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The Determinants of Crude Oil Prices Osama M. Badr 1 and Ahmed F. El-khadrawi2 Abstract Understanding the factors driving crude oil price fluctuations is essential for assessing their effects. For this purpose, the study based on the price equation presented by Kaufmann et al (2004) and expand the model to include another set of potential determinants that may reflect the fluctuations in crude oil prices. These determinants classified in to three types of factors: Supply and demand factors and factor associated with the behavior of financial market participants. The study employed quarterly data from 2008Q1 to 2015Q4. Engel and Granger econometric techniques allow us to estimate the short and long-run effects of the previous determinants on the crude oil prices. The results of our analysis indicate a robust effects of GDP growth for both China an OECD countries to crude oil prices fluctuations. Changes in the refining capacity also appear robustly related to oil prices changes. On the other hand, indicate little-to-no evidence that increased role of speculators drives prices. 1. Introduction: After four years of stability at around $105 per barrel, oil prices have declined sharply since June 2014. It is expected that Middle East and oil exporting countries will face slowdown and weakness in economic environment due to sharp drop in oil prices. After four years of stability at around $105 per barrel, oil prices have declined sharply since June 2014. It is expected that Middle East and oil exporting countries will face slowdown and weakness in economic environment due to this sharp drop in oil prices. Especially, the last oil price decline is due to factors related to global economic conditions like changes in oil supply caused by technology Aasim et al (2015). Whereas last changes can reflect a symptom of unexpected shocks in global economic activity. There is a belief that the current decline in oil prices driven by supply factors will have positive effects on global economic growth over the medium term. In spite of that, the severe economic pressures on oil exports countries as well as the uncertainty associated with the occurrence of crisis, may limit the optimistic expectations of the global economy in the short term. There is no doubt that oil price drop lowering consumers cost of living which leads to increased consumer's real income if only price decline have been passed to them. At the same time price drop means decline in firm's marginal costs using oil in their production which implies more benefits. But the flipside to these gains is income losses of oil producers. The effects of these factors not depend on demand and supply factors only but also on the decline type, whether a temporary or permanent. If the oil prices drop on a temporary basis, the 1 Osama M. Badr, Faculty of Commerce, Tanta University, Egypt, College of Business, Umm Al-Qura University, KSA, Email: [email protected] 2 Ahmed F. El-khadrawi, Faculty of Commerce, Damietta University, Egypt,Faculty of Islamic Economic and Finance, Umm Al-Qura University, KSA, Email: [email protected] 1 gains will be in oil importing countries which they will be mostly winner. While the oil exporting countries will lose their expected income which put a pressure on the national budget and lead to increase borrowing and public debt. But if the oil prices drop on a permanent basis especially in case of economy reliance on revenues from oil exports to fund other activities in the economy. Such countries experience a terms-of-trade loss, governments will need to adjust and reconstruct the public spending. From all the above, Identifying the determinants for the oil price decline is critically important for assessing its likely global economic impact. According to that, our aim is to examine which factors are significant in determining crude oil prices between 2008 -2015. Our study organized as follows; Section II. Review the causes behind oil prices fluctuations. Section III presents the economic and financial consequences associated with oil prices fluctuation. Section IV presents the empirical model used, the estimation technique and discusses the empirical results. Section V. brief summary and the important conclusion derived from the study. 2. The causes behind fluctuations in oil prices plunge As per any storable commodity, oil demand and supply conditions determine long-run trends in oil price, however, there are other factors and expectations can play a critical role in driving price fluctuations (e.g. geopolitical conflicts in some oil producing regions and producers decisions specially OPECβs decisions and finally with appreciation of U.S dollar). Theoretically, there are four groups of explanatory factors can be identified as possible contributors to the development of crude oil prices. First, change in demand due to change in global economic growth. Second, changing in supply or anticipating supply. Third, change in objectives and decisions of crude oil producers. Fourth, the factors associated with the behavior of financial market participants and speculation. According to Hamilton (2008) and Dees et al. (2008), the above determinants are not necessarily mutually exclusive, but can complement each other. Kilian (2009) Argue that during the period 1975-2007, oil price shocks have been driven mainly by demand shocks rather than supply shocks. This essentially stems from precautionary demand driven by uncertainty about future supplies. Peersman (2011) discriminate between oil demand shocks and oil supply shocks to study the macroeconomic effects across 11 industrialized countries. The results indicate that for oil demand shocks driven by global economic activity, almost all countries experience inflationary pressure and a temporary increase of economic activity. But when arise in oil prices is caused by oil-specific demand shocks there will be a transitory decline of output. Similarly, Cashin et al (2014) compare between supply-driven and demand-driven oil-price shocks to study the macroeconomic consequences for 38 countries and regions using quarterly data covering the period 1979-2011. The results were similar pretty much to Peersman (2011), in the fact that oil importing countries became more susceptible to oil supply shocks compared to oil exporting countries, while oil demand shocks can't explain cross-country differences. 2 From literature review, we can note that the most important variables that affect the oil price are supply and demand quantity. World economic activity represented by world GDP and advanced economies GDP. Temperature and its influence on oil price. Which we can describe it over the time in the following figures. Fig (1b ) show that Advanced economies represent more than 60% from World GDP and Fig (1b ) show that World GDP and Advanced economies GDP declined from year 2008 to 2009 and start to rose from 2009 to 2014 but re-declined from 2014 to 2015 due to slowdown in advanced economies especially Chinese Economy. 100000 80000 80000 60000 60000 40000 40000 20000 20000 World Advanced economies World 2013 2010 2007 2004 2001 1998 1995 1992 1989 1986 0 1983 2013 2010 2007 2004 2001 1998 1995 1992 1989 1986 1983 1980 0 Fig (1b) World and Advanced Economies GDP 1980 Fig (1a) World and Advanced Economies GDP 100000 Advanced economies Fig (2) show the world economy growth rate fluctuation from year 2008 to 2015 especially the declined in world economy growth year 2009 and 2015. Fig (2) World Economy Growth Rate 0.3 0.2 0.1 0 -0.1 198119831985198719891991199319951997199920012003200520072009201120132015 Fig (3) show the reflect of global climate changing with increasing the global temperature which will lead to increase the oil demand 3 Fig (3) Global Temperature 1 0.8 0.6 0.4 0.2 0 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Fig (4a) show the fluctuation of oil demand & supply but since the first quarter2013 where the world supply increase over the world demand and Fig (4b) show that Non OPEC supply quantity is bigger than OPEC supply quantity which indicate and explain the OPEC weakness to control Oil market over the time 100 Fig (4b) OPEC & Non- OPEC Supply Fig (4a) World Oil Demand & Supply 70 95 60 90 50 40 85 30 20 80 world Demand world supply 10 non-opec supply opec supply 1Q08 3Q08 1Q09 3Q09 1Q10 3Q10 1Q11 3Q11 1Q12 3Q12 1Q13 3Q13 1Q14 3Q14 1Q15 3Q15 1Q08 3Q08 1Q09 3Q09 1Q10 3Q10 1Q11 3Q11 1Q12 3Q12 1Q13 3Q13 1Q14 3Q14 1Q15 3Q15 0 75 Fig (5) shows the crude oil price which reflects the change in above variables. and represent wide price swings in times of shortage or oversupply during the period 1982-2015. 160 140 120 100 80 60 40 20 0 Fig (5) OPEC Oil Basket Price U$/Bbl 4 4. Methodology 4.1The Model To determine the most important factors which influencing crude oil prices, the study based on the price equation presented by Kaufmann et al (2004) and expand the model to include another set of variables that may reflect the decline in crude oil prices based on the quarterly data covering the period 2008-2015. So our model can be expressed as following: πΏππ(π)π‘ = π0 + π1 πΏππ(πΊπ·ππβ)π‘ + π2 πΏππ(πΊπ·πππ)π‘ + π3 πΏππ(π·ππ¦π )π‘ + π4 πΏππ(π πππππ)π‘ + π5 πΏππ(πΆπππ’π‘ππ)π‘ + π6 πΏππ(ππππ)π‘ + π7 πΏππ(πππ. π π‘)π‘ + π8 πΏππ(πππ. ππ)π‘ + π9 πΏππ(ππππ)π‘ + ππ‘ (1) Where (P) is the crude oil prices. (πΊπ·ππβ), (πΊπ·πππ) is the gross domestic product growth of China and OECD respectively. (π·ππ¦π ) is days of forward consumption of the OECD and China crude oil stocks, which is calculated by dividing the sum of OECD and China Stocks of crude oil by China and OECD demands for crude oil. (π πππππ) is the total refining capacity worldwide. (πΆπππ’π‘ππ)is the capacity utilization by OPEC, which denote the rate at which the processing capacities of the available refineries are utilized, and can calculated by divided OPEC production by OPEC capacity. (ππππ) is the average world temperature. (πππ. π π‘) is worldwide oil stocks. (πππ. ππ) is the worldwide oil reserves. (ππππ ) is the Nominal effective exchange rate of the U.S. dollar. (Ξ΅t ) is the error term. All the variables are expressed in natural logarithms in order to estimate their elasticities. 4.2 Data Description and Sources From equation (1), we expect the regression coefficient associated with (πΊπ·ππβ) and (πΊπ·πππ) to be positive β an increase in gross domestic product of china and OECD raises oil prices. The coefficient of (π·ππ¦π ) is expected to be negative, as an increase in oil stocks reduce oil price by reducing reliance on current production and thereby lowering the lowering the risk premium that associated with a supply disruption. We also expect a negative relationship between Refining capacity (π πππππ) and oil prices. If there is a general expansion of refining capacities, bottlenecks caused by growing demand can be countered more quickly. Following this logic, a negative impact on crude oil prices would be the result. On the other hand, if the rate of actual utilization (πΆπππ’π‘ππ) gets closer the total refining capacity limit (which means a shortage in the market), the crude oil prices will raise. We expect a positive relationship between (ππππ) and crude oil prices. We expect a negative effect of (πππ. π π‘) and (πππ. ππ) on prices. Finally, the coefficient of (ππππ) is expected to be negative. The data used in this study obtained from U.S Energy Information Administration (EIA) and IMF website. 4.3 The Estimations Procedure 5 In order to estimate equation (1), we use Engle and Grangerβs (1987) procedure to estimate our model. This method seems appropriate for our study in the sense that it allows us to estimate the long-run and short-run relationship between the crude oil prices and the considered explanatory variables. According to Engel and Granger if the variables in the model is not stationary in level I(0), we will get spurious regression. In spite of that, Engle - Granger believes that there is a possibility to generate a linear combination from non-stationary time series data, and if this linear combination is stationary, the time series is integrated of the same order. Thus, the variables can be used in their level. The focus of the cointegration test is to find out whether this combination generated from the model is Stationary or integrated I(0) by applying the Augmented Dickey Fuller (ADF) test (Dickey and Fuller, 1979) on the residuals. if we reject the null, it means that there is a cointegration between variables, which indicates an existence of long-term equilibrium relationship between the variables of the model. Since the error correction model (ECMs) depends on its first step to carry out the verification process of a long-run equilibrium relationship between the variables (through regression of the cointegration represented by equation (1)) using the application of ordinary squares (OLS), the second step of this test is to estimate the ECMs which reflect the short-term relationship by entering the residual lagged for one period . Accordingly the equation will be as follow: π π π π₯πΏππ(π)π‘ = π0 + β π1π π₯πΏππ(πΊπ·ππβ)π‘βπ + β π2π π₯πΏππ(πΊπ·πππ)π‘βπ + + β π3π π₯πΏππ(π·ππ¦π )π‘βπ π=1 π π=1 π=1 π π + β π4π π₯πΏππ(π πππππ)π‘βπ + β π5π π₯πΏππ(πΆπππ’π‘ππ)π‘βπ + β π6π π₯πΏππ(ππππ)π‘βπ π=1 π π=1 π π π=1 + β π7π π₯πΏππ(πππ. π π‘)π‘βπ + β π8π π₯πΏππ(πππ. ππ)π‘βπ + β π8π π₯πΏππ(ππππ)π‘βπ π=1 π=1 π=1 + πππ‘β1 + ππ‘ (2) In which π₯ is the first difference, and ππ‘β1 is the one period lagged discrepancy (or disequilibrium) term, which estimated from the cointegration equation (1). The number of lags (n) for the right-hand side variables is chosen using the Akaike information criteria. The coefficient Ο in the ECM model measures the adjustment force which should be significant and negative. The negative value for Ο indicates that disequilibrium between crude oil prices and its determinants moves prices toward its long-run equilibrium value. 5. Empirical Results and Discussion Before turning to the estimation results, it is important to make preliminary test which assess whether the variables used in equation (1) are stationary or integrated of the same order I(1) by applying the ADF test. The results in table (1) in the appendix show that all the variables in model 6 (1) are not stationary in levels but stationary in their first difference, which mean that all the variables are integrated in same order I(1), and therefore can be used in their levels. Table (2) in the appendix, shows the results of cointegration equation (1). All the variables have signs consistent with what was expected and, except for Temperature, are significant. The coefficients of πΊπ·ππβ, πΊπ·πππ are positive and highly significant. The estimates imply that a one percent additional increase in gross domestic product for China or OECD countries is associated with around 0.110 and 0.382 per cent increase in crude oil prices. The effect of π·ππ¦π is negative. An increase in stocks reduces reliance on current production, which tends to lower the risk premium that is associated with a supply disruption. One percent point additional increase in days of forward consumption results a reduction in crude oil prices by about 0.225. Refining capacity has a negative effect on the long-run level of oil prices. As we expected, Capacity utilization has a positive impact on oil prices. The signs of the regression coefficients are consistent with a priori expectations that prices drop rapidly at low levels of capacity utilization and rise rapidly at high levels of capacity utilization. As expected, the coefficients associated with the worldwide oil stocks and worldwide oil reserves are negative. One per cent additional increase in oil stocks and oil reserves is associated with of about 0.042 and 0.072 percent in oil prices. Finally, the coefficient associated with exchange rate is negative. The estimates imply that a one percent appreciation in U.S. dollar is associated with a decline of about 0.009. Let us now move to the ECM (equation (2)), Table (3) in the appendix shows the short-run dynamics of oil prices. The regression results indicate that the cointegrating relationship given by equation (1) can be interpreted as an equation for the long-term determinants of oil price. The coefficient of the error correction term, which Known as the adjustment force (π) is negative, and around 0.53, implying that 53 percent of the disequilibrium of the period t β1 is revised in period t. 6. Conclusion Understanding the factors driving crude oil price fluctuations is essential for assessing their effects. For this purpose, the study based on the price equation presented by Kaufmann et al (2004) and expand the model to include another set of potential determinants that may reflect the fluctuations in crude oil prices. These determinants classified in to three types of factors: Supply and demand factors and factor associated with the behavior of financial market participants. The study employed quarterly data from 2008Q1 to 2015Q4. Engel and Granger econometric techniques allow us to estimate the short and long-run effects of the previous determinants on the crude oil prices. The results of our analysis indicate a robust effects of GDP growth for both China an OECD countries to crude oil prices fluctuations. Changes in the refining capacity also appear robustly related to oil prices changes. On the other hand, the speculation factors indicate little-tono evidence that increased role of speculators drives prices. References: 7 1. Kaufmann, R.K, S. Dees, P. Karadeloglou, and M. Sanchez. (2004), βDoes OPEC matter? An econometric analysis of oil pricesβ, The Energy Journal, 25(4), 67-90. 2. Aasim, M. H., Rabah, A., Peter, B., Vikram, H., Thomas, H., Paulo, M., Martin, S., (2015), βGlobal Implications of Lower Oil Pricesβ, IMF staff discussion note, SDN/15/15. 3. Hamilton, J. (2009), βCauses and Consequences of the Oil Shock of 2007β08β, Brookings Papers on Economic Activity (Spring), 215-283 4. Dées, S. Gasteuil, A. Kaufmann, R. and Mann, M. (2008), βAssessing the factors behind oil price changesβ, European Central Bank, Working paper NO. 855. 5. Hamilton, J. (2008), βUnderstanding Crude Oil Pricesβ, NBER, Working Paper 14492 6. Kilian, L. (2009), βNot all Oil Price Shocks are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Marketβ, American Economic Review 99(3), Pp 1053β1069 7. Cashin, P., K. Mohaddes, and M. Raissi. (2014), βThe differential effects of oil demand and supply shocks on the global economyβ, Energy Economics 44, 113-134. 8. Peersman, G. and I. Van Robays. (2012). βCross-country differences in the effects of oil shocks.β Energy Economics, 34(5), 1532-1547. 9. Benes, J., M. Chauvet, O. Kamenik, M. Kumhof, D. Laxton, S. Mursula and J. Selody. (2012), βThe Future of Oil: Geology versus Technologyβ, IMF Working Paper No. 12/109. 10. U.S. Energy Information Administration (EIA),www.eia.gov 8 Appendix Table 1: Unit root test for the variables used in the study Variables πΏππ(π) πΏππ(πΊπ·ππβ ) πΏππ(πΊπ·π0π ) πΏππ(π·ππ¦π ) πΏππ(π πππππ) πΏππ(πΆπππ’π‘ππ) πΏππ(ππππ) πΏππ(πππ. π π‘) πΏππ(πππ. ππ) ππππ Augmented Dickey-Fuller test Level Trend First difference -1.30 No -4.42* (---) (-2.63) -0.80 No -6.42* (---) (-2.63) -2.10 No -5.45* (---) (-2.63) -2.31 No -4.29* (---) (-2.63) -1.33 No -5.81* (---) (-2.63) -2.11 No -6.10* (---) (-2.63) -1.18 No -4.92* (---) (-2.63) -2.02 No -6.94* (---) (-2.63) -1.95 No -4.88* (---) (-2.63) -1.11 No -4.27* (---) (-2.63) Trend No No No No No No No No No No Table (2): Estimates for long-run Price Equation (1) Cointegrating equation: Dependent variable : ππ¨π (π·)π C πΏππ(πΊπ·ππβ)π‘ πΏππ(πΊπ·πππ)π‘ Log(π·ππ¦)π‘ Log(π πππππ)π‘ Log(πΆπππ’π‘ππ)π‘ Log(ππππ)π‘ Log(πππ. π π‘)π‘ Log(πππ. ππ)π‘ Log(ππππ)π‘ Coefficient 22.84 0.110 0.382 -0.225 -0.824 0.021 0.034 -0.042 -0.072 -0.009 π 2 t-Statistic (-5.92)* (6.61)* )5.27)* (-3.28)* (-2.97)** (2.93)** (-1.37) (-2.96)** (-2.72)** (-6.22)* 0.73 2.19 -4.22 (>-2.63 (1%)) 0.614 D.W ADF Breusch-p ο· The numbers in parentheses are t-ratio. *, **, *** denote statistical significance at 1%, 5%, and 10% respectively. ο· D.W refers to Durbin-Watson test for serial correlation. 9 ο· ADF refers to Augmented Dickey Fuller test for stationarity of the residuals. ο· BreuschβP indicates the BreuschβPagan test for heteroscedasticity. Table (3): Estimates for short-run Price Equation (2) ECM : Dependent variable : ππ¨π (π·)π Ξlog(π)π‘β1 ΞπΏππ(πΊπ·ππβ)π‘ ΞπΏππ(πΊπ·πππ)π‘ ΞLog(π·ππ¦)π‘ ΞLog(π πππππ)π‘ ΞLog(πΆπππ’π‘ππ)π‘ ΞLog(ππππ)π‘ ΞLog(πππ. π π‘)π‘ ΞLog(πππ. ππ)π‘ ΞLog(ππππ)π‘ (π) π 2 Coefficient 0.007 0.097 0.237 -0.102 -0.372 0.009 0.010 -0.012 -0.044 -0.002 -0.53 t-Statistic (1.88)*** (2.63)** )3.14)* (-2.46)** (-2.61)** (2.22)** (-1.12) (-2.34)** (-2.51)** (-2.38)** (-4.10)* 0.68 1.72 -6.24 (>-2.63 (1%)) (-37.524) 0.336 0.273 D.W ADF AIC (1) Breusch-p Normality ο· The number in parentheses are t-ratio. *, **, *** denote statistical significance at 1%, 5%, and 10% respectively. ο· D.W refers to Durbin-Watson test for serial correlation. ο· BreuschβP indicates the BreuschβPagan test for heteroscedasticity. ο· Normality refers to the normality test for the residuals 10