Dynamic link between liquidity and return in the crude oil market

Okoroafor, Ugochi C.; Leirvik, Thomas Article Dynamic link between liquidity and return in the crude oil market Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Okoroafor, Ugochi C.; Leirvik, Thomas (2024) : Dynamic link between liquidity and return in the crude oil market, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-16, https://doi.org/10.1080/23322039.2024.2302636 This Version is available at: https://hdl.handle.net/10419/321407 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Okoroafor & Thomas Leirvik To cite this article: Ugochi C. Okoroafor & Thomas Leirvik (2024) Dynamic link between liquidity and return in the crude oil market, Cogent Economics & Finance, 12:1, 2302636, DOI: 10.1080/23322039.2024.2302636 To link to this article: https://doi.org/10.1080/23322039.2024.2302636 © 2024 Nord University. Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 14 Feb 2024. Submit your article to this journal Article views: 953 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 FINANCIAL ECONOMICS | RESEARCH ARTICLE Dynamic link between liquidity and return in the crude oil market Ugochi C. Okoroafor and Thomas Leirvik Nord University Business School, Bodø, Norway ABSTRACT In this study, we investigate the dynamic relationship between return and liquidity in the Brent and the West Texas Intermediate (WTI) oil markets. The research utilises daily oil price and volume data and monthly macroeconomic data from January 1, 1996 to April 28, 2023 obtained from the Energy Information Association (EIA), the Organisation for Economic Co-operation and Development (OECD), the Federal Reserve Economic Data (FRED), investing.com, and the International Monetary Fund (IMF). We employ the ARMAX(1,1)-aDCC-GARCH-t(1,1) model to capture time-varying associations between return and liquidity. Our findings reveal a significant impact of speculation on the return-liquidity relationship, which is more persistent in the WTI market. Furthermore, we observe a pattern between the Brent and WTI markets during the study period, which the heterogeneous trader hypothesis can explain. These insights hold implications for policymakers aiming to enhance the crude oil market’s stability, as well as for market traders in developing trading and risk management strategies. ARTICLE HISTORY Received 15 May 2023 Revised 31 July 2023 Accepted 3 January 2024 KEYWORDS Crude oil; market liquidity; speculation; commodities REVIEWING EDITOR David McMillan, University of Stirling, United Kingdom SUBJECTS Economics; Finance; Business, Management and Accounting JEL CLASSIFICATION g14; g15; q02; q41 1. Introduction In this article, we investigate the dynamic relationship between price fluctuations and liquidity in the Brent and WTI oil markets. We aim to understand the factors that drive this relationship and their interactions over time. Our research offers valuable insights into energy market dynamics, particularly the role of speculation and trader heterogeneity in shaping the return-liquidity relationship. This understanding contributes to developing more efficient energy planning, management, and integrated energy systems by informing decision-makers about the factors affecting oil market stability. Numerous studies have focused on liquidity and its association with price and other aspects of the stock market (e.g. Amihud & Mendelson, 1986; Chordia et al., 2000,2008; Hameed et al., 2010; Lo & Hall, 2015; Leirvik, 2022). Research on liquidity in the crude oil market remains limited, with only a few studies examining this microstructure feature in the commodity market (e.g. Haugom & Ray, 2017; Marshall et al., 2012; Marshall et al., 2013; Smales, 2019; Zhang et al., 2019). Furthermore, there is a lack of research exploring the dynamic relationship between liquidity and returns in the crude oil market. Consequently, the factors contributing to the relationship between price and liquidity in the oil market remain uncertain. In this paper, we compute the liquidity of Brent and WTI oil prices and analyze their relationship with returns in the oil market. The importance of examining the interaction between market liquidity and return in the commodity market stems from the vital roles these microstructure features play in both the financial market and the global economy. As identified by Hamilton (2003), commodity price movements serve as indicators of CONTACT Ugochi C. Okoroafor [email protected] Nord University Business School, Universitetsall een 11, Bodø, 8026, Norway. # Presend address: UiT, The Arctic University of Norway, Tromsø, Norway ß2024 Nord University. Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2302636 https://doi.org/10.1080/23322039.2024.2302636 shifts in the macroeconomy and are significant factors in preemptive monetary policy formulation. Market liquidity is crucial when evaluating the quality of a financial market, and the timing of illiquidity is a concern for various stakeholders due to its impact on trading costs and the profitability of trading strategies. Investors diversifying their equity portfolios with oil commodity assets must consider the negative effects of trading costs when re-balancing their portfolios. For oil companies and other traders with physical stakes in the crude oil market, trading costs are essential when assessing the benefits of hedging a physical position in the commodity market. The price-liquidity relationship is vital for determining risk management and trading strategies, timing trade order execution, and evaluating the effectiveness of technical indicators (see Amihud & Mendelson, 1986; Chordia et al., 2000; Huberman & Stanzl, 2005; Kyle, 1985). Consequently, this study explores the dynamic relationship between price and liquidity in the crude oil market while controlling for changes in market fundamentals. We investigate not only the existence of a relationship between liquidity and oil price but also how this relationship has evolved over the study period. We pay close attention to periods of significant fluctuation in this relationship to gain insights into the potential drivers of these changes. This empirical paper fills the current gap in the study of commodity market liquidity. In addition, the design of this study highlights the evolution of the relationship between return and liquidity, which has not been previously studied in any market. This study’s findings not only enhance our knowledge of the crude oil market’s microstructure but also provide critical insights for policymakers, market traders, and researchers, fostering informed decision-making in the energy sector and promoting a more resilient and efficient global energy landscape. The rest of this paper is structured as follows: Section two (2) continues the discussion on the extant literature on this topic, Section three (3) describes the research data and methodology, section four (4) presents and discusses the empirical results, and section five (5) concludes. 2. Literature review Even though the relationship between market liquidity and price movements in financial markets has been thoroughly investigated, there is no consensus on how liquidity affects the returns of different types of assets. For example, the seminal research of Amihud and Mendelson (1986) find that between two otherwise equal assets in terms of cash flows, the less liquid asset has a higher expected return of two assets. This suggests that liquidity is priced in equity stocks. Chen et al. (2007), Lin et al. (2011), and Fontaine and Garcia (2012) find that liquidity risk is priced in corporate bonds and yield spreads. In the equity options market, Christoffersen et al. (2018) find evidence that traders are compensated for the risks associated with holding risky illiquid equity options. While Cakici and Zaremba (2021) and French and Taborda (2018) find a negative relationship between return and illiquidity, suggesting the absence of an illiquidity premium, Ben-Rephael et al. (2015) discovers that stock liquidity has significantly improved in recent decades, leading to a decreased liquidity premium that is no longer significantly different from zero. In contrast, Leirvik (2022) finds no relationship between liquidity variation and returns in the cryptocurrency market. Szymanowska et al. (2014) and Batten et al. (2019) report that oil liquidity is priced, but Leirvik et al. (2017) argues that this liquidity premium does not extend to the aggregate stock market. Even in a small market dominated by energy, there is no relationship between market liquidity and stock returns, implying that time-varying liquidity in oil prices does not translate into returns for companies reliant on oil as their main source of profitability. In light of these contradictory findings, this paper aims to investigate the dynamic relationship between liquidity and returns in the crude oil market. Numerous models and hypotheses attempt to explain the relationship between price and liquidity, as well as the reasons for variations in this relationship across financial markets over time. In these models, liquidity is a proxy for other unobserved market traits, such as trader heterogeneity, information asymmetry, and market sentiment, which influence price and liquidity beyond the market’s fundamentals. Wang (1994) proposes trader heterogeneity as a trait represented by market liquidity, explaining priceliquidity dynamics in the equity market. As market traders differ in their information sets, capital constraints, risk appetites, and the resulting trading strategies they adopt, market dynamics change depending on the predominant type of trader at any given time. 2 U. C. OKOROAFOR AND T. LEIRVIK Behavioural asset pricing theory and the impact of heterogeneous traders on a market’s microstructure have gained popularity in the literature. For example, Baker and Stein (2004) demonstrates how market members’sentiment about expected returns affects liquidity and, consequently, the price-liquidity relationship in the equity market. Liu et al. (2023) reveals the existence of a significant liquidity premium in the Chinese stock market, attributable to firm-specific news sentiment. In addition to return predictability, Beschwitz et al. (2020) shows that market sentiment impacts liquidity and trading behaviors. Several other studies highlight how the behavior of heterogeneous investors may sustain incorrect prices and influence demand for financial instruments at different points (see, for example, Chau et al., 2016; Lee et al., 2020; Schneider, 2022; Zheng et al., 2018). Concerning heterogeneity in trading strategies, empirical findings suggest that the relationship between price and liquidity is influenced by the trading strategies adopted by different market traders. For instance, DE Long et al. (1990) posits that when noise traders use a positive feedback strategy—buying when the market price rises and selling when it falls—they create positive causality from price to market liquidity. When employing a speculative trading strategy, other traders act as contrarians in the market, trading against recent price movements. This results in different dynamics between actual price movements and liquidity changes, with an increase in market liquidity being associated with adverse price movements (Bloomfield et al., 2009; Lee et al., 2020). Pereira and Zhang (2010) and Batten and Vo (2014) suggest that the activities of traders who adapt their trades to market liquidity conditions or prioritize diversification influence the dynamics of price and liquidity in the equity market. Extending the findings from these studies to the oil market suggests that heterogeneity and market sentiment, which cannot be directly observed, may be inferred by the price-volume relationship and how it changes over time. Empirical studies on price and liquidity in the oil commodity market support some of the aforementioned hypotheses to varying degrees. For instance, Moosa and Silvapulle (2000) find evidence supporting the noise trading model in the relationship between price movements and volume in the WTI market. Manera et al. (2016) find that speculation of non-financial traders affects price volatility in the energy markets, although its impact on the price-liquidity relationship is not analysed. Haugom and Ray (2017) finds evidence of the heterogeneous trader hypothesis and its impact on the relationship between the number of trades and return distribution in the commodity market. Alfano et al. (2020) find that speculative noise traders contribute to the volatility in oil prices by overreacting to language sentiment in the news about oil market fundamentals. During periods of high volatility, Liu et al. (2021) find that irrational trading behaviour contributes more to illiquidity in the Chinese commodity futures market than the activities of fundamental/rational traders. Kang et al. (2020) finds evidence of liquidity premia in the commodity market, which differs based on the trading strategies adopted by the different types of traders within the market. In general, few studies have examined the relationship between price and liquidity in the commodity market compared to other financial markets. The few that exist have focused on trading volume as a measure of liquidity, and the method of analysis applied does not capture the time-variant nature of this relationship. Although volume is an integral aspect of market liquidity, Jones et al. (1994); Chordia et al. (2000) suggest that it becomes less informative when separated from trading frequency and does not account for transaction costs and price impacts of trading. In this study, we examine the priceliquidity dynamics in the crude oil market, focusing on the price impact of illiquidity. Furthermore, we capture any short and long-run effects of significant shocks to the crude oil market. Controlling for oil market fundamentals, we examine the changes in this relationship over time and test to what extent the aforementioned hypotheses explain the variations in the price-liquidity relationship. Due to the strong link between inflation, the macro economy and the commodity market identified in seminal studies, macroeconomic factors are vital considerations when attempting to understand the nature of the commodity markets in general, and the price-liquidity dynamics of the commodity market in particular. Macroeconomic factors such as exchange rate fluctuations, interest rates, GDP growth, foreign direct investment, and other monetary policy announcements have also been identified as significant influences on demand and supply conditions within the economies and the oil market, see, for example, Browne and Cronin (2010), and Durguti et al. (2021). Furthermore, Akram (2009) find that commodity prices increase in response to the dollar’s depreciation. Ratti and Vespignani (2013) show that increases in the M2 money supply of BRIC countries accounted for an increase in real oil prices owing to its positive impact on liquidity from the demand side. Moreover, Kilian (2009) provides a structural COGENT ECONOMICS & FINANCE 3 decomposition of oil price shocks into the aggregate oil demand and oil supply, with the residuals being accounted for by precautionary demand shocks. In this study, we model the impact of speculative non-market fundamentals on the price-liquidity relationship by controlling for the macroeconomic variables which affect the fundamentals of the crude oil market. We hypothesise that there is a significant residual impact on price and liquidity after controlling for these macroeconomic fundamentals, which is accounted for by the impact of speculative/non-fundamental factors. 3. Data and methodology 3.1. Data 3.1.1. Crude oil return In this study, we utilise daily price and volume data from Brent and West Texas Intermediate (WTI) nearmonth futures contracts as proxies for the crude spot market, from January 1, 1996, to April 28, 2023. The data set range is sufficiently large enough to capture recent and relevant events in the crude market, such as the global financial crisis of 2008–09, the 2014 oil glut, the aftermath of the global shutdown due to the Covid-19 pandemic, and the global turmoil due to the Russian war in Ukraine during 2022–23. Missing values or seemingly erroneous observations are checked manually. For example, some days are registered with Open, High, Low, and Close prices equivalent to the previous day’s closing value combined with zero volume. We assume this is an erroneous observation, and we have deleted 12 observations were this was the case. For days with changes in prices, as well as prices being different than the day before, but no volume registered, we used the previous two day’s average volume. This was the case for 115 observations. We end up with 6975 observations for WTI. Using the same methodology for Brent, we end up with 7001 daily observations. We obtained data for the WTI and Brent Crude Oil Financial futures contract, primarily traded at the New York Mercantile Exchange (NYMEX) from investing.com, and quality checked the data against observations retrieved from the Federal Reserve Economic Data (FRED). However, the FRED data only have prices for WTI and Brent, and not volume traded. Nevertheless, the prices obtained from investing.com were identical to the FRED observations. The observations for the volume variable had a few missing values for WTI. The WTI price is for a contract of 1000 US barrels, or 42000 US gallons, of WTI crude oil. The minimum tick size of the contract is $0.01 per barrel ($10 for contract), and the contract price is quoted in US dollars. The Brent contract is cash-settled based on the ICE Brent crude oil Index price. In contrast, WTI contracts are physically settled, linking their future prices closely to spot prices. Both contracts are sold at 1000 barrels per unit and have their contract prices quoted in USD. Prices from both crude oil markets serve as benchmarks for pricing other crude products globally, making them suitable proxies for the crude oil market. Daily crude oil price data is aggregated to monthly frequency to match the frequency of the macroeconomic variables used as control variables in this study. The relative monthly change in the oil price is computed using log-returns, scaled by 100 to represent percentages: Rit ¼ln Pit Pit−1  100 i¼brent,wti (1) 3.1.2. Crude oil liquidity The level of illiquidity within the commodity market is measured by the price-impact measure (ILLIQ) of Amihud (2002). Amihud’sILLIQ is preferred over trading volume in this study as Jones et al. (1994) suggest that trade volume may become less informative about liquidity variation once separated from trading frequency. Further, Marshall et al. (2012) identify that Amihud’s measure provides the most accurate representation of transaction costs when examining liquidity in commodity markets. However, this price-based liquidity proxy is limited by its tendency to underestimate the actual price impact within the commodity market, as identified in Marshall et al. (2012). The monthly ILLIQ estimate is given as: 4 U. C. OKOROAFOR AND T. LEIRVIK ILLIQiT ¼1 TX T t¼1 jRtj DVolt (2) where Rtis the daily absolute percentage oil price change, DVoltis the daily dollar trading volume, and Tis the number of trading days in the month. Despite being widely used in research, Amihud’s measure of illiquidity, ILLIQ, primarily focuses on the price impact component of liquidity and may not fully capture other aspects of liquidity, for example, the bid-ask spread. Consequently, it provides a partial picture of overall liquidity conditions. Specific market microstructure characteristics, such as trading rules, market depth, or order book dynamics may also influence ILLIQ. Despite these drawbacks, this measure of liquidity has several positive sides, as it can easily be compared across assets, is easily understood, and takes only positive values. The ILLIQ measure has also been widely adopted and extensively used in numerous empirical studies, which makes it easier to compare results to one another. In this study we utilize the ILLIQ measure because it provides a straightforward and intuitive way to capture the price impact component of liquidity, allowing us to examine the relationship between liquidity and asset prices and assess potential liquidity risks, as well as comparing our results with recent findings in the literature. 3.1.3. Fundamentals of the crude oil market In studying the relationship between price and liquidity in the crude oil market, we recognise the influence the market’s fundamentals may individually and jointly have on these variables. Based on the decomposition of oil price shocks by Kilian (2009) and other notable studies which have identified the macro-economic factors which influence oil market fundamentals such as Hamilton (2003); Ratti and Vespignani (2013); Kang et al. (2016), we include aggregate demand, aggregate oil supply, exchange rate, and money supply as external regressors in the conditional mean equation of the GARCH model to account for their impact on the relationship between oil price changes and liquidity. The weekly US Field Production of crude oil (oilprod) is used as a proxy for aggregate supply in the oil market and was obtained from the Energy Information Association (EIA). The weekly oilprod dataset spanning February 16th 1996–March 26th 2021, was transformed to monthly frequency to match the frequency of other variables used in this study. Due to the unavailability of global oil production data at the desired frequency, we have utilised data from the US market which is readily available. We apply the monthly Index of Global Economic Activity (igrea) derived by Kilian as a proxy for aggregate demand in the oil market. The igrea is computed as the weighted and inflation-adjusted percentage change in the single-voyage ocean shipping freight rates for several bulk dry cargoes. This business cycle index is superior to the proxies of aggregate demand used in other studies for several reasons. Unlike global GDP, the igrea is available at a higher (monthly) frequency and is immune to the increasing influence of the service sector on the real GDP of most countries. Unlike industrial production, the igrea is a leading indicator of the demand for commodities, as shipping rates for commodities may be seen as an indicator of a firm’s future production. The US Nominal Effective Exchange Rate (neer) index, which measures the dollar value (USD) against a weighted average of several other foreign currencies, is the proxy for the exchange rate in this study. An increase in the neer of the dollar indicates that the dollar has appreciated relative to several other currencies. As the majority of trades in the global economy are denominated in the dollar, a change in the dollar neer index has a strong influence on the demand and supply of commodities. The monthly neer data was obtained from the International Financial Statistics of the International Monetary Fund. The importance of money supply on the fundamentals of the oil market was identified in Ratti and Vespignani (2013) where an increase in the money supply of China and India was associated with an increase in global oil production and real aggregate oil demand. We broaden the scope within this study and control for the impact of global money supply on the commodity market. As a proxy for the global money supply, we utilise the Money Supply (m1) for the OECD countries, which consists of currency (banknotes and coins) and overnight deposits. The monthly m1 data was obtained from the OECD database. Aside from the igrea, all control variables have been transformed by taking their logarithmic difference to ensure stationarity before their inclusion in the regression model. This is based on the recommendation by Kilian and Zhou (2018) that the igrea is a business cycle index which must not be differenced or transformed in any way. COGENT ECONOMICS & FINANCE 5 The descriptive statistics for the variables under study are presented in Table 1. On average, the Brent market exhibits higher returns than the WTI market, though the WTI market experiences greater fluctuations and price changes during the sample period. The maximum and minimum values reveal that the WTI market has higher peaks and lower troughs than the Brent market, which is also evident in Figure 1.Figure 2 contains the time series plot for the control variables. The ILLIQ estimates for each market suggest that illiquidity has a more significant price impact in the WTI market compared to the Brent market. The distribution of price impacts due to illiquidity in the WTI market exhibits higher peaks and lower troughs than those in the Brent market. Furthermore, the fluctuations in the ILLIQ values for both markets reflect the events in the crude oil market such as the Global Financial Crisis and the initiation of lockdown restrictions in early 2020 The skew, kurtosis, and Augmented Dickey-Fuller (ADF) test statistics indicate that the return and ILLIQ series are not normally distributed, but they are trend stationary. Furthermore, the Ljung-Box and ARCH-LM tests reveal the presence of autocorrelation and heteroskedasticity in the return and illiquidity time series, suggesting that GARCH models are appropriate for this dataset. The degree of unconditional correlation between the variables has been estimated using the Pearson correlation test, and the results are presented in Table 2. The Pearson coefficient for the correlation between oil price changes and illiquidity is negative for the Brent and WTI markets, which suggests that a reduction in the level of illiquidity within the market accompanies positive oil price movements. There is a negative (positive) relationship between the exchange rate and return (illiquidity) in both the Brent Table 1. This table presents the descriptive statistics for the variables studied. All variables have been transformed by taking the logarithmic price difference except for the Index for Global Real Economic Activity (igrea). The mean and standard deviation values for Brent and WTI returns have been annualised. The values of the returns for Brent and WTI, Nominal Effective Exchange Rate (neer), Money Supply (m1), Oil Production (oilprod), and Global Real Economic Activity (igrea) are given in percentages, while the illiquidity (ILLIQ) series have been multiplied by 100. The ADF is the Augmented Dickey-Fuller test for stationarity, Q(5) is the Ljung-Box test for autocorrelation and ARCH (5) tests for the presence of ARCH effect up to 5 lags. (,) denote significance at the 1% and 5% levels. Descriptive statistics Mean Std.Dev Max Min Skew Kurtosis ADF Q (5) ARCH (5) Brentret 5.7005 31.1395 22.9488 −49.7617 −1.0199 3.1634 −11.445 31.509 39.643 BrentILLIQ 0.1141 0.1673 1.0227 0.0026 2.2019 5.9246 −4.4973 1149.1 234.98 WTIret 5.1496 34.9048 54.4478 −60.0585 −0.8705 7.6797 −12.127 24.482 188.73 WTIILLIQ 0.0553 0.0760 0.4586 0.0026 2.0944 4.8808 −5.0114 1073 192.5 neer 0.0790 1.2701 6.4856 −3.7891 0.3932 1.6391 −11.676 45.695 16.657 m1 0.9650 4.7767 85.9818 −1.2480 17.3053 304.8411 −11.499 4.7302 0.0210 oilprod 0.1950 2.9329 15.2878 −24.8563 −2.9088 31.3087 −14.334 4.4736 18.822 igrea 3.6076 66.6258 189.0402 −161.8379 0.6242 0.0570 −3.6012 1131.6 274.59 Figure 1. Return and illiquidity in the Brent and West Texas Intermediate (WTI) crude-oil market. 6 U. C. OKOROAFOR AND T. LEIRVIK and WTI markets. This supports the conclusion of Sadorsky (2000); Akram (2009), which shows that an increase in the exchange rate is associated with an adverse change in oil price and increased illiquidity in these markets. As indicated in Ratti and Vespignani (2013) and Kang et al. (2016), the correlation between money supply and price movements in the crude oil market is positive. Interestingly, the correlation between money supply and illiquidity is also positive, which indicates that increases in money supply are associated with an increase in the price impact of illiquidity in the Brent and WTI markets. The correlation coefficient between oilprod and the variables indicates that an increase in aggregate oil supply is associated with a reduction in oil price and illiquidity in the crude oil market. The reasons for this includes: Supply and demand dynamics: When oil supply increases, the availability of the commodity in the market is greater, leading to a better balance between supply and demand. This increased availability eases the pressure on prices and improves market liquidity by facilitating more trading opportunities. Lower transaction costs: With greater oil supply, the cost of trading (i.e. bid-ask spreads) may decrease as market participants can find counterparties more easily. Market efficiency: As oil supply increases, the market becomes more efficient in reflecting new information, resulting in a more stable and liquid environment. This stability reduces the price impact of illiquidity as the market can better absorb fluctuations in supply and demand. Reduced price volatility: An increase in oil supply can lead to reduced price volatility, as there is more certainty about the availability of the commodity. With less price volatility, the market is less likely to experience abrupt changes in liquidity, which would otherwise exacerbate the price impact of illiquidity. In the case of the igrea, which represents aggregate demand, the Pearson correlation coefficient indicates that an increase in aggregate demand is associated with positive price movements and reduced illiquidity in the crude oil market. Figure 2. Time series plot of the Nominal Effective Exchange Rate (neer), Money supply (m1), oil production (oilprod) and the Index for Global Real Economic Activity (igrea). Table 2. This table presents the Pearson correlation between the variables. ILLIQ refers to the Amihud illiquidity measure. The neer, m1, oilprod and igrea refer to the nominal effective exchange rate, money supply, US Oil production, and Kilian’s index of Global Real Economic Activity. (,,) denotes significance at the 1%,5% and 10% levels. Unconditional correlation coefficients Brentret BrentILLIQ WTIret WTIILLIQ neer m1 us.oilprod igrea Brentret 1.0000 −0.0763 0.9285 −0.1166 −0.3958 0.0913−0.1209 0.1463 BrentILLIQ 1.0000 −0.0771 0.9429 0.0712 −0.0035 −0.0852 −0.1873 WTIret 1.0000 −0.1033−0.3851 0.2533 −0.1157 0.1329 WTIILLIQ 1.0000 0.09190.03710 −0.0859 −0.2218 COGENT ECONOMICS & FINANCE 7 Disclosure statement The authors report there are no competing interests to declare. About the authors Ugochi C. Okoroafor. Doctoral Candidate at Nord University Business School. Research conducted in the field of financial economics and commodity markets. Thomas Leirvik. Associate Professor at Nord University Business School. Preferred field of research is financial economics and climate econometrics. References Akram, Q. F. (2009). Commodity prices, interest rates and the dollar. 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