The causal relationships between Brent and Urals oil price benchmarks

Transcription

1 18th International Scientific Conference Enterprise and the Competitive Environment, March 5 6, 2015, Brno, Czech Republic The causal relationships between Brent and Urals oil price benchmarks Vladimír Hajko 1* and Jiří Neubauer 2 1*Corresponding author, Department of Econometrics, Faculty of Military Leadership, University of Defense, Kounicova 156/65, Brno, Czech Republic, vladimir.hajko@unob.cz 2Department of Econometrics, Faculty of Military Leadership, University of Defense, Kounicova 156/65, Brno, Czech Republic, jiri.neubauer@unob.cz Abstract The article investigates the relationship between two major oil price indexes the most prominent western index (Brent) and the most prominent Russian oil price index (Urals). The comparison of the most established benchmark with the less well known price index is performed to determine the nature of the price differential and causality between them, using the high frequency commodity market data. Using sample from September 2009 to December 2013 in weekly frequency, there is a significant non-zero price differential (around 1.6 USD per barrel) between the two series, but we attribute this difference primarily to the difference in the oil blend quality. We find that the Russian market can be considered integrated with world oil market. We also find, rather surprisingly, the Urals is weakly exogenous in the VECM model, while Brent responds to changes in equilibrium. Causality tests confirm that the causal relationship likely is from Urals to Brent, rather than vice versa. Keywords: oil price, Urals, Brent, co-integration, causality 1. Introduction Significant oil price changes (sometimes called oil shocks) are often considered an important determinant in the economic growth. Nevertheless, the economic research typically only considers one of the primary benchmarks (Brent, Dubai or WTI) of oil price, most commonly Brent. A significant portion of the oil consumption in Europe (especially in Middle and Eastern Europe) is supplied from the Russian Federation. Furthermore, in economics there is a common view of the crude oil 1 (or most commodities in general) as perfectly homogenous good. The actual daily practice is a bit 1 For the sake of brevity, we will denote crude oil, or petroleum, simply as oil in the subsequent text.

2 Enterprise and the Competitive Environment, March, 5 6, more complicated and naturally differentiates between vast amounts of grades and properties of various so-called oil blends. Apart from the different value of crude oil grades, the existence of regional markets might cause that the prices of textbook example of seemingly perfect substitutes do not correspond to each other as perfectly as the theory might suggest. If the oil markets are not global, but regionalized, we would expect that the differences in the prices will not necessarily be efficiently corrected by arbitrage. In econometric terms, the existence of regionalized markets implies the absence of price co-integration. It is obvious we cannot label any price differential as the evidence of market regionalization. The most apparent reason is the fact there are different specifications for different market contracts. If the product is not delivered to the same place, it is necessary to take into the account the transportation costs. If the transportation costs outweigh the nominal price difference, it is apparent the arbitrage would not be profitable. The other side of the explanation of the price differential is the observable difference in the product itself. As is explained in the text below, the crude oil is typically sold in specific blends. Each blend has slightly different value for a refinery, because the differenced physical and chemical properties influence the ability to transform certain grade of input into various the refined products and ratios of said refined products on the output. The problem investigated in this article is the causal relationship between leading world oil price benchmark (Brent) and the Urals price benchmark. The law of one price implies that for a homogenous good, given it is traded on the competitive market, any two prices of said good should form a relatively stable equilibrium. If a price shock creates a spread between the two prices, market arbitrage will ensure the return to market equilibrium. Therefore, a market spread between any two oil prices should be stationary mean reverting series. A non-zero mean would reflect the transaction costs of the arbitrage (typically representing transportation costs from a different point of delivery) and a difference in quality Oil blends As mentioned above, it can be expected the oils with different quality will be valued differently by the primary customers, i.e. refineries. There are two most frequently used categories (or measures) to differentiate between various blends of oil. Crude oil is typically characterized as either light or heavy and sweet or sour. The distinction between heavy and light oils depends on the so-called API gravity. 2 Generally speaking, oils with API gravity 38 or more are classified as light oils, oils with API gravity between 22 and 38 are referred as medium crude oils and those with API gravity below 22 are classified as heavy crude oils (Neste Oil, 2015). Whether the given oil blend is categorized as sweet or sour, depends on the sulfur contents. Oil with less than 0.42% sulfur is classified as sweet, while the sour oil typically contains more than 0.5% sulfur. For refineries, light sweet crude oil is the most desirable type, as it requires less refining and produces a higher yield of high-value products. In order to facilitate the trade and to avoid ambiguity, the buyers and sellers in the oil market use various price benchmarks (or markers), describing the price of certain blend 2 API gravity compares the weight of oil to water: API gravity greater than 10 indicates the oil is lighter than water and therefore floats on it; API gravity less than 10 indicates it is heavier than water and therefore sinks in it.

3 Enterprise and the Competitive Environment, March, 5 6, of oil. The three most prominent 3 benchmarks are Brent Crude, West Texas Intermediate (WTI) 4 and Dubai Crude 5 (sometimes also called Dubai s Fateh 6 ). It is noteworthy that among these, the Brent has gained the leading position as an international benchmark. On today s markets, the Brent benchmark is the most prominent, and arguably, the most important and most widely used oil price benchmark (EIA, 2010; ICE, 2012). Among the most influential use of the price benchmark is the socalled formula pricing Regionalization of oil markets Regionalization of oil markets refers to a situation when the price setting mechanism in a certain region does not follow the movements of world oil price. This situation might be caused by variety of reasons (typically political and/or strategic). The necessary condition for regionalization however is the absence (or forced prevention) of market arbitrage. In fact the identification of the areas that exhibit either price co-integration or bi-directional causality is one of the crucial steps in the product market delineation in competition economics. Hajko and Bil (2013) is an example of market delineation based on the price co-integration and causality. In a similar fashion, the financial economics also has a long tradition in the investigation of the dependence and causality in the international financial markets (primarily regarding the stock prices, see e.g. Gupta and Guidi, 2012 or Aslanidis, Osborn and Sensier, 2009). In less numerous instances this approach has been applied to the oil markets. Weiner (1991) concluded there is a surprisingly high degree of regionalization in the oil markets, and attributed this evidence to the ability of oil sellers to engage in price discrimination. Gülen (1997, 1999), Ewing and Harter (2000), Bachmeier and Griffin (2006) or Bentzen (2007) argued for the opposite case, finding the evidence for world oil market integration (based on co-integration of the prices). Kleit (2001), Milonas and Henker (2001), Hammoudeh et al. (2008) or Fattouh (2010) also found the evidence for the oil markets integration, but with certain limitations, such as significant transaction costs, asymmetric adjustments or multiple threshold effects in the adjustment process. Reboredo (2011) used copula functions to analyze the dependence structure between four oil benchmarks and argued in favor of the globalization hypothesis with no asymmetric adjustment during market booms or busts. Jacks, O Rourke and Williamson (2011) argued that the absence of commodity market integration is associated with much greater commodity price volatility in the isolated countries. Arguably some of the countries in the world can be considered as special cases of interest for instance one of the largest energy commodities consumers such as China, or one of the most prominent energy commodities exporters such as Russian Federation. ICC (2013) classifies both 3 Apart from Brent, Dubai, and WTI, other popular oil blends are the OPEC Reference Basket, Urals benchmark (traded in Russian Federation), Tapis Crude (traded in Singapore), Bonny Light (Nigeria), Isthmus (Mexico) and Western Canadian Select (Canada). 4 Brent and WTI are light sweet oils (typically with API ratings of 38.5 and 39.6 respectively), typically quoted for the Free On Board delivery at certain major oil hub (typically Amsterdam-Rotterdam- Antwerp (ARA) Hub for Brent and Cushing, Oklahoma (USA) for WTI). 5 Unlike the Brent and WTI, this blend is so-called medium sour oil. As such it might be more relevant for pricing of lower quality oils. However, only a relatively small percentage of Dubai Crude is traded on the spot market, instead term deals are favored. 6 The name makes a reference to the Fateh oil field (meaning good fortune in Arabic), discovered in 1966.

4 Enterprise and the Competitive Environment, March, 5 6, Russian Federation s and China s openness as below average. Liu, Chen and Wan (2013) investigated the price differences between China s and international oil prices using nonlinear correlations. They argue that price co-movement is stronger in the long-term than in the short-term and that the international oil prices influence Chinese oil prices towards the long-term equilibrium level, but not vice versa. No such investigation has yet been done for the Russian oil market. In general, there seems to be a certain consensus in the literature that the world oil market is more or less integrated. If this is the case, the world price innovations then can be manifested in the markets in two ways. If the new information enters all markets simultaneously, all price adjustment is instantaneous on all integrated markets. This condition would imply that the co-integrated series would adjust to the long-run equilibrium, even though the short run dynamics might not be strongly influenced by the lagged values (on condition this adjustment process to new information is not faster than the lowest observable frequency measure in the dataset in reality, this might happen on the intraday markets). It may also be the case the information is primarily reflected on a certain market (probably the most well known and/or most frequently traded) and only consequently this information spreads to the other less popular markets (e.g. traders on these markets intentionally adjust their behavior in response to the changes in the leading benchmarks). If this is the case, the co-integration may or may not be present, but we should be able to at least detect Granger causality (this reasoning, however, strongly implies the unidirectional causality). Nowadays the use of Brent price as the world oil price is a common practice. In this article we compare the Brent oil price benchmark with the less common one, the socalled Urals blend. 7 Our null hypothesis is in essence the integration of Russian market with the world oil market. Given the Brent is higher quality oil blend (more valuable to refineries), we expect the presence of small non-zero differential, which can be viewed as an approximation of the agreed-upon differential in actual traded contracts. Other than that, if there is no significant regionalization of the Russian oil market, the two series should exhibit a common behavior. 2. Methodology and Data The oil price data were collected from the database published by topoilnews.com. Data were collected in the daily frequency, covering the time period September 21 st 2009 to December 31 st 2013 and converted to weekly frequency (by taking average observations for the given calendar week). 8 Given the relatively high number of observations, we opted for 1% level of significance. During this period, the price differential was significantly different from zero, on average US dollars per barrel. We focus on the vector autoregression (VAR) and vector error correction model (VECM) as description of the investigated system. In general, VAR model estimates a system equation for each variable in the vector, with each variable being explained by its own lagged values and the lagged values of all other variables. 7 Urals is categorized as medium, sour crude oil (with API gravity around 32 and sulfur content of approximately 1.2%). Usually, the delivery points are Augusta (Italy) or Amsterdam-Rotterdam-Antwerp hub. 8 We also replicated the estimations using series transformed to natural logs, but we found no substantial change or improvement in the results over the estimation in levels.

5 Enterprise and the Competitive Environment, March, 5 6, Engle and Granger (1987) shown the standard statistical inference is not valid when dealing with non-stationary variables, unless these series are co-integrated. In order to test for co-integration, it is necessary to examine the stationarity of the data first. To test for unit root, we employ the Augmented Dickey-Fuller (ADF, Dickey and Fuller, 1979) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS, Kwiatkowski et al., 1992) tests. To determine the presence of co-integration we use Johansen test (cases 2 and 3, i.e. with restricted constant and with unrestricted constant 9 ). Since there is an evidence for a co-integration between the two series, we subsequently estimate VECM model (for lag order ):! "#$%&' ( -=!. / >8/ /50678/ 4 /50697: ")$*+, ( / 4 ;! "#$%&' (8< ")$*+, (8< <?/ A / A ;B9C DE:! #$%&' (8/ -1FG 0 HIIIIIIIIJIIIIIIIIK )$*+, (8/ LM@NOPQR 1 S /5( S 05( (1) To describe the causal relationship between the two series, apart from the standard inference using Wald tests in VAR, we also opted for the Toda and Yamamoto (1995) testing procedure. This procedure tests the causality of the integrated variables by setting up VAR in levels and augmenting the lag order by the maximum order of integration of the variables, with Wald tests restricting the coefficients of the nonaugmented lag order. 3. Results Table 1 summarizes the unit root tests. We conclude both examined series are (1) so we proceed with the Johansen test the results of which are presented in We can see there is a sufficient evidence for co-integration only for case 2. As mentioned above, since we might reasonably expect a non-zero mean in the cointegration relationship, we proceed with the VECM estimation of Case 2. Estimation of of equation (1) yields the co-integrating relationship (std. errors in parentheses) 1.06! " Urals Brent! =! +! Surprisingly enough, as indicated in Table 3, only (1.266) (0.013) Brent responds to co-integration disequilibrium, with Urals being weakly exogenous. 9 In Equation (1) we apply # $ % ' = * 0, for restricted constant and - = 0 for unrestricted constant. $ & 0

6 Enterprise and the Competitive Environment, March, 5 6, Table 2 (the optimal VAR lag length based on the information criteria was 2). Table 1: Unit root test results AR(p) ADF test p-value KPSS test statistic BRENT Diff BRENT diff URALS URALS, diff KPSS test critical values: 10% % % We can see there is a sufficient evidence for co-integration only for case 2. As mentioned above, since we might reasonably expect a non-zero mean in the cointegration relationship, we proceed with the VECM estimation of Case 2. Estimation of of equation (1) yields the co-integrating relationship (std. errors in parentheses) ! Urals Brent = +. Surprisingly enough, as indicated in Table 3, only (1.266) (0.013) Brent responds to co-integration disequilibrium, with Urals being weakly exogenous.

7 Enterprise and the Competitive Environment, March, 5 6, Table 2: Co-integration, Johansen test results, p-values Trace Test Maximum Eigenvalue Test Case 2, Restricted constant = ! Case 3, Unrestricted constant = ! Causality (block exogeneity) test results are provided in Table 4. Again we can see that the influence could be primarily attributed to Urals, rather than Brent. Table 3: EC term coefficients, weak exogeneity tests Brent Urals EC term, Coefficient values (std. errors in parentheses) (0.180) (0.178) Weak exogeneity restriction test, p-value Table 4: causality test results, p-values Toda and Yamamoto Standard Granger causality (VAR in differences) Brent Urals Urals Brent Discussion and Conclusion Our results are rather surprising and unexpected. Despite the well established position of the Brent as the leading world benchmark, our results show that Urals, not Brent, is weakly exogenous in the (arguably expected) co-integration relationship. In other words, it seems that the price development of Russian oil price benchmark is not influenced by being out of long run equilibrium. Given the expected high competitiveness of commodity markets, this might indicate the data generating process of the Urals oil price is not entirely based on the market forces alone. At the same time, Brent spot market prices do seem to adjust to disequilibrium (although not spectacularly fast (recall the estimation is done for weekly frequency). Based on our results, it seems plausible to claim Russian oil market is not regionalized, but there are some limitations to pure market adjustment. Most likely the case is much more complicated, and a further investigation is required before an argument regarding the position of the Russian oil market is made. Furthermore, it should be stressed that the data available for estimation need to be enhanced to encompass the more recent development in the oil market, which unfortunately were not available to us at the time of writing this article.

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