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Dergiler / Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2026 / Volume: 27 Issue: 2

DYNAMIC COMOVEMENT OF BIST 100 WITH S&P 500 AND DAX 40: A STOCHASTIC COPULA APPROACH

Çiğdem Yerli(BARTIN ÜNİVERSİTESİ, BARTIN MESLEK YÜKSEKOKULU, MUHASEBE VE VERGİ BÖLÜMÜ)0000-0001-7629-7064
Sayfa
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Yayın Tarihi
2026-06-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Overall, the findings highlight the importance of modeling dynamic, tail-sensitive dependence to understand Turkey’s integration with developed markets and to assess the fragility of international diversification under systemic stress.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:This study examines the time-varying dependence between BIST 100 and two major developed markets, the DAX 40 and the S&P 500, over the period 2000–2025.
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Temel Bulgular & Veri:Empirical results indicate strong and highly persistent dependence between the BIST 100 and both developed indices, with pronounced amplification during global stress episodes such as the 2008 Global Financial Crisis and the COVID-19 shock.
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Sonuç & Katkı:Overall, the findings highlight the importance of modeling dynamic, tail-sensitive dependence to understand Turkey’s integration with developed markets and to assess the fragility of international diversification under systemic stress.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2000–2025 Verileri
⚙️Değişkenler:Copula Functions · Dependence · Stock Markets

Abstract

This study examines the time-varying dependence between BIST 100 and two major developed markets, the DAX 40 and the S&P 500, over the period 2000–2025. To capture nonlinear and regime-dependent co-movement, a stochastic copula framework is employed in which the dependence parameter follows a latent autoregressive process. The analysis extends the baseline stochastic copula model by adopting heavy-tailed Student’s t marginal distributions, allowing for a more realistic representation of equity returns and improving inference during periods of extreme market movements. Empirical results indicate strong and highly persistent dependence between the BIST 100 and both developed indices, with pronounced amplification during global stress episodes such as the 2008 Global Financial Crisis and the COVID-19 shock. Across market pairs, Frank and Gumbel copulas provide the best overall fit, suggesting that dependence is not solely driven by crash-specific tail clustering but reflects a combination of persistent integration and stronger co-movement during large joint market movements. To evaluate economic significance, equally weighted international portfolios are constructed and assessed using Value-at-Risk, Expected Shortfall, variance reduction, and maximum drawdown. The portfolio evidence shows that diversification benefits are substantial in tranquil periods but deteriorate sharply during crisis regimes, with negative variance reduction observed during COVID-19, indicating diversification failure when global risk factors dominate. Overall, the findings highlight the importance of modeling dynamic, tail-sensitive dependence to understand Turkey’s integration with developed markets and to assess the fragility of international diversification under systemic stress.

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Bu Makaleye Atıf Yap

Yerli, Ç. (2026). DYNAMIC COMOVEMENT OF BIST 100 WITH S&P 500 AND DAX 40: A STOCHASTIC COPULA APPROACH. Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Volume: 27 Issue: 2. https://doi.org/10.53443/anadoluibfd.1723274

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Kaynakça (47)

  1. Al Rahahleh, N., Bhatti, M. I., & Adeinat, I. (2017). Tail dependence and information flow: Evidence from international equity markets. Physica A: Statistical Mechanics and Its Applications, 474, 319–329. https://doi.org/10.1016/j.physa.2017.01.063
  2. Alsu, E., & Taşdemir, A. (2017). Türkiye'nin ihracat hacminin yoğun olduğu ülkeler ile olan finansal entegrasyonu. İşletme Araştırmaları Dergisi, 9(4), 675–691.
  3. Ayaydın, H., Barut, A., & Pala, F. (2020). Long-term relationship between G-7 country's stock markets and Bist100: Fourier approach. Kastamonu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22(1), 24–34.
  4. Bauwens, L., Laurent, S., & Rombouts, J. V. (2006). Multivariate GARCH models: A survey. Journal of Applied Econometrics, 21(1), 79–109. https://doi.org/10.1002/jae.842
  5. Bieńkowski, W., Gawrońska-Nowak, B., & Grabowski, W. (2014). Comovements of stock markets in the CEE-3 countries during the global financial crisis. Eastern European Economics, 52(5), 32–55. https://doi.org/10.1080/00128775.2014.1004907
  6. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
  7. Bonfiglioli, A., & Favero, C. A. (2005). Explaining co-movements between stock markets: The case of US and Germany. Journal of International Money and Finance, 24(8), 1299–1316. https://doi.org/10.1016/j.jimonfin.2005.08.016
  8. Bozkurt, I., & Akman, E. (2016). Financial integration into EU: The Romanian case. Amfiteatru Economic Journal, 18(42), 269–285.
  9. Boztosun, Y. D. D. D., & Çelik, D. D. T. (2011). Türkiye borsasının Avrupa borsaları ile eşbütünleşme analizi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 16(1), 147–162.
  10. Brailsford, T. J. (1996). Volatility spillovers across the Tasman. Australian Journal of Management, 21(1), 13–27. https://doi.org/10.1177/031289629602100104
  11. Creal, D. D., & Tsay, R. S. (2015). High dimensional dynamic stochastic copula models. Journal of Econometrics, 189(2), 335–345. https://doi.org/10.1016/j.jeconom.2015.03.027
  12. Dajcman, S., Festic, M., & Kavkler, A. (2012). European stock market comovement dynamics during some major financial market turmoils in the period 1997 to 2010: A comparative DCC-GARCH and wavelet correlation analysis. Applied Economics Letters, 19(13), 1249–1256. https://doi.org/10.1080/13504851.2011.619481
  13. De Servigny, A., & Renault, O. (2002). Default correlation: Empirical evidence. Standard and Poor’s.
  14. Dewandaru, G., Rizvi, S. A. R., Masih, R., Masih, M., & Alhabshi, S. O. (2014). Stock market co-movements: Islamic versus conventional equity indices with multi-timescales analysis. Economic Systems, 38(4), 553–571. https://doi.org/10.1016/j.ecosys.2014.05.003
  15. Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158–171. https://doi.org/10.1111/j.1468-0297.2008.02208.x
  16. Elyasiani, E., Perera, P., & Puri, T. N. (1998). Interdependence and dynamic linkages between stock markets of Sri Lanka and its trading partners. Journal of Multinational Financial Management, 8(1), 89–101. https://doi.org/10.1016/S1042-444X(98)00020-6
  17. Graham, M., & Nikkinen, J. (2011). Co-movement of the Finnish and international stock markets: A wavelet analysis. The European Journal of Finance, 17(5–6), 409–425. https://doi.org/10.1080/1351847X.2010.543839
  18. Hafner, C. M., & Manner, H. (2012). Dynamic stochastic copula models: Estimation, inference and applications. Journal of Applied Econometrics, 27(2), 269–295. https://doi.org/10.1002/jae.1197
  19. Harvey, A., Ruiz, E., & Shephard, N. (1994). Multivariate stochastic variance models. The Review of Economic Studies, 61(2), 247–264. https://doi.org/10.2307/2297980
  20. Hatipoğlu, M., & Sekmen, T. (2016). Borsa İstanbul ve gelişmiş ülke borsalarının ortak hareketi üzerine bir çalışma. Siyaset, Ekonomi ve Yönetim Araştırmaları Dergisi, 4(3), 24–34.
  21. Hu, L. (2006). Dependence patterns across financial markets: A mixed copula approach. Applied Financial Economics, 16(10), 717–729.
  22. Huseynova, S., & Charkasov, M. (2024). Long-run relationship between Türkiye and SHANGHAI Cooperation Organization stock markets: Cointegration analysis. Journal of Statistics and Applied Sciences, (10), 1–9. https://doi.org/10.52693/jsas.1472756
  23. Huyghebaert, N., & Wang, L. (2010). The co-movement of stock markets in East Asia: Did the 1997–1998 Asian financial crisis really strengthen stock market integration? China Economic Review, 21(1), 98–112. https://doi.org/10.1016/j.chieco.2009.11.001
  24. Joe, H. (2005). Asymptotic efficiency of the two-stage estimation method for copula-based models. Journal of Multivariate Analysis, 94(2), 401–419. https://doi.org/10.1016/j.jmva.2004.06.003
  25. Jondeau, E., & Rockinger, M. (2006). The copula-GARCH model of conditional dependencies: An international stock market application. Journal of International Money and Finance, 25(5), 827–853. https://doi.org/10.1016/j.jimonfin.2006.04.007
  26. Karahan, F. (2023). BIST 100 endeksinin uluslararası hisse senedi endeksleri ile ilişkisi. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, (78), 448–458. https://doi.org/10.51290/dpusbe.1363099
  27. Liesenfeld, R., & Richard, J. F. (2003). Univariate and multivariate stochastic volatility models: Estimation and diagnostics. Journal of Empirical Finance, 10(4), 505–531. https://doi.org/10.1016/S0927-5398(02)00072-5
  28. Longin, F., & Solnik, B. (2001). Extreme correlation of international equity markets. The Journal of Finance, 56(2), 649–676. https://doi.org/10.1111/0022-1082.00340
  29. Metin, K., & Muradoglu, G. (2001). Forecasting integrated stock markets using international co-movements. Russian & East European Finance and Trade, 37(5), 45–63.
  30. Morana, C., & Beltratti, A. (2002). The effects of the introduction of the euro on the volatility of European stock markets. Journal of Banking & Finance, 26(10), 2047–2064. https://doi.org/10.1016/S0378-4266(01)00182-0
  31. Münyas, T. (2020). Türk sermaye piyasalarının gelişmiş ülke borsaları ile entegresyonu üzerine ampirik bir araştırma. Avrasya Sosyal ve Ekonomi Araştırmaları Dergisi, 7(9), 222–234.
  32. Nelsen, R. B. (1997). Dependence and order in families of Archimedean copulas. Journal of Multivariate Analysis, 60(1), 111–122. https://doi.org/10.1006/jmva.1996.1646
  33. Nguyen, C. C., & Bhatti, M. I. (2012). Copula model dependency between oil prices and stock markets: Evidence from China and Vietnam. Journal of International Financial Markets, Institutions and Money, 22(4), 758–773. https://doi.org/10.1016/j.intfin.2012.03.004
  34. Öner, H. (2018). Kırılgan Beşli ülkelerin borsa endeksleri arasında nedensellik ilişkisi: Ampirik bir analiz. Journal of Economic Policy Researches, 5(2), 152–166.
  35. Özşahin, Ş. (2017). Yükselen piyasa ekonomilerinde menkul kıymetler borsalarının entegrasyonu: Türkiye ve BRICS ülkeleri üzerine çoklu yapısal kırılmalı eş-bütünleşme analizi. Yönetim ve Ekonomi Dergisi, 24(2), 601–619. https://doi.org/10.18657/yonveek.319440
  36. Öztürk, H. (2018). BIST 30 endeksi ile MSCI gelişmekte olan piyasalar endeksinin küresel kriz öncesi ve sonrası eşbütünleşme analizi. Business and Economics Research Journal, 9(1), 109–121.
  37. Parmaksız, S., & Kocabıyık, T. (2020). BRICS ülke borsaları ile Türk borsası arasındaki ilişkinin keşfi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 25(3), 315–341.
  38. Patton, A. J. (2006). Modelling asymmetric exchange rate dependence. International Economic Review, 47(2), 527–556. https://doi.org/10.1111/j.1468-2354.2006.00387.x
  39. Rodriguez, J. C. (2007). Measuring financial contagion: A copula approach. Journal of Empirical Finance, 14(3), 401–423. https://doi.org/10.1016/j.jempfin.2006.07.002
  40. Rua, A., & Nunes, L. C. (2009). International comovement of stock market returns: A wavelet analysis. Journal of Empirical Finance, 16(4), 632–639. https://doi.org/10.1016/j.jempfin.2009.02.002
  41. Sklar, M. (1959). Fonctions de répartition à n dimensions et leurs marges. In Annales de l’ISUP (Vol. 8, No. 3, pp. 229–231).
  42. Şimşek, M. (2016). Borsa İstanbul (BIST) ve BRICS ülkelerinin hisse senedi piyasalarının ilişkisi üzerine bir inceleme. İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 5(3), 520–536.
  43. Yu, J., & Meyer, R. (2006). Multivariate stochastic volatility models: Bayesian estimation and model comparison. Econometric Reviews, 25(2–3), 361–384. https://doi.org/10.1080/07474930600713465
  44. Tankov, P. (2006). Simulation and option pricing in Lévy copula models. In Mathematical modelling of financial derivatives (IMA Volumes in Mathematics and Applications). Springer.
  45. Theodossiou, P., & Lee, U. (1993). Mean and volatility spillovers across major national stock markets: Further empirical evidence. Journal of Financial Research, 16(4), 337–350. https://doi.org/10.1111/j.1475-6803.1993.tb00152.x
  46. Tiryaki, H. N. (2025). Estimation of stock returns volatility and spillovers among DAX, S&P 500, and XU100 using GARCH and causality tests. Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26(3), 161–181. https://doi.org/10.53443/anadoluibfd.1635574
  47. Yıldız, A., & Aksoy, E. (2014). Morgan Stanley gelişmekte olan borsa endeksi ile BIST endeksi arasındaki eşbütünleşme ilişkisinin analiz edilmesi. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 28(1), 1–23.