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Bağımlılık Modellemede Stokastik Kopula Yaklaşımı: Emtia ve Döviz Kuru Piyasasından Bulgular

Year 2024, Volume: 14 Issue: 1, 1 - 18, 28.07.2024

Abstract

Bu çalışmada, BRICS ülkelerinin emtia fiyatları ile döviz kurları arasındaki bağımlılık yapısı, zamanla değişen kopulaların özel bir sınıfı olan stokastik kopula ile modellenmiştir. Bu model doğrusal olmayan bir modeldir ve parametresi gözlemlenemeyen bir stokastik süreci takip etmektedir. Bu yaklaşım hem gözlemleri hem de gizli süreci dikkate aldığından bağımlılığın daha esnek ve kapsamlı bir şekilde ele alınmasına olanak sağlar.
Bu çalışmadaki veri seti Ocak 2015 ile Aralık 2022 arası günlük kapanış fiyatlarını kapsamakta olup Yahoo finans websitesinden alınmıştır. Verilerin analizi için RStudio ve MATLAB programları kullanılmıştır. Petrol fiyatları ile BRICS ülkelerinin döviz kurları arasında zamanla değişen simetrik bir bağımlılık olduğu tespit edilmiştir. Petrol ve BRL ile petrol ve RUB için üst kuyruk bağımlılığı, diğer BRICS ülkelerinin ise petrol ve döviz kurları için alt kuyruk bağımlılığının olduğu göz ardı edilmemelidir. Öte yandan altın ile diğer BRICS ülkelerinin döviz kurları arasında zamanla değişen simetrik bir bağımlılık mevcutken, altın ile BRL arasındaki ilişki çoğunlukla üst kuyruk bağımlılığı ile ölçülmektedir. Son olarak altın ve petrol fiyatları arasındaki bağımlılığın dinamik ve simetrik olduğu ancak asimetrinin etkisini ölçmek için üst kuyruk bağımlılığının da dikkate alınması gerektiği ileri sürülmektedir. Bulguların politika yapıcılar ve yatırımcılar için önemli çıkarımlar sunmaktadır.

References

  • Albulescu, C. T., Aubin, C., Goyeau, D., & Tiwari, A. K. (2018). Extreme co-movements and dependencies among major international exchange rates: A copula approach. The Quarterly Review of Economics and Finance, 69, 56-69.
  • 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.
  • Bollerslev T (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
  • Cherubini, U., Luciano, E., & Vecchiato, W. (2004). Copula methods in finance. John Wiley & Sons.
  • Fenech, J. P., & Vosgha, H. (2019). Oil price and Gulf Corporation Council stock indices: New evidence from time-varying copula models. Economic Modelling, 77, 81-91.
  • Hafner C M and Manner H (2012). Dynamic stochastic copula models: Estimation, inference and applications. Journal of Applied Econometrics, 27(2): 269-295.
  • He, Y., & Hamori, S. (2019). Conditional dependence between oil prices and exchange rates in BRICS countries: An application of the copula-GARCH model. Journal of Risk and Financial Management, 12(2), 99.
  • Joe, H. (2014). Dependence modeling with copulas. CRC Press, New York.
  • Kayalar, D. E., Küçüközmen, C. C., & Selcuk-Kestel, A. S. (2017). The impact of crude oil prices on financial market indicators: copula approach. Energy Economics, 61, 162-173.
  • Kumar, S., Tiwari, A. K., Chauhan, Y., & Ji, Q. (2019). Dependence structure between the BRICS foreign exchange and stock markets using the dependence-switching copula approach. International Review of Financial Analysis, 63, 273-284.
  • Meng, J., Nie, H., Mo, B., & Jiang, Y. (2020). Risk spillover effects from global crude oil market to China’s commodity sectors. Energy, 202, 117208.
  • Nelsen R B (2007). An introduction to copulas. Springer Science & Business Media, New York.
  • Patton A J (2006). Modelling asymmetric exchange rate dependence. International economic review, 47(2): 527-556.
  • Penzer, J., Schmid, F., & Schmidt, R. (2012). Measuring large comovements in financial markets. Quantitative Finance, 12(7), 1037-1049.
  • Reboredo, J. C. (2012). Modelling oil price and exchange rate co-movements. Journal of Policy Modeling, 34(3), 419-440.
  • Sebai, S., & Naoui, K. (2015). A study of the interactive relationship between oil price and exchange rate: A copula approach and a DCC-MGARCH model. The Journal of Economic Asymmetries, 12(2), 173-189.
  • Sklar, A. 1959. Functions de Répartition à n dimensions et leurs Marges. Publications Institut de Statistique de l’Université de Paris 8: 229–31.
  • Wu, C. C., & Lin, Z. Y. (2014). An economic evaluation of stock–bond return comovements with copula-based GARCH models. Quantitative Finance, 14(7), 1283-1296.
  • Wu, C. C., Chung, H., & Chang, Y. H. (2012). The economic value of co-movement between oil price and exchange rate using copula-based GARCH models. Energy Economics, 34(1), 270-282.
  • Yang, L., & Hamori, S. (2014). Gold prices and exchange rates: a time-varying copula analysis. Applied Financial Economics, 24(1), 41-50.

Stochastic Copula Approach for Modeling Dependency: Evidence from Commodity and Exchange Rate Markets

Year 2024, Volume: 14 Issue: 1, 1 - 18, 28.07.2024

Abstract

In this study, the dependency structure between commodity prices and exchange rates of BRICS countries is modeled by the stochastic copula which is a particular class of time-varying copulas. This model is a nonlinear and its parameter follows an unobservable stochastic process. Since this approach regards both the observations and the latent process, it enables to be handled the dependency in a more flexible and comprehensive way.
The data set includes daily closing prices between January 2015 and December 2022, and they are extracted from Yahoo finance website. RStudio and MATLAB programs are used to analyze the data. It is found that there is a time-varying symmetrical dependence between oil prices and the exchange rates of BRICS countries. It should not be ignored that there is an upper tail dependence for oil and BRL and oil and RUB, and a lower tail dependence for oil and exchange rates of other BRICS countries. On the other hand, there is a time-varying symmetrical dependence between gold and the exchange rates of other BRICS countries while the relationship between gold and BRL is mostly measured by the upper tail dependence. Finally, it is suggested that dependency between gold and oil prices are dynamic and symmetric, but the upper tail dependency should be taken into account to measure the effect of asymmetry. The findings have important implications for policy makers and investors.

References

  • Albulescu, C. T., Aubin, C., Goyeau, D., & Tiwari, A. K. (2018). Extreme co-movements and dependencies among major international exchange rates: A copula approach. The Quarterly Review of Economics and Finance, 69, 56-69.
  • 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.
  • Bollerslev T (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
  • Cherubini, U., Luciano, E., & Vecchiato, W. (2004). Copula methods in finance. John Wiley & Sons.
  • Fenech, J. P., & Vosgha, H. (2019). Oil price and Gulf Corporation Council stock indices: New evidence from time-varying copula models. Economic Modelling, 77, 81-91.
  • Hafner C M and Manner H (2012). Dynamic stochastic copula models: Estimation, inference and applications. Journal of Applied Econometrics, 27(2): 269-295.
  • He, Y., & Hamori, S. (2019). Conditional dependence between oil prices and exchange rates in BRICS countries: An application of the copula-GARCH model. Journal of Risk and Financial Management, 12(2), 99.
  • Joe, H. (2014). Dependence modeling with copulas. CRC Press, New York.
  • Kayalar, D. E., Küçüközmen, C. C., & Selcuk-Kestel, A. S. (2017). The impact of crude oil prices on financial market indicators: copula approach. Energy Economics, 61, 162-173.
  • Kumar, S., Tiwari, A. K., Chauhan, Y., & Ji, Q. (2019). Dependence structure between the BRICS foreign exchange and stock markets using the dependence-switching copula approach. International Review of Financial Analysis, 63, 273-284.
  • Meng, J., Nie, H., Mo, B., & Jiang, Y. (2020). Risk spillover effects from global crude oil market to China’s commodity sectors. Energy, 202, 117208.
  • Nelsen R B (2007). An introduction to copulas. Springer Science & Business Media, New York.
  • Patton A J (2006). Modelling asymmetric exchange rate dependence. International economic review, 47(2): 527-556.
  • Penzer, J., Schmid, F., & Schmidt, R. (2012). Measuring large comovements in financial markets. Quantitative Finance, 12(7), 1037-1049.
  • Reboredo, J. C. (2012). Modelling oil price and exchange rate co-movements. Journal of Policy Modeling, 34(3), 419-440.
  • Sebai, S., & Naoui, K. (2015). A study of the interactive relationship between oil price and exchange rate: A copula approach and a DCC-MGARCH model. The Journal of Economic Asymmetries, 12(2), 173-189.
  • Sklar, A. 1959. Functions de Répartition à n dimensions et leurs Marges. Publications Institut de Statistique de l’Université de Paris 8: 229–31.
  • Wu, C. C., & Lin, Z. Y. (2014). An economic evaluation of stock–bond return comovements with copula-based GARCH models. Quantitative Finance, 14(7), 1283-1296.
  • Wu, C. C., Chung, H., & Chang, Y. H. (2012). The economic value of co-movement between oil price and exchange rate using copula-based GARCH models. Energy Economics, 34(1), 270-282.
  • Yang, L., & Hamori, S. (2014). Gold prices and exchange rates: a time-varying copula analysis. Applied Financial Economics, 24(1), 41-50.
There are 20 citations in total.

Details

Primary Language English
Subjects Econometric and Statistical Methods, Risk Analysis, Applied Statistics
Journal Section Research Articles
Authors

Emre Yıldırım 0000-0002-2816-7473

Mehmet Ali Cengiz 0000-0002-1271-2588

Publication Date July 28, 2024
Submission Date November 16, 2023
Acceptance Date July 1, 2024
Published in Issue Year 2024 Volume: 14 Issue: 1

Cite

APA Yıldırım, E., & Cengiz, M. A. (2024). Stochastic Copula Approach for Modeling Dependency: Evidence from Commodity and Exchange Rate Markets. İstatistik Araştırma Dergisi, 14(1), 1-18.