (ES) risk ölçütleri hesaplanmış ve geriye dönük yöntemlerle test edilmiştir. Türkiye bankacılık hisseleri özelinde yapılan bu çalışmada, GARCH ve kurallı asma kopula modellerinin birlikte uygulanmasının, geleneksel GARCH tabanlı yaklaşımlara kıyasla VaR ve ES risk ölçütü tahminlerini iyileştirdiğine dair bulgular elde edilmiştir.
Anahtar Kelime: Dependence Analysis of the ISE100 Banking Sector Using Vine Copula
Öz: 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..
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
- Aas, K., Czado, C., Frigessi, A., & Bakken, H. (2009). Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics, 44(2), 182-198.
- Allen, D. E., Ashraf, M. A., McAleer, M., Powell, R. J., & Singh, A. K. (2013). Financial dependence analysis: applications of vine copulas. Statistica Neerlandica, 67(4), 403-435.
- Aloui, R., Aïssa, M. S. B., & Nguyen, D. K. (2013). Conditional dependence structure between oil prices and exchange rates: a copula-GARCH approach. Journal of International Money and Finance, 32, 719-738.
- Bedford, T., & Cooke, R. M. (2001). Probability density decomposition for conditionally dependent random variables modeled by vines. Annals of Mathematics and Artificial intelligence, 32(1), 245- 268.
- Bedford, T., & Cooke, R. M. (2002). Vines a new graphical model for dependent random variables. The Annals of Statistics, 30, 1031–1068. https://doi.org/10.1214/AOS/1031689016
- Binici, M., Köksal, B., & Orman, C. (2013). Stock return comovement and systemic risk in the Turkish banking system. Central Bank Review, 13.
- Brechmann, E., & Czado, C. (2013). Risk management with high-dimensional vine copulas: An analysis of the Euro Stoxx 50. Statistics & Risk Modeling, 30(4), 307-342. https://doi.org/10.1524/ strm.2013.2002
- Christoffersen, P., Hahn, J., & Inoue, A. (2001). Testing and comparing value-at-risk measures. Journal of Empirical Finance, 8(3), 325-342.
- Czado, C. (2019). Analyzing dependent data with vine copulas. Lecture Notes in Statistics, Springer, 222.
- Çamlıca, F., Güneş, D., & Özen, E. (2017). A financial connectedness analysis for Turkey (No. 1719).
- Dißmann, J. F. (2010). Statistical inference for regular vines and application, Technische Universität München, Retrieved from: https://mediatum.ub.tum.de/doc/1079308/file.pdf
- Dißmann, J., Brechmann, E. C., Czado, C., & Kurowicka, D. (2013). Selecting and estimating regular vine copulae and application to financial returns. Computational Statistics & Data Analysis, 59, 52-69.
- Geidosch, M., & Fischer, M. (2016). Application of vine copulas to credit portfolio risk modeling. Journal of Risk and Financial Management, 9(2), 4.
- Hernandez, J.A. (2015). Vine copula modelling of dependence and portfolio optimization with application to mining and energy stock return series from the Australian market (Doctoral dissertation). Retrieved from: https://ro.ecu.edu.au/theses/1693/
- Joe, H. (1996). Families of m-variate distributions with given margins and m(m-1)/2 bivariate dependence parameters. Lecture notes-monograph series, 120-141.
- Joe, H. (1997). Multivariate models and multivariate dependence concepts. CRC press.
- Joe, H., Cooke, R. M., & Kurowicka, D. (2010). Regular vines: generation algorithm and number of equivalence classes. In D. Kurowicka & H. Joe (Eds.), Dependence Modeling: Vine Copula Handbook (pp. 219-231). World Scientific Publishing Co., Singapore.
- Kupiec, P. H. (1995). Techniques for verifying the accuracy of risk measurement models (Vol. 95, No. 24). Division of Research and Statistics, Division of Monetary Affairs, Federal Reserve Board.
- Kurowicka, D., & Cooke, R. M. (2006). Uncertainty analysis with high dimensional dependence modelling. John Wiley & Sons.
- Heinen, A., & Valdesogo, A. (2010). Dynamic d-vine model. In D. Kurowicka & H. Joe (Eds.), Dependence Modeling: Vine Copula Handbook (pp. 329-353). World Scientific Publishing Co., Singapore.
- Li, D. X. (2000). On default correlation: A copula function approach. The Journal of Fixed Income, 9(4), 43-54.
- Maugis, P. A., & Guegan, D. (2010) An econometric study of vine copulas. International Journal of Economics and Finance, 2, 1-13.
- McNeil, A. J., & Frey, R. (2000). Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach. Journal of Empirical Finance, 7(3-4), 271-300.
- Mensah, P. O., & Adam, A. M. (2020). Copula-based assessment of co-movement and tail dependence structure among major trading foreign currencies in Ghana. Risks, 8(2), 55.
- Min, A., & Czado, C. (2010). Bayesian inference for multivariate copulas using pair-copula constructions. Journal of Financial Econometrics, 8(4), 511-546.
- Nagler, T., Schepsmeier, U., Stoeber, J., Brechmann, E. C., Graeler, B., & Erhardt. (2022). T. VineCopula: Statistical Inference of Vine Copulas. R package version 2.4.4 https://CRAN.R-project.org/ package=VineCopula
- Nelsen, R. B. (2007). An introduction to copulas. Springer Science & Business Media.
- Özgür, C., & Sarıkovanlık, V. (2021). An application of Regular Vine copula in portfolio risk forecasting: evidence from Istanbul stock exchange. Quantitative Finance and Economics, 5(3), 452-470.
- Pastpipatkul, P., Yamaka, W., & Sriboonchitta, S. (2018). Portfolio selection with stock, gold and bond in Thailand under vine Copulas functions. In International Econometric Conference of Vietnam (pp. 698-711). Springer, Cham.
- Patton, A. J. (2004). On the out-of-sample importance of skewness and asymmetric dependence for asset allocation. Journal of Financial Econometrics, 2(1), 130-168.
- Patton, A. J. (2008). Copula-based Models for Financial Time Series. OFRC Working Papers Series, Oxford Financial Research Centre.
- Patton, A. (2013). Copula methods for forecasting multivariate time series. Handbook of Economic Forecasting, 2, 899-960.
- Pourkhanali, A., Kim, J. M., Tafakori, L., & Fard, F. A. (2016). Measuring systemic risk using vine-copula. Economic Modelling, 53, 63-74.
- Reboredo, J. C., & Ugolini, A. (2016). Systemic risk of Spanish listed banks: a vine copula CoVaR approach. Spanish Journal of Finance and Accounting/Revista Española de Financiación y Contabilidad, 45(1), 1-31.
- Rockinger, M., & Jondeau, E. (2001). Conditional dependency of financial series: An application of copulas.
- Sklar, M. (1959). Fonctions de repartition an dimensions et leurs marges. Publ. Inst. Statist. univ. Paris, 8, 229-231.
- Şengül, S., & Yılmaz, E. (2019). Measuring Systemic Risks in the Turkish Banking Sector 1. Business and Economics Research Journal, 10(5), 1071-1084.
- Talaslı, I. (2013). Systemic risk analysis of Turkish financial institutions with systemic expected shortfall. Central Bank Review, 13(3), 25-40.
- Xia, X. (2018). Essays on Dependence Modelling with Vine Copulas and its Applications (Doctoral dissertation, University of Leicester).
- Zeevi, A., & Mashal, R. (2002). Beyond correlation: Extreme co-movements between financial assets. Available at SSRN 317122.
- Zhang, J. (2015). Systemic risk measure: CoVaR and Copula (Master Thesis). Retrieved from: https:// edoc.hu-berlin.de/bitstream/handle/18452/14892/zhang.pdf?sequence=1
- Zhang, D., Yan, M., & Tsopanakis, A. (2018). Financial stress relationships among Euro area countries: an R-vine copula approach. The European Journal of Finance, 24(17), 1587-1608.
- Zhang, L., & Singh, V. P. (2019). Copulas and their applications in water resources engineering. Cambridge University Press.
| APA | Yıldırım Külekci B, poyraz g, GUR I, evkaya o (2023). BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi. , 55 - 81. 10.26650/ISTJECON2022-1229039 |
| Chicago | Yıldırım Külekci Bükre,poyraz gulden,GUR Ismail,evkaya ozan BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi. (2023): 55 - 81. 10.26650/ISTJECON2022-1229039 |
| MLA | Yıldırım Külekci Bükre,poyraz gulden,GUR Ismail,evkaya ozan BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi. , 2023, ss.55 - 81. 10.26650/ISTJECON2022-1229039 |
| AMA | Yıldırım Külekci B,poyraz g,GUR I,evkaya o BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi. . 2023; 55 - 81. 10.26650/ISTJECON2022-1229039 |
| Vancouver | Yıldırım Külekci B,poyraz g,GUR I,evkaya o BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi. . 2023; 55 - 81. 10.26650/ISTJECON2022-1229039 |
| IEEE | Yıldırım Külekci B,poyraz g,GUR I,evkaya o "BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi." , ss.55 - 81, 2023. 10.26650/ISTJECON2022-1229039 |
| ISNAD | Yıldırım Külekci, Bükre vd. "BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi". (2023), 55-81. https://doi.org/10.26650/ISTJECON2022-1229039 |
| APA | Yıldırım Külekci B, poyraz g, GUR I, evkaya o (2023). BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi. İstanbul İktisat Dergisi, 73(1), 55 - 81. 10.26650/ISTJECON2022-1229039 |
| Chicago | Yıldırım Külekci Bükre,poyraz gulden,GUR Ismail,evkaya ozan BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi. İstanbul İktisat Dergisi 73, no.1 (2023): 55 - 81. 10.26650/ISTJECON2022-1229039 |
| MLA | Yıldırım Külekci Bükre,poyraz gulden,GUR Ismail,evkaya ozan BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi. İstanbul İktisat Dergisi, vol.73, no.1, 2023, ss.55 - 81. 10.26650/ISTJECON2022-1229039 |
| AMA | Yıldırım Külekci B,poyraz g,GUR I,evkaya o BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi. İstanbul İktisat Dergisi. 2023; 73(1): 55 - 81. 10.26650/ISTJECON2022-1229039 |
| Vancouver | Yıldırım Külekci B,poyraz g,GUR I,evkaya o BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi. İstanbul İktisat Dergisi. 2023; 73(1): 55 - 81. 10.26650/ISTJECON2022-1229039 |
| IEEE | Yıldırım Külekci B,poyraz g,GUR I,evkaya o "BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi." İstanbul İktisat Dergisi, 73, ss.55 - 81, 2023. 10.26650/ISTJECON2022-1229039 |
| ISNAD | Yıldırım Külekci, Bükre vd. "BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi". İstanbul İktisat Dergisi 73/1 (2023), 55-81. https://doi.org/10.26650/ISTJECON2022-1229039 |
