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BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi

Yıl 2023, Cilt: 73 Sayı: 1, 55 - 82, 26.06.2023
https://doi.org/10.26650/ISTJECON2022-1229039

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.

Kaynakça

  • Aas, K., Czado, C., Frigessi, A., & Bakken, H. (2009). Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics, 44(2), 182-198. google scholar
  • 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. google scholar
  • Aloui, R., A'ı'ssa, 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. google scholar
  • 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), 245268. google scholar
  • 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/A0S/1031689016 google scholar
  • Binici, M., Köksal, B., & Orman, C. (2013). Stock return comovement and systemic risk in the Turkish banking system. Central Bank Review, 13. google scholar
  • 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 google scholar
  • Christoffersen, P., Hahn, J., & Inoue, A. (2001). Testing and comparing value-at-risk measures. Journal of EmpiricalFinance, 8(3), 325-342. google scholar
  • Czado, C. (2019). Analyzing dependent data with vine copulas. Lecture Notes in Statistics, Springer, 222. google scholar
  • Çamlıca, F., Güneş, D., & Özen, E. (2017). A financial connectedness analysis for Turkey (No. 1719). google scholar
  • DiGmann, J. F. (2010). Statistical inference for regular vines and application, Technische Universitat München, Retrieved from: https://mediatum.ub.tum.de/doc/1079308/file.pdf google scholar
  • DiGmann, 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. google scholar
  • Geidosch, M., & Fischer, M. (2016). Application of vine copulas to credit portfolio risk modeling. Journal of Risk and Financial Management, 9(2), 4. google scholar
  • 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/ google scholar
  • 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. google scholar
  • Joe, H. (1997). Multivariate models and multivariate dependence concepts. CRC press. google scholar
  • 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. google scholar
  • 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. google scholar
  • Kurowicka, D., & Cooke, R. M. (2006). Uncertainty analysis with high dimensional dependence modelling. John Wiley & Sons. google scholar
  • 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. google scholar
  • Li, D. X. (2000). On default correlation: A copula function approach. The Journal of Fixed Income, 9(4), 43-54. google scholar
  • Maugis, P. A., & Guegan, D. (2010) An econometric study of vine copulas. International Journal of Economics and Finance, 2, 1-13. google scholar
  • 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. google scholar
  • 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. google scholar
  • Min, A., & Czado, C. (2010). Bayesian inference for multivariate copulas using pair-copula constructions. Journal of Financial Econometrics, 8(4), 511-546. google scholar
  • 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 google scholar
  • Nelsen, R. B. (2007). An introduction to copulas. Springer Science & Business Media. google scholar
  • 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. google scholar
  • 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. google scholar
  • 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. google scholar
  • Patton, A. J. (2008). Copula-based Models for Financial Time Series. OFRC Working Papers Series, Oxford Financial Research Centre. google scholar
  • Patton, A. (2013). Copula methods for forecasting multivariate time series. Handbook of Economic Forecasting, 2, 899-960. google scholar
  • Pourkhanali, A., Kim, J. M., Tafakori, L., & Fard, F. A. (2016). Measuring systemic risk using vine-copula. Economic Modelling, 53, 63-74. google scholar
  • Reboredo, J. C., & Ugolini, A. (2016). Systemic risk of Spanish listed banks: a vine copula CoVaR approach. Spanish Journal of Finance and Accounting/Revista Espanola de Financiacion y Contabilidad, 45(1), 1-31. google scholar
  • Rockinger, M., & Jondeau, E. (2001). Conditional dependency of financial series: An application of copulas. google scholar
  • Sklar, M. (1959). Fonctions de repartition an dimensions et leurs marges. Publ. Inst. Statist. univ. Paris, 8, 229-231. google scholar
  • Şengül, S., & Yılmaz, E. (2019). Measuring Systemic Risks in the Turkish Banking Sector 1. Business and Economics Research Journal, 10(5), 1071-1084. google scholar
  • Talaslı, I. (2013). Systemic risk analysis of Turkish financial institutions with systemic expected shortfall. Central Bank Review, 13(3), 25-40. google scholar
  • Xia, X. (2018). Essays on Dependence Modelling with Vine Copulas and its Applications (Doctoral dissertation, University of Leicester). google scholar
  • Zeevi, A., & Mashal, R. (2002). Beyond correlation: Extreme co-movements between financial assets. Available at SSRN 317122. google scholar
  • 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 google scholar
  • 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. google scholar
  • Zhang, L., & Singh, V. P. (2019). Copulas and their applications in water resources engineering. Cambridge University Press. google scholar

Dependence Analysis of the ISE100 Banking Sector Using Vine Copula

Yıl 2023, Cilt: 73 Sayı: 1, 55 - 82, 26.06.2023
https://doi.org/10.26650/ISTJECON2022-1229039

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.

Kaynakça

  • Aas, K., Czado, C., Frigessi, A., & Bakken, H. (2009). Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics, 44(2), 182-198. google scholar
  • 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. google scholar
  • Aloui, R., A'ı'ssa, 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. google scholar
  • 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), 245268. google scholar
  • 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/A0S/1031689016 google scholar
  • Binici, M., Köksal, B., & Orman, C. (2013). Stock return comovement and systemic risk in the Turkish banking system. Central Bank Review, 13. google scholar
  • 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 google scholar
  • Christoffersen, P., Hahn, J., & Inoue, A. (2001). Testing and comparing value-at-risk measures. Journal of EmpiricalFinance, 8(3), 325-342. google scholar
  • Czado, C. (2019). Analyzing dependent data with vine copulas. Lecture Notes in Statistics, Springer, 222. google scholar
  • Çamlıca, F., Güneş, D., & Özen, E. (2017). A financial connectedness analysis for Turkey (No. 1719). google scholar
  • DiGmann, J. F. (2010). Statistical inference for regular vines and application, Technische Universitat München, Retrieved from: https://mediatum.ub.tum.de/doc/1079308/file.pdf google scholar
  • DiGmann, 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. google scholar
  • Geidosch, M., & Fischer, M. (2016). Application of vine copulas to credit portfolio risk modeling. Journal of Risk and Financial Management, 9(2), 4. google scholar
  • 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/ google scholar
  • 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. google scholar
  • Joe, H. (1997). Multivariate models and multivariate dependence concepts. CRC press. google scholar
  • 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. google scholar
  • 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. google scholar
  • Kurowicka, D., & Cooke, R. M. (2006). Uncertainty analysis with high dimensional dependence modelling. John Wiley & Sons. google scholar
  • 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. google scholar
  • Li, D. X. (2000). On default correlation: A copula function approach. The Journal of Fixed Income, 9(4), 43-54. google scholar
  • Maugis, P. A., & Guegan, D. (2010) An econometric study of vine copulas. International Journal of Economics and Finance, 2, 1-13. google scholar
  • 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. google scholar
  • 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. google scholar
  • Min, A., & Czado, C. (2010). Bayesian inference for multivariate copulas using pair-copula constructions. Journal of Financial Econometrics, 8(4), 511-546. google scholar
  • 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 google scholar
  • Nelsen, R. B. (2007). An introduction to copulas. Springer Science & Business Media. google scholar
  • 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. google scholar
  • 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. google scholar
  • 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. google scholar
  • Patton, A. J. (2008). Copula-based Models for Financial Time Series. OFRC Working Papers Series, Oxford Financial Research Centre. google scholar
  • Patton, A. (2013). Copula methods for forecasting multivariate time series. Handbook of Economic Forecasting, 2, 899-960. google scholar
  • Pourkhanali, A., Kim, J. M., Tafakori, L., & Fard, F. A. (2016). Measuring systemic risk using vine-copula. Economic Modelling, 53, 63-74. google scholar
  • Reboredo, J. C., & Ugolini, A. (2016). Systemic risk of Spanish listed banks: a vine copula CoVaR approach. Spanish Journal of Finance and Accounting/Revista Espanola de Financiacion y Contabilidad, 45(1), 1-31. google scholar
  • Rockinger, M., & Jondeau, E. (2001). Conditional dependency of financial series: An application of copulas. google scholar
  • Sklar, M. (1959). Fonctions de repartition an dimensions et leurs marges. Publ. Inst. Statist. univ. Paris, 8, 229-231. google scholar
  • Şengül, S., & Yılmaz, E. (2019). Measuring Systemic Risks in the Turkish Banking Sector 1. Business and Economics Research Journal, 10(5), 1071-1084. google scholar
  • Talaslı, I. (2013). Systemic risk analysis of Turkish financial institutions with systemic expected shortfall. Central Bank Review, 13(3), 25-40. google scholar
  • Xia, X. (2018). Essays on Dependence Modelling with Vine Copulas and its Applications (Doctoral dissertation, University of Leicester). google scholar
  • Zeevi, A., & Mashal, R. (2002). Beyond correlation: Extreme co-movements between financial assets. Available at SSRN 317122. google scholar
  • 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 google scholar
  • 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. google scholar
  • Zhang, L., & Singh, V. P. (2019). Copulas and their applications in water resources engineering. Cambridge University Press. google scholar
Toplam 43 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İşletme
Bölüm Araştırma Makalesi
Yazarlar

Bükre Yıldırım Külekci 0000-0002-1246-9549

Gülden Poyraz 0000-0002-8324-6270

İsmail Gür 0000-0001-7014-4606

Ozan Evkaya 0000-0002-5076-8144

Yayımlanma Tarihi 26 Haziran 2023
Gönderilme Tarihi 17 Ocak 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 73 Sayı: 1

Kaynak Göster

APA Yıldırım Külekci, B., Poyraz, G., Gür, İ., Evkaya, O. (2023). BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi. İstanbul İktisat Dergisi, 73(1), 55-82. https://doi.org/10.26650/ISTJECON2022-1229039
AMA Yıldırım Külekci B, Poyraz G, Gür İ, Evkaya O. BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi. İstanbul İktisat Dergisi. Haziran 2023;73(1):55-82. doi:10.26650/ISTJECON2022-1229039
Chicago Yıldırım Külekci, Bükre, Gülden Poyraz, İsmail Gür, ve Ozan Evkaya. “BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula Ile İncelenmesi”. İstanbul İktisat Dergisi 73, sy. 1 (Haziran 2023): 55-82. https://doi.org/10.26650/ISTJECON2022-1229039.
EndNote Yıldırım Külekci B, Poyraz G, Gür İ, Evkaya O (01 Haziran 2023) BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi. İstanbul İktisat Dergisi 73 1 55–82.
IEEE B. Yıldırım Külekci, G. Poyraz, İ. Gür, ve O. Evkaya, “BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi”, İstanbul İktisat Dergisi, c. 73, sy. 1, ss. 55–82, 2023, doi: 10.26650/ISTJECON2022-1229039.
ISNAD Yıldırım Külekci, Bükre vd. “BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula Ile İncelenmesi”. İstanbul İktisat Dergisi 73/1 (Haziran 2023), 55-82. https://doi.org/10.26650/ISTJECON2022-1229039.
JAMA Yıldırım Külekci B, Poyraz G, Gür İ, Evkaya O. BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi. İstanbul İktisat Dergisi. 2023;73:55–82.
MLA Yıldırım Külekci, Bükre vd. “BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula Ile İncelenmesi”. İstanbul İktisat Dergisi, c. 73, sy. 1, 2023, ss. 55-82, doi:10.26650/ISTJECON2022-1229039.
Vancouver Yıldırım Külekci B, Poyraz G, Gür İ, Evkaya O. BIST100 Bankacılık Sektöründeki Bağımlılığın Asma Kopula ile İncelenmesi. İstanbul İktisat Dergisi. 2023;73(1):55-82.