Detecting the hidden asymmetric relationship between crude oil and the US dollar: A novel neural Granger causality method

Lu Wang, Hang Ruan, Yanran Hong and Keyu Luo

Research in International Business and Finance, 2023, vol. 64, issue C

Abstract: The relationship between financial series is not always easy to detect due to their underlying asymmetry and nonlinearity. Both characteristics are not usually considered simultaneously, which may lead to many drawbacks in financial analysis. Hence, we develop a novel neural Granger causality method from both asymmetric and nonlinear perspectives and further revisit the response and impact of crude oil on the exchange rate. Our findings reveal the unidirectional nonlinear and asymmetric effect of crude oil on the exchange rate; that is, positive and negative oil prices can have a substantial impact on exchange rate shocks. Interestingly, this influence seems to strengthen after the Russia–Ukraine conflict. Besides, we also use simulation technology to evaluate the rationality and effectiveness of our proposed methods. Investors, policymakers, and scholars may be interested in our findings regarding the oil-dollar relationships; as well as interested in applying our methodology to other contexts.

Keywords: Asymmetric Granger causality; Nonlinearity; Neural network; Oil price; USD; Russia–Ukraine conflict (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:64:y:2023:i:c:s0275531923000259

DOI: 10.1016/j.ribaf.2023.101899

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