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

Year 2024, Volume: 24 Issue: 4, 811 - 818, 20.08.2024
https://doi.org/10.35414/akufemubid.1383940

Abstract

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.

Ethical Statement

Bu makale tüm etik standartlarına uygun olarak hazırlanmıştır.

References

  • Bollerslev, T., 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
  • 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
  • Cherubini, U., Luciano, E. and Vecchiato, W., 2004. Copula methods in finance. John Wiley & Sons.
  • 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
  • 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
  • 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
  • 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
  • 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
  • Joe, H., 2014. Dependence modeling with copulas. CRC press.
  • 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
  • McNeil, A. J., Frey, R. and Embrechts, P., 2005. Quantitative risk management: concepts, techniques and tools-revised edition. Princeton university press.
  • 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
  • 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
  • Nelsen, R. B., 2006. An introduction to copulas. Springer.
  • 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
  • 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
  • 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
  • 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

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

Year 2024, Volume: 24 Issue: 4, 811 - 818, 20.08.2024
https://doi.org/10.35414/akufemubid.1383940

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.

Ethical Statement

This paper complies with all the ethical standards.

References

  • Bollerslev, T., 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
  • 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
  • Cherubini, U., Luciano, E. and Vecchiato, W., 2004. Copula methods in finance. John Wiley & Sons.
  • 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
  • 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
  • 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
  • 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
  • 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
  • Joe, H., 2014. Dependence modeling with copulas. CRC press.
  • 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
  • McNeil, A. J., Frey, R. and Embrechts, P., 2005. Quantitative risk management: concepts, techniques and tools-revised edition. Princeton university press.
  • 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
  • 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
  • Nelsen, R. B., 2006. An introduction to copulas. Springer.
  • 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
  • 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
  • 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
  • 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
There are 18 citations in total.

Details

Primary Language English
Subjects Risk Analysis, Applied Statistics
Journal Section Articles
Authors

Emre Yıldırım 0000-0002-2816-7473

Mehmet Ali Cengiz 0000-0002-1271-2588

Early Pub Date July 23, 2024
Publication Date August 20, 2024
Submission Date October 31, 2023
Acceptance Date June 27, 2024
Published in Issue Year 2024 Volume: 24 Issue: 4

Cite

APA 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, 24(4), 811-818. https://doi.org/10.35414/akufemubid.1383940
AMA Yıldırım E, Cengiz MA. Dynamic Dependence between Oil and Stock Markets: International Evidence with Stochastic Copula Approach. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. August 2024;24(4):811-818. doi:10.35414/akufemubid.1383940
Chicago Yıldırım, Emre, and Mehmet Ali Cengiz. “Dynamic Dependence Between Oil and Stock Markets: International Evidence With Stochastic Copula Approach”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 24, no. 4 (August 2024): 811-18. https://doi.org/10.35414/akufemubid.1383940.
EndNote Yıldırım E, Cengiz MA (August 1, 2024) Dynamic Dependence between Oil and Stock Markets: International Evidence with Stochastic Copula Approach. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 24 4 811–818.
IEEE E. Yıldırım and M. A. Cengiz, “Dynamic Dependence between Oil and Stock Markets: International Evidence with Stochastic Copula Approach”, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 24, no. 4, pp. 811–818, 2024, doi: 10.35414/akufemubid.1383940.
ISNAD Yıldırım, Emre - Cengiz, Mehmet Ali. “Dynamic Dependence Between Oil and Stock Markets: International Evidence With Stochastic Copula Approach”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 24/4 (August 2024), 811-818. https://doi.org/10.35414/akufemubid.1383940.
JAMA Yıldırım E, Cengiz MA. Dynamic Dependence between Oil and Stock Markets: International Evidence with Stochastic Copula Approach. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2024;24:811–818.
MLA Yıldırım, Emre and Mehmet Ali Cengiz. “Dynamic Dependence Between Oil and Stock Markets: International Evidence With Stochastic Copula Approach”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 24, no. 4, 2024, pp. 811-8, doi:10.35414/akufemubid.1383940.
Vancouver Yıldırım E, Cengiz MA. Dynamic Dependence between Oil and Stock Markets: International Evidence with Stochastic Copula Approach. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2024;24(4):811-8.