Moderating Effects of Exchange Rates in the US-China Context: A Study on Manufacturing Purchasing Managers’ Index (PMI), Stock Indices, and Crude Oil Price

This study investigates how Purchasing Managers' Indices (PMI) from the US and China, along with US stock indices, influence crude oil prices. It specifically examines the moderating effects of US and Chinese exchange rates on these relationships. Using data from January 2016 to December 2023 and regression analysis, the result show that US PMI, Chinese PMI, and US stock indices significantly impact oil prices. Interestingly, the US exchange rate moderates the impact of US PMI more, while the Chinese exchange rate moderates the impact of Chinese PMI and US stock indices on oil prices. This suggests China's growing economic influence, as changes in its currency index have a stronger moderating effect on these relationships.

Introduction

The global oil market plays an important role in the world economy, and understanding the factors that influence crude oil prices is critical for governments, businesses, and investors (Fanget al., 2006). Oil prices affect inflation, economic growth, and various industries around the world. Therefore, identifying the determinants of oil prices is very important for information-based decision-making.

Crude oil prices are known to be complex and fluctuating, prompting many researchers to study and predict oil prices as well as the factors that influence them (see Fig. 1). Many studies have investigated the relationship between crude oil prices and macroeconomic variables, including economic growth, inflation, interest rates, exchange rates, and stock indices (Tabash et al., 2022; Conget al., 2008; Kasongwa & Minja, 2022; Wang & Sun, 2017; Piriyev, 2018; Ivanovski & Hailemariam, 2021). Hamilton (2011) analyzes significant oil price fluctuations and their economic consequences and Hamilton (1996) examines the changing relationship between oil price shocks and the U.S. economy from the 1970s to the early 1990s. Kilian (2009) analyzes the factors that influence oil price movements and their relationship to US macroeconomic performance. He found that changes in aggregate oil demand had a more significant impact on oil prices than fluctuations in supply. Mei-Chih and Chang (2019) further explores the demand-driven perspective by examining the relationship between the Purchasing Managers’ Index (PMI) and oil price fluctuations in the US and China.

Fig. 1. Crude oil price in 2000–2023. Source: Energy International Agency.

The US and China are the two largest economies in the world. The US is the largest oil producer and consumer, while China is the second largest oil importer after the US (Organization of the Petroleum Exporting Countries (OPEC), 2023). Kilian (2009) suggests that in recent decades, disruptions in global oil production have not been a major factor in oil price fluctuations. In contrast, oil demand from fast-growing emerging markets has become the main determinant of oil prices.

China’s economic growth and increasing demand for oil have driven efforts to internationalize its currency, the Yuan (Renminbi, RMB) (Kamel & Wang, 2019). China has expanded the use of the RMB in cross-border trade and investment, created a market for assets expressed in RMB, and successfully included the RMB in the IMF’s SDR currency basket. In 2018, China launched crude oil futures contracts on the Shanghai International Energy Exchange to increase the role of the RMB in international oil trading (Slav, 2018). This development has sparked debate about the RMB’s potential to challenge the US dollar as the world’s reserve currency.

The “Petrodollar” system refers to the flow of funds from oil producing countries to the US in exchange for oil sales denominated in US dollars. In contrast, “Petroyuan” refers to China’s efforts to expand the use of the Yuan in global oil transactions. The shift from Petrodollar to Petroyuan could affect global oil supply and demand, affecting the exchange rates of the US dollar and Chinese RMB. Although research suggests that the RMB may not yet replace the Petrodollar in the short term (Salameh, 2018; Kamel & Wang, 2019; Wagdi & Habib, 2022), economist Zoltar Pozsar believes that it is currently twilight for the Petrodollar and dawn for the Petroyuan (Zoltar, 2022).

In December 2022, Xi Jinping stated China’s commitment to purchase oil and gas in Yuan and use the Shanghai exchange for oil and gas transactions in Yuan (El Dahan & El Yaakoubi, 2022). In March 2023, China’s giant oil company, CNOOC, completed an LNG import transaction with TotalEnergies using Yuan for 65,000 tons of LNG from the United Arab Emirates (UAE) (Hayley, 2023).

Holmes (2023) notes that OPEC members and other BRICS countries are considering the use of the Yuan. Russia, Iran, and Venezuela, which provide about 40% of global oil reserves, can sell their oil in Yuan. Türkiye, Argentina, Indonesia, and Gulf oil producers have also expressed interest in joining BRICS. These developments point to the Yuan’s growing role as a reserve currency, which could shift global power dynamics and give China greater influence in economic policies that affect the world.

The relationship between oil prices and macroeconomic variables, including exchange rates, has received extensive research attention. Studies have shown that changes in oil prices influence economic activities in developed countries (Tabash et al., 2022; Conget al., 2008; Kasongwa & Minja, 2022; Youssef & Mokni, 2020; Yan, 2012; Lvet al., 2020; Lin & Su, 2020; Kumar, 2019). However, research on the impact of these macroeconomic variables, particularly the exchange rate, on oil prices remains underexplored. This research gap represents an opportunity to contribute to the existing literature.

This study aims to investigate the direct effects of US and Chinese PMI, US stock indices, and US and Chinese exchange rates on crude oil prices and examine the moderating effects of US and Chinese exchange rates on the relationships between US and Chinese PMI, US stock indices, and crude oil prices. By analyzing these relationships, this study seeks to provide a deeper understanding of the factors influencing global oil prices. This knowledge can be valuable for market participants, policymakers, and governments to make informed decisions regarding oil pricing and its related economic implications in the US-China context.

The remainder of this paper is structured as follows. The next section reviews the relevant literature on the relationship between oil prices, exchange rates, and other macroeconomic variables. Following that, the methodology section discusses the data and techniques employed in this study. The results section presents the findings of the analysis, and the discussion section interprets them in the context of existing literature and explores their implications. Finally, the conclusion section summarizes the key findings, limitations, and future research directions.

Literature Review

Cheng (2005) found a significant negative correlation, suggesting that a stronger US dollar leads to lower oil prices. This aligns with the findings of Beckmannet al. (2020), who observed a long-term negative correlation between these variables. The underlying explanation lies in the impact of exchange rates on the purchasing power of oil-importing countries. A stronger dollar makes oil cheaper for them, potentially leading to a decrease in global oil demand and consequently, lower prices.

However, the relationship is not always straightforward. Bénassy-Quéréet al. (2007) highlight the role of China’s growing oil demand. When oil prices rise, oil-importing countries like China experience a deterioration in their balance of payments. This pressure can weaken their currencies, including a potential depreciation of the yuan relative to the dollar. However, a weaker yuan can make Chinese exports more competitive, potentially boosting economic activity and leading to increased oil demand—a phenomenon that can counteract the initial price pressure.

Wang and Sun (2017) identified economic activity as the most significant driver, implying that rising economic output translates into higher oil demand and consequently, higher prices. Conversely, economic downturns often lead to decreased demand and lower oil prices. Additionally, wars and political tensions can cause sharp price spikes due to supply disruptions and market uncertainty (Wang & Sun, 2017).

The impact of oil prices extends beyond the oil market itself, influencing various macroeconomic variables. Kasongwa and Minja (2022) discovered that oil prices moderate the effect of inflation on stock market performance. Rising oil prices can fuel inflationary pressures, leading to investor concerns and potentially dampening stock market performance. However, the study suggests that oil price movements can also influence the direction of the stock market, highlighting the complex interplay between these factors. Thorbecke (2019) analyzed the impact of oil price changes on U.S. stock returns before and after the shale oil revolution.

China’s growing economic influence and increasing oil demand have attracted attention in the context of oil prices and exchange rates. Mei-Chih and Chang (2019) examined the relationship between Chinese and US PMI and oil prices, revealing a dynamic interplay between these factors. Bénassy-Quéréet al. (2007) investigated the role of China’s oil demand in influencing the relationship between oil prices and the US dollar. Zhang and Feng (2012) studied the relationship of PMI and GDP in China. Liu and Jiang (2009) discovered that shocks in international crude oil prices have a significant impact on China's economy, influencing factors such as GDP, inflation, and industrial output.

Studies have also explored the impact of oil price shocks on stock markets and exchange rates. Tabash et al. (2022) found that oil prices have a negative impact on stock market indices and exchange rates, particularly during periods of geopolitical conflicts like the Russia-Ukraine crisis (Bagchi & Paul, 2023).

The Petrodollar system, where oil is traded primarily in US dollars, has been the subject of much debate. Wagdi and Habib (2022) surveyed OPEC countries to assess their perspectives on the Petrodollar after the 2022 Russian invasion of Ukraine. This shift reflects a desire to reduce dependence on the US dollar and potentially challenge its global reserve currency status. While some believe that the Petrodollar system may not be replaced in the near future (Salameh, 2018; Kamel & Wang, 2019; Wagdi & Habib, 2022), others see the rise of the Yuan as a potential challenger (Zoltar, 2022; Mathews & Selden, 2018).

The hypothesis for this research is proposed below:

• H1: There is no simultaneous influence of US PMI, US Stock Indices, and Chinese PMI on oil Prices.

While the literature review provides evidence for the individual influence of US and Chinese economic activity (measured by PMI) on oil prices (Mei-Chih & Chang, 2019; Wang & Sun, 2017), there is a lack of research directly examining their simultaneous effect on oil prices. This gap presents an opportunity for further investigation. It would be interesting to explore how the combined effect of US and Chinese economic activity, along with US stock market performance, impacts the global demand for oil and, consequently, oil prices.

• H2: There is no moderating effect of US exchange rates on the influence of US PMI, Chinese PMI, and US stock index on crude oil prices.

The literature review offers some insights into the relationship between exchange rates and oil prices, highlighting their potential to predict each other in the short term (Beckmannet al., 2020). However, it does not explicitly address the moderating effect of exchange rates on the relationship between US PMI and oil prices (H2). Further research is needed to explore whether fluctuations in the US dollar or another currency (e.g., Yuan) influence the impact of US economic activity that is reflected in PMI on oil prices.

• H3: There is no moderating effect of Chinese exchange rates on the influence of the US PMI, Chinese PMI, and US stock index on crude oil prices.

Similar to H2, the literature review offers limited evidence on the moderating effect of exchange rates on the relationship between Chinese PMI and oil prices (H3). While Bénassy-Quéréet al. (2007) highlight the role of China’s growing oil demand in influencing the relationship between oil prices and exchange rates, they do not explore into the moderating effect on the PMI-oil price relationship. Investigating whether exchange rate fluctuations, particularly the Yuan, influence the impact of Chinese economic activity on oil prices would be a valuable addition to the existing research.

Fig. 2 shows the conceptual framework for this research.

Fig. 2. Conceptual framework of the present study.

Methodology

In this study, we investigate the relationship among crude oil prices (Y), US PMI (X1), Chinese PMI (X2), US stock indices (X3), US exchange rates (Z1), and Chinese exchange rates (Z2) over the period from January 2016 to December 2023. We use monthly time series data with a total of 96 observations in the overall sample.

This study aims to provide an understanding of the influence of the US and Chinese Purchasing Managers' Index (PMI) and stock indices on crude oil prices, with a particular focus on how exchange rates from the US and China moderate these relationships. The study focuses on three types of variables: dependent, independent, and moderating variables, with details of each variable described in Table I.

Table I. Summary of Operational Variables

Type	Variable	Detail	Reference
Dependent	Crude oil price (Y)	Brent crude oil	eia.gov
Independent	US PMI (X1)	Manufacturing purchasing manager index in US	macrovar.com
	CN PMI (X2)	Manufacturing purchasing manager index in China	finance.yahoo.com
	US stock indices (X3)	S&P 500	finance.yahoo.com
Moderating	DXY (Z1)	US dollar index	finance.yahoo.com
	RMB index (Z2)	Bank for international settlements (BIS) renminbi index	iftp.chinamoney.com

This research uses the data and builds a multiple linear regression model while the moderation effect of exchange rates is tested using Hierarchical Regression Analysis with SPSS 25.0 for Windows.

Data processing begins with classical assumption tests to ensure the absence of normality, multicollinearity, heteroskedasticity, and autocorrelation issues within the model. If no issues are identified, linear regression analysis and hypothesis testing are conducted.

This method involves two equations. The first equation assesses the main effects, examining the influence of independent variables on the dependent variable. The second equation examines moderation effects on the relationship between independent and dependent variables. To determine the significance of moderation effects, the original equation (without moderation) is regressed. Then, the original equation plus the moderating variable is regressed (Hairet al., 2014).

Both equations are as follows:

(1) $$\begin{array}{r}Y=\mathrm{\sigma }+\mathrm{\beta }1\text{X}1+\mathrm{\beta }2\end{array}$$

(2) $$\begin{array}{rl}Y& =\mathrm{\sigma }+\mathrm{\beta }1\text{X}1+\mathrm{\beta }2\text{X}2+\mathrm{\beta }3\text{Z}1+\mathrm{\beta }4(\text{X}1\ast \text{Z}1)\\ & +\mathrm{\beta }4(\text{X}2\ast \text{Z}1)\end{array}$$

(3) $$\begin{array}{r}Y=\mathrm{\sigma }+\mathrm{\beta }1\text{X}1+\mathrm{\beta }2\text{X}2+\mathrm{\beta }3\text{X}3+\mathrm{\beta }4(\text{X}4)+\mathrm{\beta }4(\text{X}5)\end{array}$$

where

Y – dependent variable

σ – constant

β_1–5 – regression coefficients for each variable

X_1,2 – independent variables

Z₁ – moderating variable

X_4,5 – interaction of independent variables with moderating variable.

Acceptance of the moderation hypothesis depends on how significant the moderating variable’s impact is on the dependent variable. This is determined by examining the regression coefficient or beta (β) resulting from the interaction effect of the independent variable with the moderating variable on the dependent variable. Positive results indicate that the moderating variable strengthens the influence of the independent variable on the dependent variable, while negative results indicate weakening effects.

Partial test (t-test) is used to test whether there is a partial influence of independent variables on the dependent variable. Testing the regression results is conducted using a t-test at a confidence level of 95% or α = 5% with the following conditions:

• If the significance level (p-value) is less than 5% (0.05), it can be concluded that H₀ is rejected and H₁ is accepted.

• If the significance level is greater than 5% (0.05), it can be concluded that H₀ is accepted and H₁ is rejected.

The F-statistic test is used to determine whether all independent variables included in the regression model have a simultaneous effect on the variable. If the significance probability value <0.05, then the independent variables collectively influence the dependent variable. The decision-making basis for the F-statistic test can be seen in the ANOVA table in the processing results by examining the calculated F value and its probability.

Results and Discussion

Based on the results of the classical assumption tests, it is known that the initial data set meets the normality, multicollinearity, and heteroscedasticity tests but does not meet the autocorrelation test. Therefore, data X and Y were transformed twice using the ρ (rho) estimation method based on the Durbin-Watson statistic.

• H1: There is no simultaneous influence of US PMI, US Stock Indices, and Chinese PMI on Oil Prices.

Descriptive statistics are provided in Table II. The statistical analysis results for testing the first hypothesis revealed positive regression coefficient values of +0.889 for the US PMI variable (X1), +0.687 for the Chinese PMI variable (X2), and +0.896 for the US Stock Indices (X3). Meanwhile, the statistical result of the F-test showed that the significance value (sig) is smaller than the significance level (α) = 5% (0.000 < 0.05), thus H0 is rejected, and H1 is accepted, indicating a significant influence of US PMI (X1), Chinese PMI (X2), and US Stock Indices (S&P 500) (X3) simultaneously on Brent (Y).

Table II. Descriptive Statistics Anova Test

Model	Sum of squares	df	Mean square	F	Sig.
Regression	724.664	3	241.555	6.522	0.000b
Residual	3444.223	93	37.035
Total	4168.887	96

The positive regression coefficients obtained for the US PMI, Chinese PMI, and US Stock Indices variables indicate that an increase in these variables is associated with an increase in crude oil prices. This finding suggests that economic activities and stock market performance in both the US and China have a positive impact on oil prices.

The findings of this study are consistent with Mei-Chih and Chang (2019), who found a positive relationship between the Purchasing Manager’s Index (PMI) of the US manufacturing sector and the world crude oil prices. Yuet al.’s (2011) study indicates that changes in domestic and foreign crude oil prices have a notable impact on Chinese manufacturing PMI, while Le and Chang (2015) found that sudden changes in oil prices can affect stock market performance but the extent of this impact may vary depending on several factors.

• H2: There is no moderating effect of US exchange rates on the influence of US PMI, Chinese PMI, and US Stock Index on Crude Oil Prices.

Descriptive statistics are provided in Table III.

Table III. The Comparison of Regression Test Results for Equations (1) and (2)

Variable	B	t	Sig.	Conclusion
X1	0.899	3.011	0.003	Significant
X2	0.687	1.117	0.267	Not significant
X3	0.896	2.538	0.013	Significant
X1 × Z1	−28.735	−19.873	0.000	Significant
X2 × Z1	0.827	2.487	0.015	Significant
X3 × Z1	0.0004	−1.784	0.078	Not significant

Based on the results as shown in Table III, the results suggest that PMI US (X1) significantly influences Brent (Y) with a positive coefficient of 0.889 (p = 0.003 < 0.05). This aligns with Mei-Chih and Chang (2019) findings, linking increased US manufacturing activity to higher crude oil prices. The relationship between US PMI and global crude oil prices can be explained by several factors: first, increased manufacturing activity in the US generally leads to increased energy demand, including crude oil, thus driving up crude oil prices. Second, the US PMI can be seen as an indicator of overall US economic health. Investors can use PMI as a signal to predict future oil demand, which can affect current oil prices.

However, PMI China (X2) does not show statistical significance (p = 0.267 > 0.05), with a positive coefficient of 0.687. This might be possible due to the fact that the dependence of the Chinese manufacturing industry on crude oil is not as great as the need for crude oil in the US manufacturing industry and the energy diversification implemented by China. Second, the Chinese government and refinery pivots implement crude oil inventory and stocking strategies so they will not affect global crude oil demand directly regardless of increases in China’s manufacturing PMI. This contrasts with Mei-Chih and Chang (2019) research, possibly due to differences in data period collection and China’s energy diversification efforts.

In contrast, US Stock Indices (X3) exhibit statistical significance (p = 0.013 < 0.05), with a positive coefficient of 0.896. This supports the idea of a positive relationship between US stock indices and Brent prices, driven by economic activity and investor sentiment. This finding complements the research of Bénassy-Quéréet al. (2007), who found a positive relationship between real oil prices and real dollar prices, and Thorbecke (2019), who stated that there is a link between oil prices and the US stock market that is influenced by increased US oil production. Most research conducted discusses the effect of oil prices on the stock market more often than the reverse effect, and this research can enrich the literature on the relationship between the stock market and crude oil prices.

The positive and significant relationship found between the US Stock Index and crude oil prices can be caused by, among other things: first, an increase in the S&P 500 generally indicates increased economic activity in the United States, and this increase can lead to increased energy demand, including crude oil. This higher demand drives up Brent prices. Second, the stock market and the crude oil market are often influenced by investor sentiment. When investors are optimistic about the economy and the stock market, they are more likely to take risks and invest in high-risk assets, such as stocks.

The interaction term between US PMI (X1) and US Exchange Rate (Z1) is statistically significant (p < 0.05), with a negative coefficient of −28.735, indicating that US PMI moderated by US Exchange Rate (X1 × Z1) has a negative and significant partial effect on Brent (Y). This negative relationship is attributed to the strong USD exchange rate, which increases Brent crude oil prices for non-US countries, thus decreasing global demand.

Similarly, the interaction term between China’s PMI (X2) and the US Exchange Rate (Z1) is statistically significant (p < 0.05), with a positive coefficient of 0.827, indicating that China’s PMI moderated by the US Exchange Rate (X2 × Z1) has a positive and significant partial effect on Brent (Y). Despite the strong USD exchange rate, China’s increased demand for Brent due to economic activity outweighs the negative effect.

However, the interaction term between US Stock Indices (X3) and US Exchange Rate (Z1) is not statistically significant (p > 0.05), with a positive coefficient of 0.0004, indicating that US Stock Indices moderated by US Exchange Rate (X3 × Z1) do not significantly affect Brent (Y). This suggests that the USD exchange rate does not consistently moderate the relationship between US stock indices and crude oil prices, which can vary due to various factors such as investor sentiment and global events.

• H3: There is no moderating effect of Chinese exchange rates on the influence of the US PMI, Chinese PMI, and US Stock Index on Crude Oil Prices.

As shown in Table IV, The PMI US variable, moderated by the Chinese Exchange Rate (X1 × Z2), significantly impacts Brent prices with a negative coefficient. This suggests that when the Chinese Exchange Rate strengthens, it adversely affects US manufacturing competitiveness on the global stage. This finding aligns with Mei-Chih and Chang (2019) study, which highlights the link between US manufacturing activity and global crude oil prices. The possibility is that as the Chinese Exchange Rate strengthens, US products will become relatively more expensive in international markets. Consequently, there is a decline in demand for US manufactured goods, leading to reduced crude oil demand in the US and, subsequently, lower Brent prices.

Table IV. The Comparison of Regression Test Results for Equations (1) and (3)

Variable	B	t	Sig.	Conclusion
X1	0.899	3.011	0.003	Significant
X2	0.687	1.117	0.267	Not significant
X3	0.896	2.538	0.013	Significant
X1 * Z2	−12.487	−11.264	0.000	Significant
X2 * Z2	−10.245	−4.972	0.000	Significant
X3 * Z2	0.044	2.381	0.019	Significant

Similarly, the PMI China variable, moderated by the Chinese Exchange Rate (X2 × Z2), demonstrates a significant negative effect on Brent prices. This result contradicts Mei-Chih and Chang (2019) research, suggesting that oil prices are influenced by Chinese PMI in the long run, especially after China became the world’s largest crude oil importer. The rationale here lies in the dynamics of the Chinese market. When the Chinese Exchange Rate strengthens against the US Dollar, US products become more expensive for international buyers, including China. Consequently, this diminishes the competitiveness of US goods, leading to decreased demand for US products, including crude oil. This might ultimately impact Brent prices negatively.

On the other hand, the US Stock Indices (X3) variable, moderated by the Chinese Exchange Rate (X3 * Z2), has a positive impact on Brent prices. This finding complements the research of Bénassy-Quéréet al. (2007) and Thorbecke (2019), which suggest a positive relationship between real oil prices and real dollar prices, as well as a link between oil prices and the US stock market, respectively. The reasoning behind this lies in the interplay of market dynamics: when the Chinese Exchange Rate weakens against the US Dollar, it enhances the competitiveness of US products in the global market. This, in turn, could potentially increase demand for US manufacturing, including oil, resulting in higher Brent prices.

Further analysis was made by comparing coefficients and significance for each variable X1, X2, and X3 when moderated by the US Exchange Rate and Chinese Exchange Rate, as shown in Table V.

Table V. The Difference Between US Exchange Rate (Z1) and Chinese Exchange Rate (Z2) Effect on X1, X2, and X3

Independent variable	Moderating variable
	Z1		Z2
	B	Sig.	B	Sig.
X1	−28.735	0.000	−12.487	0.000
X2	0.827	0.015	−10.245	0.000
X3	0.0004	0.078	0.044	0.019

The US Exchange Rate (Z1) has a stronger moderating effect on the relationship between PMI US (X1) and Brent (Y) compared to the Chinese Exchange Rate (Z2). This is likely because the US Exchange Rate reflects the strength of the US economy and its purchasing power in the international market, which can directly influence US demand for Brent crude oil.

Chinese Exchange Rate (Z2) has a stronger moderating effect on the relationship between PMI China (X2) and Brent (Y) compared to US Exchange Rate (Z1). This is consistent with China’s significant role in global crude oil demand. A stronger Chinese Exchange Rate makes Brent crude oil cheaper for China, potentially leading to increased demand and higher prices.

Chinese Exchange Rate (Z2) also has a stronger moderating effect on the relationship between US Stock Indices (S&P 500) (X3) and Brent (Y) compared to US Exchange Rate (Z1). This suggests that changes in the Chinese currency index have a more significant impact on the relationship between US stock market performance and Brent oil prices.

Overall, this finding highlights the interconnectedness of China, the US, and the global economy in influencing global crude oil prices. It further emphasizes the growing dominance of the Chinese economy in shaping global economic dynamics.

Conclusion and Recommendation

This study investigated the factors influencing global crude oil prices, focusing on the US and China’s economic activities and exchange rates. The findings reveal that the US PMI, Chinese PMI, and US Stock Indices all have a positive and significant direct effect on Brent oil prices. This indicates that economic growth in both the US and China, along with US stock market performance, contributes to higher crude oil prices.

This study further examined the moderating effects of US and Chinese exchange rates on these relationships. The results show that the US Exchange Rate (Z1) has a stronger moderating effect on US PMI (X1) compared to the Chinese Exchange Rate (Z2). A strong USD makes Brent oil more expensive for non-US countries, decreasing global demand. On the other hand, the Chinese Exchange Rate (Z2) has a stronger moderating effect on both Chinese PMI (X2) and US Stock Indices (X3) compared to the US Exchange Rate (Z1). A stronger Chinese currency makes Brent oil cheaper for China, potentially leading to increased demand. It can also enhance the competitiveness of US products in the global market, boosting demand and prices.

Overall, this research highlights the link between the US, China, and the global economy in affecting oil prices. It also emphasizes China’s growing influence on the world’s economic landscape.

While this research provides valuable insights into the factors influencing global crude oil prices, there are some limitations that affect the generalizability of its conclusions. The chosen data period (2016–2023) may not fully capture the impact of significant events that occurred outside the timeframe. Additionally, the focus on the US and China excludes the influence of other major oil producers and consumers, potentially limiting the generalizability of the findings to the entire global market. Furthermore, the study prioritizes PMI, stock indices, and exchange rates as explanatory variables. While these choices offer valuable insights, other macroeconomic factors, such as interest rates, inflation, or geopolitical tensions, could also influence oil prices and were not explored here. Additionally, alternative statistical methods, such as wavelet analysis, might be employed to capture more complex relationships, especially for the moderating effects. These limitations highlight the need for further research that incorporates a broader timeframe, considers a wider range of countries and economic indicators, and explores the mechanisms influencing oil prices in greater detail.