Unveiling the dynamic linkages between energy, forex and financial markets amidst natural and man-made outbreaks

Purpose This paper aims to analyze the dynamic linkages of the energy market with the forex market. The energy market is measured by crude oil WTI, while the forex market is proxied by Brazilian real (RBRL), Mexican peso (RMXN), South African rand (RZAR), Turkish lira (RTRY) and British pound sterling (RGBP) exchange rate. Design/methodology/approach For the study, daily observations of these constituent asset classes extending from December 31, 2019, to August 16, 2022, are taken as the data. Furthermore, it is categorized into two different sub-samples in the form of the COVID-19 outbreak (December 31, 2019 to February 23, 2022) and the Russo−Ukraine invasion (February 24, 2022 to August 16, 2022). For empirical estimation, Diebold and Yilmaz model (2014) and Barunik and Krehlik test (2018) are used to examine the dynamic linkages. Findings The study concludes that the Mexican peso (RMXN) receives and transmits the highest spillover, while crude oil (RCOWTI) receives and transmits the least volatility to the network connection in full sample. In addition, the authors report that the dynamic linkage is not constant in the short, medium and long run. Furthermore, the spillover index in the Russo−Ukraine invasion is higher (29.92%) than full observation (22.03%) and COVID-19 outbreak (21.10%) in the short run. Originality/value This paper ventures to offer insight to investors, traders and policymakers based on normal trading days and crisis periods.

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Publication Details

Journal
Review of Accounting and Finance
Published
2024-11-27
DOI
https://doi.org/10.1108/raf-03-2024-0086
Citations
2
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
2.26
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article

Unveiling the dynamic linkages between energy, forex and financial markets amidst natural and man-made outbreaks

Nandita Mishra, Silky Vigg Kushwah, Farhad Taghizadeh–Hesary, Miklesh Prasad Yadav
2 citations
Review of Accounting and Finance
Market Dynamics and Volatility
2.26
article

Unveiling the dynamic linkages between energy, forex and financial markets amidst natural and man-made outbreaks

Nandita Mishra, Silky Vigg Kushwah, Farhad Taghizadeh–Hesary, Miklesh Prasad Yadav
article en
2 citations

Abstract

Purpose This paper aims to analyze the dynamic linkages of the energy market with the forex market. The energy market is measured by crude oil WTI, while the forex market is proxied by Brazilian real (RBRL), Mexican peso (RMXN), South African rand (RZAR), Turkish lira (RTRY) and British pound sterling (RGBP) exchange rate. Design/methodology/approach For the study, daily observations of these constituent asset classes extending from December 31, 2019, to August 16, 2022, are taken as the data. Furthermore, it is categorized into two different sub-samples in the form of the COVID-19 outbreak (December 31, 2019 to February 23, 2022) and the Russo−Ukraine invasion (February 24, 2022 to August 16, 2022). For empirical estimation, Diebold and Yilmaz model (2014) and Barunik and Krehlik test (2018) are used to examine the dynamic linkages. Findings The study concludes that the Mexican peso (RMXN) receives and transmits the highest spillover, while crude oil (RCOWTI) receives and transmits the least volatility to the network connection in full sample. In addition, the authors report that the dynamic linkage is not constant in the short, medium and long run. Furthermore, the spillover index in the Russo−Ukraine invasion is higher (29.92%) than full observation (22.03%) and COVID-19 outbreak (21.10%) in the short run. Originality/value This paper ventures to offer insight to investors, traders and policymakers based on normal trading days and crisis periods.

Review of Accounting and FinanceVol. 24(1)
Linköping University (SE), Tokai University (JP), International Management Institute (IN), Indian Institute of Foreign Trade (IN), Lebanese American University (LB)
Affordable and clean energy
Openalex Percentile: Top 7%
Market Dynamics and Volatility
2.26
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