The decomposition of oil price shocks is crucial for understanding the multifractal nature of price-volume dynamics in crude oil futures. Using the structural vector autoregression (SVAR), this study decomposes the crude oil futures prices into three types of oil price shocks, viz., supply shocks, demand shocks, and risk shocks. The heterogeneous effects of decomposed oil price shocks on the trading volume of the crude oil futures are uncovered based on the multifractal detrended cross-correlation analysis (MF-DCCA). The results reveal significant multifractal characteristics between the oil price shocks and the trading volume changes in the West Texas Intermediate (WTI) futures market. Specifically, demand shocks exhibit the strongest long-range correlation with trading volume, which may be related to the influence of slow-moving variables such as macroeconomic factors, seasonality, and global energy policies on demand changes. Additionally, the greater multifractality observed between supply shocks and trading volume suggests heightened market complexity and risk, possibly linked to recent geopolitical disruptions. Finally, from the standpoint of market efficiency, the crude oil futures market responds the most efficiently to the demand shocks, while its efficiency is the lowest when reacting to supply shocks. This study further decomposes oil price into distinct types of shocks, analyzing the multifractal relationship between price shocks and trading volume, offering a novel perspective for understanding the transactional dynamics of the crude oil futures market. The findings deepen the insights of economic actors engaging in the crude oil futures market into the characteristics of the price-volume relationship and furthermore help them identify the heterogeneous effects of decomposed oil price shocks on the trading volume in their investment decisions and risk monitoring.
Energy is crucial for boosting productivity and fostering global economic growth (Khan et al. 2023). With the diminishing effects of the COVID-19 pandemic, energy demand is gradually recovering. As a key driver of the world economy, fluctuations in oil prices, induced by demand and supply, extreme climate events, production costs, geopolitical events, and so on, shape various global dynamics (Benk and Gillman, 2020; Rajput et al. 2021). Nevertheless, relying exclusively on price fluctuations to gauge market conditions provides only a partial understanding. According to the general equilibrium theory and the Efficient Market Hypothesis (EMH), plenty of market traders and practitioners should adequately engage in transactions of oil to make sure that observed prices can represent the true but unobserved equilibrium price. However, many scholars find that oil trading volume is probably able to provide alternative information of oil price fluctuations of which cannot be gained from historical price itself (Go and Lau, 2020). It has been highlighted that price fluctuations are closely related to changes in trading volume (Ardalankia et al. 2020). The complex and time-varying price-volume relationship of oil provides market traders and practitioners with precious information about the movements of oil potential prices. This relationship can be explored to analyze market risk, market efficiency, and the designs of trading strategies (Chan et al. 2022). Specifically, if trading volume significantly influences oil price fluctuations and contributes to return prediction, it can be utilized by traders for arbitrage and speculative purposes (Girard and Biswas, 2007). Conversely, it reduces the likelihood of inefficiencies in the oil market. Furthermore, if a statistically significant relationship is established between trading volume and price changes, incorporating volume into price forecasting models becomes essential for enhancing estimation accuracy, thereby enabling traders to develop more effective trading strategies (Go and Lau, 2020; Patra, 2024). This, in turn, is particularly relevant for risk management professionals, as models designed to estimate risk must account for price fluctuations (Patra and Bhattacharyya, 2021). Typically, previous studies have documented a positive correlation between oil price and trading volume (Abdullahi et al. 2014; Ciner, 2002), a negative correlation (Ji and Zhang, 2019), or an asymmetric correlation (Alizadeh and Tamvakis, 2016; Ftiti et al. 2019) using various methods. Despite the fact that no general consensus on the price-volume relationship of oil was reached, those studies almost agree that the misinterpretation of the relationship can cause oil markets to be relatively illiquid, thus leading to extreme price fluctuations. More importantly, given the recent turbulence in oil markets, distinguishing various oil price shocks and their relationship with trading volume is crucial for a deeper understanding of market participants’ behavior under such conditions. Therefore, conducting a thorough analysis of the price-volume relationship in the crude oil futures market holds great significance.
Oil price fluctuations are impacted by a multitude of interconnected factors, stemming from oil’s dual role as both a tangible commodity and a financial asset. In addition to core determinants such as global supply-demand imbalances, geopolitical tensions, and inventory levels, investor expectations significantly affect oil price movements as well (Zhu et al. 2021; Zhao et al. 2022). The interaction of these variables results in a highly complex and nonlinear pattern within oil price trends. Among these, long-term oil price movements are most strongly dictated by the fundamentals of supply and demand (Mahajan and Sah, 2025). Moreover, the International Energy Agency’s 2024 medium-term report underscored a deceleration in crude oil demand growth. This deceleration is attributed to several concurrent developments, including swift adoption of electric vehicles, reductions in oil consumption for power generation in the Middle East, and broader shifts within the global economic landscape. Demand shocks can induce irrational behavior among investors (Ge, 2023). Such shocks can lead to heightened emotional volatility, causing investors to overinterpret fluctuations in oil prices. This overreaction may contribute to either optimistic or pessimistic market sentiment, ultimately resulting in adjustments to investment strategies and variations in trading volume. Simultaneously, on the supply side, the expansion of production capacity in the United States and other oil-producing nations in the world is expected to drive significant growth in global oil production over the coming years (Dam et al. 2025). An increase in oil supply typically results in an oversupply situation and a subsequent decline in oil prices. Under the influence of the income effect, such a price drop enhances consumers’ purchasing power, which may lead to higher demand for oil. Conversely, when oil prices rise, the substitution effect prompts consumers to opt for relatively more affordable alternative energy sources (Esmaeili et al. 2024). Furthermore, investors often interpret news related to oil supply dynamics. For instance, supply disruptions caused by unexpected events are generally perceived as negative news, thereby shaping investors’ expectations about future oil prices (Olovsson, 2019). For instance, investors anticipating a price decline may reduce their oil holdings, thus contributing to increased volatility in oil trading volume.
Apart from typical supply and demand fluctuations, extreme external events also play a crucial role in shaping oil price dynamics (Asadi et al. 2022). Such events have short-term but significant impacts on both the production and consumption sides of the market. A notable example is the outbreak of the COVID-19 pandemic, which caused a dramatic decline in global oil demand and heightened market instability. Moreover, persistent geopolitical events in major oil-producing regions often bring about swift and substantial disruptions. The Russia-Ukraine conflict, for instance, disrupted critical energy transportation networks, constrained oil supply, and resulted in a sharp rise in prices. Except as their direct impact on the actual supply and demand dynamics of oil, extreme events also drive changes in market trading behavior by altering investors’ risk preferences. When uncertainty rises due to extreme events, some investors tend to increase their speculative demand for oil (Kilian and Murphy, 2014). The shifts in speculative positions result from the combined efforts of both bullish and bearish investors. On one hand, extreme events, e.g., wars and public health emergencies, can trigger a surge in market panic, prompting some investors to adopt short positions as a risk-averse strategy. On the other hand, certain investors engage in long positions based on arbitrage opportunities, anticipating an increase in oil prices, which in turn contributes to trading volume fluctuations (Jiao et al. 2023).
Overall, oil shocks influence oil price and trading volume dynamics through multiple channels, while fluctuations in trading volume, in turn, amplify the impact of oil shocks on oil prices, particularly during extreme events. A bidirectional causal relationship between crude oil price volatility and trading volume has been established (Haukvik et al. 2024), indicating the presence of a feedback effect (Aboura and Chevallier, 2013). This feedback effect often exacerbates existing price trends, leading to deviations from fundamental values (Kallinterakis et al. 2020). Within this process, investor sentiment plays a crucial role (Tang et al. 2025). During periods of heightened uncertainty, investors are frequently influenced by news reports and market trends, exhibiting herd behavior driven by fear or conformity rather than conducting rational, fundamentals-based analysis. Consequently, significant fluctuations in trading volume may trigger a chain reaction among investors, further amplifying market volatility and adding complexity to market responses. Figure 1 illustrates the mechanism between oil price shocks and trading volume.
The validity of the EMH has been increasingly questioned in light of the complex characteristics observed in crude oil prices. Oil prices often exhibit volatility, nonlinear trends, intricate interdependencies, and self-similarity patterns (Xu et al. 2020). These traits primarily stem from the crude oil futures market’s vulnerability to a wide array of external and internal influences. This complexity is further compounded by the interrelated dynamics of trading volume and oil price movements. A more suitable framework for examining these dynamics is provided by the Fractal Market Hypothesis (FMH). However, conventional econometric approaches often struggle to fully capture these sophisticated features (Eibinger et al. 2024). A major limitation of these approaches is their reliance on linearity and stationarity assumptions, which fail to represent the true behavior of oil markets. In response, researchers have increasingly turned to nonlinear physical methods, especially fractal-based approaches, to analyze non-stationary time series. Notably, multifractal models offer significant advantages in capturing the evolving dynamics and evaluating the efficiency of financial markets (Mandelbrot, 1997). As a result, methods such as Multifractal Detrended Cross-Correlation Analysis (MF-DCCA) have gained prominence in empirical investigations. A growing body of literature has utilized multifractal frameworks to uncover the presence of fractal structures in crude oil futures markets (Yao et al. 2020; Zhang et al. 2021). Recently, multifractal methods have garnered increasing attention due to their ability to extract valuable information from commodity prices, particularly in the context of crisis events (Inacio et al. 2025; Gaio and Capitani, 2025). As a key commodity during periods of crisis, crude oil is inevitably influenced by such events (Bouazizi et al. 2024). The application of multifractal methods allows for the stable capture of dynamic correlations between variables even in periods of heightened market turbulence (Ftiti et al. 2021). Therefore, utilizing multifractal techniques to analyze oil price shocks and trading volume not only addresses the limitations of traditional methods in handling nonlinear and non-stationary data but also provides a more comprehensive understanding of the intricate market dynamics during volatile periods. This approach enhances insights into market risk and efficiency, offering a more robust framework for evaluating the impact of crisis events on oil markets.
Building on this foundation, a substantial body of empirical research has examined the multifractal characteristics of oil prices, their interactions with traditional financial markets, and their implications for market efficiency. However, relatively few studies have examined the multifractal relationship between price and volume, underscoring the need for further exploration in this area. Moreover, as previously discussed, oil price fluctuations are influenced by a multitude of factors, each exerting distinct and sometimes divergent effects on financial markets. Simplifying oil prices as a singular risk factor may overlook the underlying complexity of these dynamics and lead to biased interpretations by investors and policymakers (Kilian, 2009; Ready, 2018). Therefore, differentiating among various types of oil price shocks is essential for a more accurate understanding of crude oil market dynamics, forming a key focus of this study. Finally, while previous studies have provided empirical insights into oil price shocks (see Antonakakis et al. 2017; Kumar and Mallick, 2024), a research gap remains in examining the relationship between oil price shocks and trading volume from a multifractal perspective. Given the potential of multifractal analysis to capture complex market behaviors, further research is needed to explore how different types of oil price shocks interact with trading volume. By integrating multifractal analysis with oil price decomposition methods, this study seeks to provide a more detailed understanding of the nonlinear dependencies between oil prices and trading volume, offering new perspectives for investment strategy optimization and risk management.
Given the above considerations, the further motivation of this study is to contribute to the literature by utilizing the MF-DCCA approach to examine the multifractal relationship between trading volume and three distinct types of shocks, i.e., supply, demand, and risk shocks, which are derived by the original crude oil futures prices using Ready (2018) decomposition method. The analysis reveals that, of the three shocks, demand shocks maintain a more persistent long-range dependence with trading volume. This suggests that underlying market drivers, such as seasonal consumption patterns and shifts in global energy policies, significantly influence oil demand variations. In contrast, supply shocks exhibit a greater degree of multifractality relative to trading volume, indicating elevated levels of market risk. Furthermore, the study identifies that the market efficiency of the crude oil futures tends to be lowest during supply shock events. This inefficiency creates potential opportunities for investors to achieve abnormal returns. At the same time, the inefficiency caused by supply shocks highlights the potential for structural improvements through regulatory actions. Regulators are instrumental in fostering market robustness through enhanced information disclosure, the promotion of innovation in trading infrastructures, and the implementation of efficiency-driven reforms.
This study enriches current academic discussions primarily in two aspects. Firstly, it provides a fresh perspective by addressing the research gap regarding the relationship between oil prices and trading volumes. Although considerable literature has examined the connection between oil prices and volume, as well as the impacts of oil price shocks, a gap remains in understanding the dynamics that link price shocks specifically to trading volume. The lack of such analysis limits our understanding of market dynamics, particularly under volatile conditions. Therefore, to provide deeper insights and contribute to stability and effective risk management in crude oil markets, this paper examines the multifractal interactions between trading volume and oil price shocks. Secondly, distinct from previous studies, this research integrates the MF-DCCA method with the decomposition framework proposed by Ready (2018) based on the structural vector autoregression (SVAR) model. The SVAR model is firstly employed to decompose oil prices into supply, demand, and risk shocks. Subsequently, the MF-DCCA method is used to examine the interactions between oil price shocks and trading volume, further assessing the market efficiency of the crude oil futures. Analyzing the heterogeneous impacts of oil shocks on trading volume broadens existing scholarly discourse and provides valuable practical implications for market participants.
The structure of the paper is outlined as follows: In Section 2, a comprehensive review of relevant literature is provided. Section 3 presents the Ready (2018) method and the MF-DCCA approach. Section 4 describes the sample data and reports the empirical findings. A detailed discussion is offered in Section 5, while Section 6 concludes with a summary of the key insights.
