Journals / Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2026 / Cilt: 24 Sayı: 4

Dynamic Dependence between Oil and Stock Markets: International Evidence with Stochastic Copula Approach

Petrol ile Borsalar arasındaki Dinamik Bağımlılık: Stokastik Kopula Yaklaşımı ile Uluslararası Bulgular

Emre Yıldırım(ANKARA HACI BAYRAM VELİ ÜNİVERSİTESİ)0000-0002-2816-7473
Mehmet Ali Cengiz(ONDOKUZ MAYIS UNIVERSITY)0000-0002-1271-2588
Pages
811–818
Publication Date
2024-08-20
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This indicates that oil and the US stock market are more likely to move together during periods of market uptrend.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Modeling the dependency structure between variables has recently received increasing attention in many disciplines, especially finance and economics.
📊
Key Findings & Data:In this study, the dependence between oil prices and stock markets is investigated through the stochastic copula approach, which is a class of time-varying copulas.
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Conclusion & Impact:This indicates that oil and the US stock market are more likely to move together during periods of market uptrend.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Bağımlılık · Stokastik Kapula · Dinamik Modelleme

Abstract

Modeling the dependency structure between variables has recently received increasing attention in many disciplines, especially finance and economics. In this study, the dependence between oil prices and stock markets is investigated through the stochastic copula approach, which is a class of time-varying copulas. This model enables to capture whole dependency between variables dynamically. Unlike time-varying copulas, it takes into account the latent process as well as observations in modeling dependency and thus evaluates the dependency structure in a more comprehensive framework. Empirical findings suggest that dependency between oil and stock markets evolve over time. There is a symmetric dependence between oil and the UK stock market, but the relationship between oil and the US stock market is measured by upper tail dependence. This indicates that oil and the US stock market are more likely to move together during periods of market uptrend.

Özet

Değişkenler arasındaki bağımlılık yapısının modellenmesi son dönemde öncelikle finans ve ekonomi olmak üzere birçok disiplinde giderek artan bir ilgi kazanmıştır. Bu çalışmada, petrol fiyatları ile hisse senedi piyasaları arasındaki bağımlılık, zamanla değişen kapulaların bir sınıfı olan stokastik kopula yaklaşımıyla araştırılmaktadır. Bu model değişkenler arasındaki tüm bağımlılığı dinamik olarak yakalamayı sağlar. Zamanla değişen kapulalardan farklı olarak bağımlılığı modellemede gözlemlerin yanı sıra gizli süreci de dikkate alır ve böylece bağımlılık yapısını daha kapsamlı bir çerçevede değerlendirir. Deneysel bulgular, petrol ve hisse senedi piyasaları arasındaki bağımlılığın zamanla geliştiğini göstermektedir. Petrol ile Birleşik Krallık borsası arasında simetrik bir bağımlılık vardır ancak petrol ile ABD borsası arasındaki ilişki üst kuyruk bağımlılığı ile ölçülmektedir. Bu, petrol ve ABD borsasının yükseliş trendi dönemlerinde birlikte hareket etme eğiliminin daha yüksek olduğunu göstermektedir.

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Cite This Article

Yıldırım, E., Cengiz, M. A. (2024). Dynamic Dependence between Oil and Stock Markets: International Evidence with Stochastic Copula Approach. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 24 Sayı: 4, 811–818. https://doi.org/10.35414/akufemubid.1383940

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References (18)

  1. Bollerslev, T., 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
  2. Caillault, C. and Guegan, D., 2005. Empirical estimation of tail dependence using copulas: application to Asian markets. Quantitative finance 5(5): 489-501. https://doi.org/10.1080/14697680500147853
  3. Cherubini, U., Luciano, E. and Vecchiato, W., 2004. Copula methods in finance. John Wiley & Sons.
  4. Garcia-Jorcano, L. and Benito, S., 2020. Studying the properties of the Bitcoin as a diversifying and hedging asset through a copula analysis: Constant and time-varying. Research in International Business and Finance, 54, 101300. https://doi.org/10.1016/j.ribaf.2020.101300
  5. Haffar, A. and Le Fur, É., 2022. Dependence structure of CAT bonds and portfolio diversification: a copula-GARCH approach. Journal of Asset Management, 23(4): 297-309. https://www.doi.org/10.1057/s41260-022-00271-3
  6. Hafner, C. M. and 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
  7. Hu, J., 2010. Dependence structures in Chinese and US financial markets: a time-varying conditional copula approach. Applied Financial Economics, 20(7): 561-583. https://doi.org/10.1080/09603100903459865
  8. Hu, L., 2006. Dependence patterns across financial markets: a mixed copula approach. Applied financial economics, 16(10): 717-729. https://doi.org/10.1080/09603100500426515
  9. Joe, H., 2014. Dependence modeling with copulas. CRC press.
  10. Li, B. and Zeng, Z., 2018. Time-varying dependence structures of equity markets of China, ASEAN and the USA. Applied Economics Letters, 25(2): 87-91. https://doi.org/10.1080/13504851.2017.1296545
  11. McNeil, A. J., Frey, R. and Embrechts, P., 2005. Quantitative risk management: concepts, techniques and tools-revised edition. Princeton university press.
  12. Marimoutou, V., and Soury, M. (2015). Energy markets and CO2 emissions: Analysis by stochastic copula autoregressive model. Energy, 88, 417-429. https://doi.org/10.1016/j.energy.2015.05.060
  13. Naeem, M. A., Bouri, E., Costa, M. D., Naifar, N. and Shahzad, S. J. H., 2021. Energy markets and green bonds: A tail dependence analysis with time-varying optimal copulas and portfolio implications. Resources Policy, 74, 102418. https://doi.org/10.1016/j.resourpol.2021.102418
  14. Nelsen, R. B., 2006. An introduction to copulas. Springer.
  15. 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
  16. Rehman, M. U., Asghar, N. and Kang, S. H., 2020. Do Islamic indices provide diversification to bitcoin? A time-varying copulas and value at risk application. Pacific-Basin Finance Journal, 61, 101326. https://doi.org/10.1016/j.pacfin.2020.101326
  17. 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
  18. Sklar, A., 1959. Functions de Répartition à n dimensions et leurs Marges. Publications Institut de Statistique de l’Université de Paris 8(2) 29–31. Yang, L. and Hamori, S., 2014. Gold prices and exchange rates: a time-varying copula analysis. Applied Financial Economics, 24(1): 41-50. https://doi.org/10.1080/09603107.2013.859375