{'fraction_non_alphanumeric': 0.03585144012127337, 'fraction_numerical': 0.0230166750884285, 'mean_word_length': 4.795168374816984, 'pattern_counts': {'":': 0, '<': 16, '<?xml version=': 0, '>': 10, 'https://': 3, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Determinants of the WTI-Brent Price Spread Revisited

We apply autoregressive distributed lag regression (ARDL) and several methods of structural break analysis on a daily data set between 1995 and 2014 to explore various supply and demand factors as drivers of the price differential between WTI and Brent crude oil. In line with previous literature, we identify a major break in the WTI-Brent spread in December 2010. The ARDL regression reveals that the convenience yield, as a proxy for crude oil inventories, is the most important spread determinant. Moreover, also the trading activity in crude oil paper markets, shipping costs, as well as the stock market development in the US and Europe affect the size of the spread. Unlike other papers, we find that the impact of the spread determinants changed after the break in 2010. Especially, the impact of local WTI inventories as well as the influence of paper markets activity on physical trading in crude oil spot markets have gained in importance. In summary, the rising variability in the spread time series after 2010, which reflects a decoupling process of WTI and Brent, can be explained by an absolute increase in several economic determinants.


Introduction
Due to its paramount importance for the global economy, crude oil is the world's most actively exchanged commodity. However, not all traded crude oil barrels are alike. There are multitudes of varieties and blends from hundreds of different oil production spots located all over the world. 1 The wide heterogeneity of crude oil grades is a challenge for a unique market price and hence also for its marketability. Thus, traders define a group of crude oil benchmarks as pricing reference. The most dominant global benchmarks for (light) crude oil are West Texas Intermediate (WTI) and North Sea Brent crude (Brent). 2 Although the quality characteristics of Brent and WTI are quite similar, WTI is a bit lighter making it more valuable for refining (Energy Intelligence Group, 2011).
The local market price of different crudes, as any other tradable good, is determined by supply and demand. If price differences between various local markets are large, demand and supply from one market may spill over to another market. This concept of market integration was first proposed by Adelman (1984) and the globalization hypothesis for global oil markets. The level of market integration depends on the interchangeability between regional markets and transaction costs (Kleit, 2001), capacities for transporting oil to the next steps in the supply chain, and the ability of suppliers to change the supply line (George and Breul, 2014). As an implication of integrated oil markets, the price of crudes with similar quality characteristics, like WTI and Brent, should manifest a co-movement, such that the price spread is more or less constant over time (Fattouh, 2010b). Fig. 1 depicts the WTI and Brent crude oil spot prices, as well as their price spread 3 .

<<<INSERT FIGURE 1 HERE >>>
Until 2008, WTI and Brent prices moved closely together with a relatively constant average spread around $1.3 per barrel. Accordingly, WTI's price historically traded at a premium against Brent, which approximately represents the transportation costs of moving oil form the North Sea to refineries along the U.S. Golf Coast (Kaminski, 2014). Between 2006 and 2010, the volatility of the WTI-Brent spread increased, which is illustrated by alternating periods with positive and negative price spreads. Since 2010, WTI and Brent decoupled resulting in an increasing discount of WTI against Brent that peaked at 1 For example, the International Crude Oil Market Handbook (Energy Intelligence Group, 2011) lists more than 190 different crude oil streams. 2 WTI is a blend of several American domestic crude streams with its major trading spot in Cushing, Oklahoma. Brent oil covers four crude streams pumped in the North Sea. 3 In this paper, the WTI-Brent spread is defined as WTI spot (futures) price -Brent spot (futures) price. a maximum spread of -$29.59 per barrel in September 2011. By the end of 2015, the disparity of the benchmark prices shrunk to about $1 per barrel. However, since the second half of 2017, WTI is again traded at a continuous discount and the spread re-widened again. Accordingly, the price gap between WTI and Brent has not yet found its equilibrium state.
The substantial volatility of the WTI-Brent spread has real economic and policy implications, as firms and governmental institutions rely on global oil benchmarks to define their business strategy and energy policy. When global markets are integrated, producers with the lowest costs provide the supply and consumers can rely on the signaling and allocation function of prices (Kleit, 2001). Otherwise, if local prices diverge and spreads are volatile, transaction costs increase and the balancing mechanism across markets diminishes (Gülen, 1999). Moreover, depending on whether oil markets are fragmented or unified, local policy interventions may have regional or global effects (Weiner, 1991). Moreover, different authors argue that the wide price spread illustrates that WTI no longer reflects the world oil supply-demand balance and thus loses its eligibility as global oil price benchmark (Bentzen, 2007;Fattouh, 2010a;Kaufmann and Ullman, 2009). In addition, abnormal WTI-Brent price differences result in revenue declines of U.S. oil producers and other oil-exporting countries, because oil imports to the U.S. are priced on the basis of WTI (Janzen and Nye, 2013). If, as a consequence, upstream oil producers lower their production levels, also the transport volumes and thus midstream sector's (esp., transport operators) earnings decrease as well. Additionally, the negative WTI-Brent price gap affects contractual agreements where WTI is the reference price and thus also the instruments used for financial risk management (Kaminski, 2014).
These implications make the examination of the WTI-Brent spread evolution to an intensively discussed topic in academic research, media, and political debates. The academic literature's focus is on the analysis of structural breaks in the WTI and Brent price time series and the cointegration among both prices (e.g., Chen et al., 2015;Fattouh, 2010b;Hammoudeh et al., 2008;Kao and Wan, 2012). In contrast, only few authors, like Büyüksahin et al. (2013) as well as Milonas and Henker (2001), examine the driving forces of crude oil price differentials, but without analyzing the impact of structural breaks on the spread determinants. This paper re-examines a broad set of supply and demand factors as drivers for the variation in the WTI-Brent price spread. Thereby, we combine the previous literature on structural break analysis and the determinants of price differentials. Based on a daily data set for the time period between 1995:01 and 2014:07, we identify structural breaks in the time series and estimate an autoregressive distributed lag (ARDL) model to analyze how the impact of the spread determinants changed. Our findings confirm a major structural breakpoint in December 2010. The most important spread determinant is the convenience yield, which is a proxy for local crude oil inventories. Also the trading activity in crude oil paper markets, shipping costs, as well as the stock market development in the US and Europe affect the size of the spread. Unlike other papers, we find that the impact of the spread determinants changed after the break in 2010. Especially, the importance of WTI inventories as explanatory factor and the influence of paper market trading on the physical spot market heavily increased after 2010. Moreover, weak evidence can be found that shipping costs are less relevant after the break. These outcomes underpin that, after 2010, the WTI-Brent spot spread is largely driven by WTI inventories and pressure from the paper markets. The increasing importance and differences in the spread determinants, as well as the amplified variability in the WTI-Brent price gap reflect the decoupling process of the two leading oil price benchmarks.
The remainder of this paper is organized as follows. In section 2, we present a brief overview of the literature. Section 3 explains the data set and the determining factors of the WTI-Brent spread, followed by the methodology outlined in Section 4. The results of the empirical analysis are presented in section 5, followed by a discussion in Section 6. Section 7 concludes.

Literature review
There is a wide range of literature on the integration of global crude oil markets (e.g., Gülen, 1997;Gülen, 1999;Kleit, 2001;Weiner, 1991). Especially two research streams are related to this work, which are summarized in Tab. 1.

<<<INSERT TABLE 1 HERE >>>
The first stream analyzes structural breaks in the time series of major oil price differentials. Bentzen (2007) apply Perron (1998, 2003)'s test for multiple structural breaks and document a disruption in the WTI-Brent price spread in November 1999, which is approximately the time of OPEC's policy intervention to bring prices into a target zone of $22-$28 per barrel. In contrast, Büyüksahin et al. (2013) use the Chow (1960) structural break test and identify two major structural changes in the WTI and Brent price differential in November 2008 and December 2010. Moreover, they disentangle the WTI-Electronic copy available at: https://ssrn.com/abstract=3305692 Brent spread in a landlock and a transatlantic spread. Through this approach, they prove that the structural break in 2008 is due to a change in the landlock spread, while a second break in 2010 appears specific to Brent crude. The structural break in late 2010 is confirmed by several authors like Chen et al. (2015), Leybourne et al. (2007), Liu et al. (2016), as well as Ye and Karali (2016). In addition, when employing the rolling Chow test by Hansen (1997), Li et al. (2015 find a breakpoint in early 2010. The second stream explores the determining factors of crude oil price differentials. Milonas and Henker (2001) use the convenience yield as proxy for the local availability of crude oil. Within an OLS regression framework and daily observations for the period between 1991:02 and 1996:01, the authors show that the convenience yield is a statistically significant factor for the magnitude of the price spread between WTI and Brent futures contracts with two and three months to maturity. In a further study, Bacon and Tordo (2004) conclude from an OLS regression over the period from 2003:01 through 2004:06, that price differentials among 56 different crudes can be explained by quality characteristics (gravity, sulfur content, and acidity), as well as by transport costs. Moreover, Büyüksahin et al. (2013) adopt an ARDL model to explore the predictive power of three variable groups (macroeconomic fundamentals, physical-market conditions, and financial factors) on the observed spread between nearby futures on WTI and Brent crude oil. Based on daily futures prices, but monthly macroeconomic and oil production data between 2004:04 and 2011:12, they find that the climate of the U.S. economy impacts the WTI-Brent spread level. However, the negative sign of the coefficients is often counterintuitive.
Among the physical factors, storage problems in Cushing are a significant factor indicating a decreasing WTI-Brent spread when storage bottlenecks occur. Moreover, there is some evidence that greater Taken together, the previous literature provides evidence that the price gap between WTI and Brent is alternating over time with a major structural break in 2010. Moreover, recent publications document Electronic copy available at: https://ssrn.com/abstract=3305692 that several factors beyond quality differences are drivers of crude oil price differences. In this study, we extend both aforementioned streams by (i) analyzing the change in the spread determinants before and after structural break(s), (ii) investigating the transmission channel between physical and paper markets for crude oil, (iii) combining a wide set of spread determinants including new variables, which have not been examined before (e.g., extreme weather conditions in the Atlantic Ocean and North Sea), and variables that have been investigated separately (e.g. the convenience yield in Milonas and Henker (2001) and other supply and demand factors in Büyüksahin et al. (2013)).

Data
For the variables used in this study, we obtain daily data for the time period between January 01, 1995 and July 23, 2014. This observation period extends previous studies, like Milonas and Henker (2001), and includes more recent events in global oil markets. In contrast to previous studies, for example Büyüksahin et al. (2013), we only consider spread determinants with data available on a daily basis.
Hence, we match daily observations of the WTI-Brent spread with daily data of the explanatory variables. Tab. 2 provides an overview of the variables used in this study.

WTI-Brent price spread
We use opening spot prices for WTI crude (from Datastream) and closing spot prices of Brent oil (from Bloomberg). Through this approach, we account for the time lag of trading hours between U.S. and European markets (analogously to Milonas and Henker, 2001). We define the WTI-Brent spot spread as the WTI crude oil price " #$% minus the Brent price " &'()" . 4 For the further analysis, we use the normalized spread " , which smooths the time series and reduces heteroscedasticity:

Convenience yield
Following Milonas and Henker (2001), we investigate the convenience yield as proxy for crude oil availability. This interrelation is derived from the theory of storage (among many others, Kaldor, 1939;Working, 1927Working, , 1949, which implies that the convenience yield is inversely related to inventories. 5 A high convenience yield indicates low storage volumes and thus a high level of supply risk. Consequently, we assume that an increase in WTI's convenience yield widens the price spread between WTI and Brent (and vice versa for Brent).
For daily estimates of the convenience yield, we follow the cost-of-carry hypothesis (Brennan, 1958).
Accordingly, commodity futures prices are equal to the spot price plus the convenience yield and the cost-of-carry represented by the storage costs (e.g., tanker rates, pipeline costs, and insurance fees). In its continuous form, the cost-of-carry pricing formula of a WTI or Brent future ",$ • is given by: • 9:; <,= >,? <,= • @?
where " • denotes the corresponding spot price of WTI or Brent, ",$ represents the risk-free interest rate from to , ",$ • denotes the storage costs of WTI or Brent, and ",$ • is the convenience yield of WTI or Brent. − is the time to the contract's maturity. By rearranging Eq.
(2), we receive the convenience yield: Convenience yields are derived from WTI and Brent crude oil futures with three months to maturity.
In addition, futures contracts with six and twelve months to maturity are used for robustness analysis.
Futures price data is obtained from the New York Mercantile Exchange (NYMEX) for WTI and the Intercontinental Exchange (ICE) for Brent. As risk-free rate of return, we use three months U.S.
Treasury bill yields from the Federal Reserve System's website. For better comparability, discrete interest rates are transformed into continuous rates. For the storage costs, we follow Ederington et al.
(2012) and assume the 'fairly common rough estimate' of $0.40 per barrel per month. In line with Stepanek et al. (2013), storage costs are measured as logarithmic difference between the storage fee and daily spot prices. For robustness analysis, we also test inventory-dependent storage costs.
Due to the non-existence of a continuous three months futures contract, we construct a contract or respectively a convenience yield with the desired maturity. Our preferred method is an approach applied in term structure estimation of yield curves (e.g., Martellini, 2003). In the first step, we calculate the convenience yield from the observed future prices according to Eq. (3). In the second step, we interpolate all data points (maturity and convenience yield) with the help of a cubic spline function. In the last step, we evaluate the spline function at the respective maturity. This is our preferred methodology due to the nonlinear construction of the futures price for the required maturity. As robustness test, we define and test alternative roll-over strategies.

Trading volume and open interest
As liquidity in derivatives markets might affect spot market segmentation, we follow Büyüksahin et al. (2013)  interest and trading volume on the respective spot price, which therefore increases (decreases) the WTI-Brent spread.

Freight rates
The transportation costs of crude oil determine its interchangeability between physical markets and thus also its price gap. Transportation costs are mainly driven by the freight rate for shipping. Freight rates increase with declining shipping space and port capacities. Thus, when freight rates are high, it is more costly to ship crude oil from Europe to North America 6 (Lanza et al., 2005), which negatively affects liquidity of intercontinental crude oil markets. As a consequence, the balancing mechanism between regional physical markets are hampered if transportation costs are high. Thus, we expect an increasing absolute price spread between WTI and Brent when freight rates rise. As proxy variable for the crude oil freight rates, we define as the Baltic Dry Index, which is a broad index for different goods, vessel types, and routes. The data for the freight rate index is retrieved from Bloomberg. As fright rates influence the absolute spread, they are pre-multiplied by the sign of the spread. In addition, we also use the Baltic Dry Dirty Tanker index ( ), which is a dedicated index for shipping of crude oil and thus a more precise measure of the transportation costs for crude oil. For the empirical analyses, 6 Exports of WTI crude oil from North America to Europe can rather be neglected due to rigid export restrictions for over 40 years by the U.S. government until January 2016.
we report results for both indices, since the time series for the is only available since 2002, whereas is available for the entire sample period.

Economic conditions
On the demand side, we consider the economic situation in North America and Europe as drivers of the WTI-Brent spread. A growing economy is accompanied by increasing industrial demand for essential commodities, which causes rising prices in local crude oil markets. Accordingly, we expect an increasing (decreasing or even negative) price difference when the U.S. (European) economy grows. As a proxy for the industrial crude oil demand, we construct an aggregated oil-consuming industry stock index for the U.S. and Europe. We use performance index time series for different (super)sectors from the Industry Classification Benchmark (ICB), which were chosen with regard to their high crude oil consumption. In particular, we consider the following sectors: chemicals (ICB-1300), construction & materials (ICB-2300), automobiles & parts (ICB-3300), as well as the supersectors utilities (ICB-7000) and industrial goods (ICB-2700). Data for the different indices is retrieved from STOXX. We then construct S, and TS as arithmetic mean of the five aforementioned indices for the U.S. and Europe. Both time series are normalized with a value of one at the starting point of our sample on January 01, 1995.

Heating and cooling
Besides crude oil consumption for production purposes, crude oil demand is also affected by households and their crude oil needs for heating and cooling. While directly used for heating, crude oil is indirectly connected to cooling through electricity production. As heating and cooling both increase crude oil demand, we expect them to widen the price gap between the two local oil prices. To account for crude oil consumption for heating and cooling, we create two temperature indices. For the U.S.
( S, ), we take the arithmetic mean of the daily temperature in the following cities across the continent:

Methodology
We apply the ARDL methodology developed by Pesaran and Shin (1999) to explore the long-run relationship between the WTI-Brent spread and its determinants. The ARDL model has been frequently employed in empirical research due to several preferable aspects (among many others, Ahmad and Du, 2017;Büyüksahin and Robe, 2014;Ozturk and Acaravci, 2013). First, it allows to disentangle the shortrun and long-run dynamics of the regressors. Second, the ARDL model generates unbiased estimates for the long-run effects even if some of the model regressors are endogenous (Narayan, 2005). Third, the ARDL bounds test by Pesaran et al. (2001) is more flexible compared to other traditional cointegration techniques, like Johansen and Juselius (1990), as it does not require the input variables to be integrated of the same order. Indeed, the ARDL bounds test can deal with mixed order of integration.
Thus, it can even be employed if some variables are stationary in levels, i.e. they are (0), and others are stationary in first differences, i.e. (1). Finally, the ARDL procedure allows that the variables in the model may have different length of optimal lags, which is not possible for conventional cointegration approaches.
In the ARDL model, the normalized WTI-Brent price spread " is regressed over its own lags, as well as present and lagged values of the spread determinants. The ARDL( , H , … , ) ) model with optimal lags is estimated via ordinary least squares and can be described by an unrestricted error correction representation:  (2018).
If a long-run relationship exists, the error correction model can be re-formularized as follows: where "@H is the correction term and indicates the speed of adjustment to the long-run equilibrium after a shock in the system. If the value of the bounds test falls in between the two critical bounds, the results for cointegration are inconclusive and the decision depends on the error correction term "@H (Banerjee et al., 1998;Kremers et al., 1992). When "@H is significant with a negative sign, it implies a long-run cointegration among the estimated variables. Otherwise, cointegration must be neglected.

Summary statistics and unit root test
Tab. 3 presents summary statistics for the WTI-Brent spread and the spread determinants.
<<<INSERT  (0) and (1). Since none of the variables is (2), the requirements for the ARDL model can be confirmed.

Structural break identification
For the structural break identification, we (i) determine the number of potential breakpoints, (ii) detect the dates when the time series changes abruptly, and (iii) analyze the robustness of the breakpoints while controlling for the WTI-Brent spread determinants.
In the first step, we perform Hansen (2000)'s test for breakpoint detection, as it accounts for heteroscedasticity through conditional distributions and, compared to other approaches like the Chow test, does not foster the identification of an excessive number of breaks. This method identifies three potential breakpoints: two are significant at the 1% level, a third breakpoint is significant at the 5% level.
In the second step, we apply several parametric tests to locate the position of the three breakpoints (Bai and Perron, 2003;Busetti and Harvey, 2001;Harvey and Mills, 2003;Lee and Strazicich, 2001;Perron, 1997;Zivot and Andrews, 1992 Perron, 1997;Zivot and Andrews, 1992). The general idea is to include a crash dummy 10 , a shift dummy, or a trend in the autoregressive equation of the inspected variable, which is in our case the WTI-Brent spread. In the Zivot and Andrews (1992)'s test, the minimum t-statistic of the regression coefficient of the lagged variable is the relevant test statistic. Perron (1997) suggests the maximum absolute t-statistic of the regression coefficient of the breakpoint dummy variable. Lee and Strazicich (2001) use the Schwarz Bayesian criterion to identify the optimal breakpoint. In addition, we also conduct a minimum BIC search to find the optimal break.
All models show the similar three breakpoints. 11 However, our favorite model by Lee and Strazicich In the third step, we include the determinants of the WTI Brent spread as controls in the autoregressive equation. Hence, we receive a time series of residuals, which are tested for the structural breaks. Again, we find a major breakpoint in December 2010. 10 Due to the large number of observations in our daily time series, we define the crash dummy to be one in a period of 10 days. 11 Results of the structural break analysis are reported in Appendix A in the online supplement.

Cointegration analysis
After the breakpoint detection, we analyze the WTI-Brent price time series via cointegration techniques. To find the optimal lag length, we follow Toda and Yamamoto (1995). For the cointegration analysis, we set up a VAR model as follows: (1 − ) " = "@H + H (1 − ) "@H + ⋯ + l (1 − ) "@l + f + H + " , where " = ( " , " ) $ . To test for the long-run relationship among WTI and Brent crude oil prices, we apply the cointegration tests by Engle and Granger (1987)  bound. Accordingly, we can infer that there is strong evidence for a long-run economic relationship among the regressors and the WTI-Brent price spread in the full sample period and in the period before the break in 2010. In contrast, the evidence for cointegration is weaker after the break, as both the estimate of the F-test and the t-test fall in between the critical bounds of (0) and (1). This finding indicates that the relationship between the determinants and the spread changed after 2010.

ARDL model: Long-run elasticities
Tab. 7 presents the long-run elasticities of the ARDL model. As model diagnostics indicate that we cannot reject the null hypothesis of no serial correlation from Durbin's test and also not the null hypothesis of a constant variance in the residuals from Breusch-Pagan test for heteroscedasticity, we apply Newey-West procedure for robust standard errors (Newey and West, 1987). We also report the interaction terms of the spread determinants and the 2010 break dummy for all variables driving the balancing mechanisms between the physical spot market and the paper markets ( #$% , &'()" , #$% , &'()" , and ).

<<<INSERT TABLE 7 HERE >>>
In Model (1), we see a strong and statistically significant impact of the WTI and Brent convenience yield on the spread values, whereas the coefficient is positive for WTI and negative for Brent. Hence, high levels of the WTI (Brent) convenience yield increase (lower) the size of the spread. As the convenience yield is an indicator for inventories, our findings imply that the storage levels of the two crudes are strong drivers of the spread. In addition, we can see that the trading volume in Brent paper markets explains variation of the spread. Due to the negative sign of the long-run coefficient, extended trading of Brent futures lowers the price differential between WTI and Brent. Moreover, the elasticity estimates for the economic condition reveal that an increase in the U.S. stock index widens the spread, while a surge in the European stock markets decreases the size of the price gap. Finally, there is also evidence that heating periods in the U.S., as a proxy for increased energy demand for cooling, influence the size of the price spread. However, the sign is contrary to the expected positive impact.
Electronic copy available at: https://ssrn.com/abstract=3305692 Regarding the impact of the structural breakpoints, we observe in Models (1) and (2)  In addition, we can also see that transportation costs measured by the Baltic Dry index ( ) have no explanatory power. In contrast, when using the Baltic Dirty Tanker index ( ) in Model 6, which is the better proxy for the transportation costs of crude oil, we find that increasing shipping costs enlarge the spread. This might be due to the fact that when shipping from Brent oil to the U.S. coast becomes more expensive, it will be more difficult to reduce price differentials between both crudes by transporting it from one physical market to the other. However, as discussed in Section 3, a disadvantage of this alternative measure is that the time series shortens as data is not available for the entire sample period. Summing up, we confirm the results from the bounds test and find evidence for a change in the underlying dynamics of the spread determinants after 2010.
Model (7) directly tests the interactions among the physical market (spot) and the paper markets (futures) by including the WTI-Brent futures price spread as an explanatory factor in the ARDL model.
The results imply a positive impact from the paper markets on the spot markets. This effect reinforces strongly after the break in 2010, which indicate pressure from the paper market on the physical spot market.
Tab. 7 also reports the coefficients of the error-correction term "@H from Eq. (5), which measures how quickly variables converge to the equilibrium. As the coefficient is statistically significant with a negative sign in all models, we can confirm the presence of an established long-run relationship in the full sample. The significant speed of adjustment suggests convergence of the model dynamics from short-run to a long-run equilibrium. Deviations from the equilibrium are corrected by 2.93% per day in Model 1 up to 10.63% in Model 7.

Robustness analysis
In addition to the ARDL results presented in the previous section, we performed various tests for the robustness of our results with alternative variable definitions and model specifications. The results are reported in Appendix B and C of the online supplement.
First, we extend the estimation of the convenience yield by two alternative methods. First, we construct a futures contract with three months to maturity by the weighted average of the two futures prices with lower and higher maturity. The weights are defined linearly as difference between three months and the maturity of the respective future divided by the maturity difference between both (higher -lower maturity). This implies a daily roll-over from the short-term future to the long-term future (Hammerschmid, 2018;Szymanowska et al., 2014). Second, we simply use the future with the closet maturity to three months to calculate the convenience yield (Geman and Nguyen, 2005;Gibson and Schwartz, 1990;Milonas and Henker, 2001). When applying these alternative methods, the ARDL results remain stable and we can confirm the previous findings from Tab. 7.
Second, we include the term spread (defined as difference between the futures and the spot price) as an alternative proxy for the convenience yield. The findings confirm the outcomes from the direct examination of the convenience yield, with a significant impact of the Brent and WTI term spread, whereas the latter increases after 2010.
Fourth, as extension of Model (7) in Tab. 7, we include the futures price spread (3, 6, and 12 months to maturity) instead of the spot price spread as dependent variable and treat the spot price spread as explanatory factor. This analysis shows that the impact of the physical market on the paper market ( ,V: X›oe< = 1.0388 * * * ) is stronger than vice versa ( ,V: •ž<ž--= 0.6792 * * * ), which coincide with previous evidence, for example, by Lautier et al. (2018).

Discussion
Consolidating the different regression models leads to the conclusion that the WTI-Brent spread is positively correlated with the convenience yield obtained from the three months WTI futures contracts and negatively correlated with the convenience yield of the Brent futures contracts, both to a strong significant extent. This finding is in line with the theory of storage according to Working (1927Working ( , 1949 and Brennan (1958). Accordingly, the convenience yield is a surrogate for the availability of a commodity and a high convenience yield implies a critical supply situation, which leads to higher spot prices and thus to a larger spread. Although this finding in line with Milonas and Henker (2001), who find a significant impact of WTI convenience yield at the 10% or 5% level (depending on the maturity of the futures used for the spread calculation), we obtain higher effects in terms of significance levels (all at the 1% level and even lower) as well as economic significance as shown by the long-run elasticities from the ARDL estimation. This could be reasoned by the differences in the data sample. Milonas and Henker (2001) examine the period of 1991 -1995, a time when the WTI-Brent spread was more or less negligible. Another difference is that we apply an alternative method for the computation of the convenience yield and also include other supply and demand factors as controls in the ARDL model.
Moreover, we can conclude from the interaction terms with the break dummy that the relevance of the convenience yield for WTI strongly increases after 2010. Accordingly, the spread reacts more intensively to changes in the WTI inventory levels, which is not surprising due to the extreme storage conditions arising from a glut of crude oil in Cushing. This oversupply of U.S. crude oil was also stimulated by the shale oil revolution (Kilian, 2016). As a consequence of increased fracking, the U.S. oil production rates reversed and started to increase in 2008 after a long-standing decline (EIA, 2017).
However, pipeline systems in the U.S. were originally designed to transport imported crude oil from the Gulf Coast to the interior of the country, and not the other way around to transport shale oil from the interior, where fracking sites are located, to the Gulf Coast (Kilian, 2016).
Comparing the findings for the other spread determinants with the previous literature shows some interesting differences. Similar to Büyüksahin et al. (2013), we find a significant effect of the U.S. economy on the WTI-Brent spread. However, the sign of the effect is different. The positive effect resulting from the ARDL model in Tab. 8 is in line with the predicted sign, but in contrast to the negative sign reported in Büyüksahin et al. (2013). An explanation could be the difference in the variable definition, as Büyüksahin et al. (2013) use an U.S. Business Climate index, whereas we create a dedicated stock index for oil-dependent industries. In addition, we also measure the impact of the European economy on the spread, which was included in previous studies. For the variables measuring the extent of paper markets trading, Büyüksahin et al. (2013) document a significant effect for the open interest in both WTI and Brent markets. This result cannot be confirmed by our results. Instead, we find explanatory power for the trading volume as proxy for paper market activity. In addition to that, we analyze the forces driving the crude oil market segmentation. To proxy the trade barrier, we use the Baltic Dry and the Baltic Dry Tanker index. While Büyüksahin et al. (2013) find no effect of shipping rates, we find indeed a positive impact of the tanker index. This implies that high freight rates cause higher barriers to transport oil and consequently higher WTI-Brent spreads. This underpins the importance of low transportation costs to balance the supply and demand across regional markets. On the other hand, higher freight rates coincide often with situations of economic prosperity leading to higher economic scarcity of crude oil, causing oligopolistic local structures and higher price differences.

Conclusion
This study investigates the determinants of the price differential between Western Texas Intermediate For the structural break analysis, we apply several methods and detect a major break in December 2010, which is strongly in line with previous literature. From the cointegration analysis, we find that the strong long-run relationship between WTI and Brent becomes weaker after 2010. The results from impulse response analysis suggest that Brent reacts stronger to WTI shocks than vice versa. However, after 2010 there is no more response of WTI to a Brent price shock illustrating the decoupling of the two major crude oil variants. The results from the ARDL model imply that the convenience yield, as proxy for crude oil inventories, is the most important spread determinant. Moreover, we find a strong positive influence of the WTI convenience yield, which is an indicator for the crude oil supply situation in the U.S., on the WTI-Brent spread. There is also a strong negative influence of the Brent convenience yield. Both results are in line with the theory of storage, i.e. low storage volumes and thus high convenience yield strongly influence the size of the price gap between WTI and Brent. Moreover, also the trading activity in crude oil paper markets, transportation costs for crude oil shipping, as well as the stock market development in the U.S. and Europe determine the size of the spread. Unlike other papers, we find that the impact of the spread determinants changed after the major break in 2010. Especially, the importance of WTI inventories as explanatory factor for the WTI-Brent and also the influence of the paper market on the physical spot market heavily increased after 2010. As the convenience yield is by nature a function of the future prices, our findings underpin the hypothesis by Fattouh (2010b) that derivatives foster a 'great pool' oil market. In a similar vein, we find evidence that the balancing mechanism between both crudes changed over time and paper markets became more important for equalizing price differentials between WTI and Brent.
While we focus on WTI and Brent as crude oil classifications, an interesting subject for future research would be the examination of the determinants of alternative crude oil spreads like the price gap between WTI or Brent and Dubai crude. In addition, it would be interesting to add further spread determinants, as for example, a measure for the shale oil production volume. However, this requires the availability of daily production data to match them with the daily observations of the spread and the determinants examined in this study. Another possible direction for further research is whether the same variables, which drive the WTI-Brent spread, also affect other commodities (e.g., cacao or wheat).  Büyüksahin et al. (2013) 2006:06 -2012:07 Chow test SB: 2008:11, 2010:12 Argua (2015 2001:01 -2014:05 Bai-Perron test for multiple structural breaks SB: 2003:01, 2005:01, 2007:01, 2009:02, 2011:02 Chen et al. (2015 1988 : (Dickey and Fuller, 1981) and PP is the Phillips and Perron (1988) unit root test. The lag order for the tests are select using the Schwartz-Bayesian Information Criterion. All unit root tests regressions include an intercept and drift. The tests for the spot and futures spread also include a structural break component.

Figure 2. Impulse response functions
The figure depicts the orthogonalized impulse response function of Brent on WTI (left column) and WTI on Brent (right column) for the first 10 days after a one standard deviation shock in WTI (Brent) spot price. The first row refers to the entire time period (January 01, 1995 -July 23, 2014), the second row refers to the time period before the structural break (January 01, 1995 -December 08, 2010), and the third row refers to the time period after the structural break (December 09, 2010 -July 23, 2014). Dashed lines represent 99% confidence bands.