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Relationship between oil price and exchange rate by FDA and copula

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  • Jong-Min Kim
  • Hojin Jung

Abstract

This article investigates the relationship between daily crude oil prices and exchange rates. Functional data analysis is used to show the clustering pattern of exchange rates and oil prices over the time period through high dimensional visualizations. We select exchange rates for important currencies related to crude oil prices by using the objective Bayesian variable selection method. The selected sample data exhibits non-normal distribution with fat tails and skewness. Under the non-normality of the return series, we use copula functions that do not require to assume the bivariate normality to consider marginal distribution. In particular, our study applies the popular and powerful statistical methods such as Gaussian copula partial correlations and Gaussian copula marginal regression. We find evidence of significant dependence for all considered pairs, except for the Mexican peso-Brent. Our empirical results also show that the rise in the West Texas Intermediate (WTI) oil price returns is associated with a depreciation of the US dollar.

Suggested Citation

  • Jong-Min Kim & Hojin Jung, 2018. "Relationship between oil price and exchange rate by FDA and copula," Applied Economics, Taylor & Francis Journals, vol. 50(22), pages 2486-2499, May.
    • Handle: RePEc:taf:applec:v:50:y:2018:i:22:p:2486-2499
    DOI: 10.1080/00036846.2017.1400652
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    Citations

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    Cited by:

    1. Yan, Wan-Lin & Cheung, Adrian (Wai Kong), 2024. "Connectedness among Chinese climate policy uncertainty, exchange rate, Chinese and international crude oil markets: Insights from time and frequency domain analyses of high order moments," The North American Journal of Economics and Finance, Elsevier, vol. 73(C).
    2. Niyati Bhanja & Samia Nasreen & Arif Billah Dar & Aviral Kumar Tiwari, 2022. "Connectedness in International Crude Oil Markets," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 227-262, January.
    3. repec:mth:ijafr8:v:9:y:2019:i:1:p:298-316 is not listed on IDEAS
    4. Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021. "Applications of machine learning for corporate bond yield spread forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    5. Xiaoyong Xiao & Jing Huang, 2018. "Dynamic Connectedness of International Crude Oil Prices: The Diebold–Yilmaz Approach," Sustainability, MDPI, vol. 10(9), pages 1-16, September.
    6. Chkir, Imed & Guesmi, Khaled & Brayek, Angham Ben & Naoui, Kamel, 2020. "Modelling the nonlinear relationship between oil prices, stock markets, and exchange rates in oil-exporting and oil-importing countries," Research in International Business and Finance, Elsevier, vol. 54(C).
    7. Joshua Eklund & Jong-Min Kim, 2022. "Examining Factors That Affect Movie Gross Using Gaussian Copula Marginal Regression," Forecasting, MDPI, vol. 4(3), pages 1-14, July.
    8. Rehman, Mobeen Ur & Katsiampa, Paraskevi & Zeitun, Rami & Vo, Xuan Vinh, 2023. "Conditional dependence structure and risk spillovers between Bitcoin and fiat currencies," Emerging Markets Review, Elsevier, vol. 55(C).
    9. Li Liu & Yu-Min Liu & Jong-Min Kim & Rui Zhong & Guang-Qian Ren, 2020. "Analysis of Tail Dependence between Sovereign Debt Distress and Bank Non-Performing Loans," Sustainability, MDPI, vol. 12(2), pages 1-20, January.
    10. Dacio Villarreal-Samaniego, 2021. "The dynamics of oil prices, COVID-19, and exchange rates in five emerging economies in the atypical first quarter of 2020," Estudios Gerenciales, Universidad Icesi, vol. 37(158), pages 17-27, March.

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