Assessing connectivity delves into the evaluation of “systemic risk,” which pertains to the potential for rapid crisis proliferation across businesses, industries, and markets, potentially acting as an early indicator of an economic downturn’s magnitude. Managing risk becomes particularly challenging in highly interconnected financial markets, as Maggi et al. (2020) highlight. Indeed, the increased interconnectedness was a significant factor that amplified the extent and severity of the subprime crisis that occurred in 2007–2008, per Andries and Galasan (2020). Thus, it should be no surprise that academics have made considerable effort to understand how markets and their components communicate risk. The foundational work of Diebold and Yilmaz (2012, and 2014) has been instrumental, sparking extensive research into the implications of interconnectedness within financial and macroeconomic spheres. Drawing on Diebold and Yilmaz’s seminal contributions, a significant volume of literature has examined these dynamics, underscoring their importance in macroeconomic and financial analyses.
Currency markets worldwide are crucial for investors and policymakers because exchange rates are essential markers of the health of an economy. Additionally, foreign currency markets swiftly react to shifts in the broader economic landscape, economic conditions, and political events. Conversely, crude oil is an essential global commodity that has the potential to affect a variety of macroeconomic and monetary factors. Studies by Reboredo (2012), Noman et al. (2023), and Chang (2020) have shown how rising oil prices can lead to economic downturns, trade deficits, higher inflation, falling bond and stock prices, and heightened capital uncertainty. Furthermore, as demonstrated by the sharp rise in WTI, the crude oil market is infamous for its volatility crude oil prices from $36.98 on April 20, 2020, to $123 on March 8, 2022—a climb of more than 230%, rather than an increase of $160 per barrel, which would not be possible since the starting price was $36.98. The volatility has been exacerbated by public health concerns and geopolitical tensions, such as those arising from the COVID-19 pandemic and subsequent conflicts, contributing to the oil market’s unpredictability. Furthermore, fluctuations in the price of crude oil can significantly impact other financial and economic variables, including exchange rates. Notably, countries heavily reliant on oil imports have faced considerable currency devaluation due to soaring oil prices, as documented by Uche et al. (2022a) and Ali et al. (2022).
We examine the general and directional interconnectedness between foreign exchange and crude oil markets using two approaches to connectedness. Diebold and Yilmaz’s generalized connectedness technique (DY(2012, 2014)) is the first, and the second is the time-varying parameter vector autoregression (TVP-VAR) extended joint connectedness approach by Balcilar et al. Currency exchange rates are expected to be influenced by changes in commodity prices, especially in countries heavily reliant on these commodities (Chen and Rogoff, 2003; Gohar et al. 2022b, 2023). Oil is considered a commodity traded most globally to specifically affect a variety of financial indicators, including currency exchange rates and the countries’ balance of payments that rely heavily on oil. The interplay between foreign exchange and oil markets is influenced by several factors, including global price changes (Singh et al. 2018). Linkages between divergences in business cycles and integration in the currency and financial markets can also be elucidated (Maydybura et al. 2023; Chang et al. 2022a, 2023; Wan and He, 2021). However, it is essential to note that the causal link between those markets is dynamic and prone to alter in both direction and strength, contingent upon many factors. For instance, there has been a notable shift from the long-observed inverse relationship between exchange rates and oil prices, as reported by Singh et al. (2018).
Three theoretical explanations—the trade channel, the wealth effect channel, and the portfolio reallocation channel—help elucidate how fluctuations in oil prices impact the exchange rates (Singh et al. 2018; Habib et al. 2016; Chang et al. 2020a, 2020b). The portfolio reallocation channel posits that the medium- and long-term effects of oil price changes on currency rates are influenced by trade patterns and a country’s currency preferences, particularly towards the US dollar (Beckmann et al. 2020; Habib et al. 2016). According to the wealth effect theory, nations with oil exports experience a financial boon compared to those reliant on oil imports. As a result, the currencies of exporting countries tend to strengthen, whereas those of importing countries generally weaken. Current account imbalances play a crucial role in this dynamic, especially in the short term (Golub, 1983; Kilian, 2009; Krugman, 1983; Habib et al. 2016). The trade channel, explored in studies by Backus and Crucini (2000), Basher et al. (2016), and Chang et al. (2020c), suggests that rising oil prices bolster the trade balances of oil-exporting countries and deteriorate those of oil-importing countries. Contrary to the expected, this dynamic leads to an appreciation of the currency rates of exporting countries and a depreciation of the currency rates of importing countries.
In numerous research papers, the connectedness approach has been widely utilized to analyze the correlations between exchange rates and the relationships between oil prices and currency values experimentally. Variables like the period, countries considered, dataset, and methodologies significantly employed influence the outcomes of these studies. According to Wen and Wang’s (2020) research on the volatility connectedness among various currencies, the Euro and the US dollar are predominantly the sources of volatility. In contrast, the yen and the pound sterling tend to be net recipients of these fluctuations. Their findings also indicate that the volatility within the entire network is susceptible to changes in the global economic state and tends to increase during periods of economic turmoil.
According to Singh et al. (2018), there are dynamic links to networks between the forecasted currency volatility indices for nine prominent currency pairs, with Prices for crude oil and exchange rates between May 2007 and December 2016. According to their results in the dataset’s earlier portion, currency volatility was influenced by fluctuations in crude oil prices. In contrast, in the latter part, the volatility of currencies impacted crude oil price movements. Malik and Umar (2019) demonstrated that the correlation between oil price shocks and exchange rate movements has significantly strengthened since the global financial crisis, using fundamental shocks from oil price fluctuations as indicators. They further indicated that although there is a robust association between oil price shocks and currency rate changes, the latter does not entirely account for the variations in exchange rates.
Regarding unclear trade strategies, Huynh et al. (2020) analyzed the maneuvering impacts and connections on the returns and volatility of exchange rates among nine major world currencies relative to the US dollar. They identified varying spillover patterns and linkages between the currency rates amidst the uncertainty of trade policies. Wan and He (2021) assessed the dynamic interconnectedness of the G7 currencies using a Bayesian time-varying model. Their study revealed that the economic and financial environment influences the overall connectedness and exhibits significant temporal variations. Throughout the study time frame, the US dollar continuously exerted influence across the network, whereas the Japanese yen and the Euro alternated between affecting and being affected by shockwaves. Subsequent research by Wang et al. (2024) and Gohar et al. (2022c) supports these findings, highlighting the intricate dynamics of currency interconnectedness.
According to the assessment by Shang and Hamori (2021), WTI crude oil significantly contributes to the volatility and disturbances in returns that affect foreign currency markets. They found that WTI has a more pronounced effect on the exchange rates of oil-importing countries, especially before the COVID-19 period. They suggest that countries heavily reliant on American oil exhibit higher currency rate volatility due to the spillover effects of WTI crude oil prices.
In their 2021 study, Nekhili et al. investigated the relationship between currencies, such as the Swiss franc, Euro, British pound, Swedish krona, Canadian dollar, and Japanese yen, and commodity futures prices, including gold, wheat, copper, and crude oil. Their focus was on the time-frequency of returns and the spillover of fluctuations. The scholars found that the interactions between currencies and commodities are uneven, time-dependent, crisis-sensitive, and product-specific, with implications for the future prices of these commodities. Additionally, they discovered that oil plays a significant role in contributing to currency market volatility.
Adekoya and Oliyide (2020) highlighted a strong correlation between the US dollar, oil, and other financial markets during the COVID-19 pandemic. Conversely, Asadi et al. (2022) concluded from their analysis of volatility linkages among the crude oil, natural gas, coal, and stock markets in China and the US that the relationship between the currency and energy markets could be more robust.
Ahmad et al. (2020) analyzed high-frequency data and found that fluctuations in oil prices negatively affect China’s exchange rate. Alam et al. (2019) studied six significant currencies against the US dollar, utilizing data at five-minute intervals to explore the causal relationships between exchange rate changes and crude oil prices. More robust causal relationships were found between the crude oil and currency markets than between them. Additionally, they observed that these causal relationships intensify over time during economic and financial uncertainty.
In a related study, Fasanya et al. (2021) investigated the interactions between widely traded currency pairs and oil in the context of unpredictable US economic policies. They reported a robust connection between the crude oil and currency markets, with oil prices particularly reactive to these fluctuations. Employing a nonparametric quantile-based causality analysis, they suggested that economic policy uncertainty significantly influences spillovers across assets, most notably in the lower and median quantiles. Albulescu and Ajmi (2021) examined the impacts of commodity currencies and oil across time and frequency domains, uncovering a substantial link between oil and capital markets. In their findings, oil receives indirect shocks during different cyclical stages. This conclusion is corroborated by the studies of Uche et al. (2022b) and Hashmi and Chang (2023), highlighting the intricate dynamics between oil prices and financial markets.
Building upon the framework established by Balcilar et al. (2021), our research employs an extended joint connectedness approach based on time-varying parameter vector autoregression (TVP-VAR). This method, refined with insights from Lastrapes and Wiesen (2021) and Antonakakis et al. (2020), offers several advantages. Notably, it captures dynamic changes in coefficients over time more accurately, an essential feature given the extreme volatility observed in our selected variables. This volatility necessitates a robust model capable of adjusting to rapid parameter fluctuations. Additionally, the TVP-VAR approach is less sensitive to outliers and does not require an arbitrary selection of rolling windows, thereby preserving the integrity of our data analysis. Precise forecasts for decomposition in error variance are achieved without data loss, and the model’s efficacy with low-frequency datasets is particularly beneficial for our study’s scope. Moreover, this method introduces a theoretically derived normalization approach, moving away from the more contrived approach traditionally used in the DY methodology (2012, 2014).
To further clarify, our selection of the TVP-VAR model is driven by more than its proficiency in managing time-variant coefficients. The rationale behind this choice is deeply entrenched in the specific attributes of our dataset, characterized by substantial volatility and intricate dynamical patterns, which conventional models might fail to capture comprehensively. The adaptability of the TVP-VAR model to evolving conditions, underpinned by solid theoretical foundations, positions it as an optimal tool for dissecting our data. Its capability to offer more detailed and precise interpretations of the shifting interrelations among variables is pivotal to enhancing the depth and rigor of our analysis. Thus, utilizing the TVP-VAR model in our research is a methodologically sound choice, meticulously aligned with the unique challenges and traits inherent to our study’s framework.
Our findings indicate that the extended joint connectedness method using TVP-VAR outperforms the generalized connectedness method proposed by DY (2012, 2014) in various scenarios. For example, even though the Euro was the leading receiver of shocks according to the TVP-VAR enhanced joint connection technique, the DY generalized connectedness approach found the Euro to be the primary transmitter of shocks. We are confident that the findings from the extended Joint TVP-VAR connectedness approach are robust, offering new insights that significantly enrich the existing body of literature. This is because implementing a more sophisticated strategy confers multiple technique advancements.
The literature on the nexus between oil prices and exchange rates needs to be more cohesive and often presents a contradictory view. Our study introduces an advanced methodology that overcomes various methodological constraints, addressing this gap in the research. We uncover compelling evidence of highly time-varying dependencies between the two markets and establish bidirectional causality—a contrast to prior research, which predominantly suggests a unidirectional influence from the international currency markets’ oil market. Additionally, our analysis must include the time frame of the COVID-19 epidemic, given the significant economic disruptions it has caused. This aspect of our research is particularly critical as it allows us to examine the dynamics of these relationships amidst the volatility triggered by such an unprecedented event. Remarkably, we find an intensified connection between oil markets and currency rates during the COVID-19 pandemic. Our findings contribute novel insights to understanding the interplay between oil prices and exchange rates, providing valuable implications for regulators and investors.
Our study examines the connectedness between oil prices and exchange rates using the exchange rate data from five oil-exporting and six oil-importing countries. The five oil-exporting nations in our study—Canada, Mexico, Russia, Brazil, and Norway—were selected for their significant roles in the global oil supply chain. Each country contributes notably to the world’s oil exports and presents unique economic and geopolitical characteristics, offering a well-rounded view of the oil export sector. The six oil-importing countries—the UK, South Korea, Japan, China, Euro, and India—were chosen due to their substantial impact on global oil demand. These nations represent a blend of developed and emerging economies across various continents, providing a comprehensive perspective on global oil consumption. This deliberate selection of countries ensures our study comprehensively covers the diverse economic, geographic, and political dimensions that shape the global oil market, thereby providing a balanced analysis of the relationship between oil prices and foreign exchange rates.
This article is organized as follows: “Data and methodology” details the data, methodologies, and summary statistics employed in this study. “Findings” reports the outcomes of various connectedness assessments. Section 4 interprets these results, and Section 5 concludes the article.
