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Why Is the Correlation between Crude Oil Prices and the US Dollar Exchange Rate Time-Varying?—Explanations Based on the Role of Key Mediators

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  • Jia Liao

    (School of Business, Shanghai University of International Business and Economics, Shanghai 201620, China)

  • Yu Shi

    (School of International Economics and Business, Nanjing University of Finance and Economics, Nanjing 210023, China)

  • Xiangyun Xu

    (School of International Economics and Business, Nanjing University of Finance and Economics, Nanjing 210023, China)

Abstract

Using DCC-GARCH model, this paper finds that, since 1990, the relationship between crude oil prices and the US dollar index is time-varying, demonstrating a process of ‘very weak correlation—negative correlation—enhanced negative correlation—weakening negative correlation’, but the existing research does not provide enough reasonable explanation. Therefore, this paper proposed a ‘key mediating factors’ hypothesis which points out that whether there is a common ‘key mediating factor’ is important source of the time-varying relationship between two assets. We argue that market trend and financial market sentiment undertook the role of ‘key mediating factor’ during the period of the 2002 to the financial crisis and financial crisis to 2013, while other periods lack the ‘key mediating factors’.

Suggested Citation

  • Jia Liao & Yu Shi & Xiangyun Xu, 2018. "Why Is the Correlation between Crude Oil Prices and the US Dollar Exchange Rate Time-Varying?—Explanations Based on the Role of Key Mediators," IJFS, MDPI, vol. 6(3), pages 1-13, June.
    • Handle: RePEc:gam:jijfss:v:6:y:2018:i:3:p:61-:d:154226
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    2. Maneejuk, Paravee & Kaewtathip, Nuttaphong & Jaipong, Peemmawat & Yamaka, Woraphon, 2022. "The transition of the global financial markets' connectedness during the COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    3. César Castro & Rebeca Jiménez-Rodríguez, 2020. "Dynamic interactions between oil price and exchange rate," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-20, August.
    4. Urolagin, Siddhaling & Sharma, Nikhil & Datta, Tapan Kumar, 2021. "A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting," Energy, Elsevier, vol. 231(C).

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