Oil Price Forecasting Using Crack Spread Futures and Oil Exchange Traded Funds

Given the emerging consensus from previous studies that crude oil and refined product (as well as crack spread) prices are cointegrated, this study examines the link between the crude oil spot and crack spread derivatives markets. Specifically, the usefulness of the two crack spread derivatives products (namely, crack spread futures and the ETF crack spread) for modeling and forecasting daily OPEC crude oil spot prices is evaluated. Based on the results of a structural break test, the sample is divided into pre-crisis, crisis, and post-crisis periods. We find a unidirectional relationship from the two crack spread derivatives markets to the crude oil spot market during the post-crisis period. In terms of forecasting performance, the forecasting models based on crack spread futures and the ETF crack spread outperform the Random Walk Model (RWM), both in-sample and out-of-sample. In addition, on average, the results suggest that information from the ETF crack spread market contributes more to the forecasting models than information from the crack spread futures market.


Introduction
Crude oil is an important energy commodity that is vital for most economic activities. Consequently, numerous studies have been devoted to modeling and forecasting crude oil prices. In recent years, the link between the crude oil and refined product markets has been addressed often in the energy economics literature. In particular, several studies have examined the existence of a long-run equilibrium relationship between the prices of crude oil and refined products (Asche, Gjolberg, & Völker, 2003;Gjolberg & Johnsen, 1999;Haigh & Holt, 2002;Lanza, Manera, & Giovannini, 2005;Serletis, 1994). The emerging consensus from these studies is that crude oil and refined product prices are cointegrated. As a result, we should be able to forecast future oil price movements based on information from the refined product markets. However, only a few empirical works have investigated the ability of refined product prices to forecast the price of crude oil (see, for example, Gjolberg & Johnsen, 1999;Lanza et al., 2005;Murat & Tokat, 2009). A deeper understanding of the relationship between the crude oil and refined product markets and of the predictive power of the refined products are indeed worthy of investigation and may carry important implications for energy consumers, producers, investors and policymakers.
In this study, we explore the link between the crude oil spot and refined product derivatives markets and examine the ability of the refined product derivatives prices to predict movements in the crude oil price. In oil and energy markets, the profits of oil refiners, the major participants in the markets, depend largely on the crack spread (the difference between the price of crude oil and the prices of refined products -typically gasoline and heating oil). Because the demand from oil refiners, whose production decisions are tied directly to the crack spread, largely affect the price of crude oil (Verleger, 1982;Verleger, 2011), it is possible that there is a long-run equilibrium relationship between crude oil and crack spread derivatives prices. This study therefore examines the existence of long-run equilibrium price relationships between the crude oil and crack spread derivatives markets. Specifically, we explore the equilibrium price relationships between (i) the OPEC crude oil spot and crack spread futures and (ii) the OPEC crude oil spot and Exchange Traded Fund (ETF) crack spread. The Granger causality is then used to analyze the lead-lag relationship and determine whether the crack spread derivatives prices are useful for forecasting the movements of crude oil spot prices. Finally, we compare the forecasting ability of the two crack spread derivatives with that of a conventional Random Walk Model (RWM).
Our paper contributes to the existing literature by enriching the understanding of the dynamic relationships between crude oil spot and refined product derivatives prices in the following ways. First, unlike many previous studies, we focus on the prices of crude oil spot and crack spread derivatives. With the notable exception of Murat and Tokat (2009), our study is among the first to examine the link between the crude oil spot and crack spread derivatives markets and to investigate oil price forecasting models based on information from the crack spread derivatives markets.
Unlike Murat and Tokat (2009), we analyze not only the futures market but also the ETF market. Thus, the findings from this study also have implications regarding which market contributes more to the forecasting models. In addition, while Murat and Tokat (2009) ignore the presence of heteroskedasticity in the residuals, we correct for the time-varying nature of the variance and covariance of commodity prices and returns by utilizing a Multivariate GARCH (MGARCH) model. Second, whereas most of the previous literature, including Murat and Tokat (2009), focuses on either Brent or West Texas Intermediate (WTI) crude oil prices, we investigate the dynamics of the new OPEC Reference Basket (ORB) price. The understanding of the OPEC crude oil price is important given the share of OPEC's crude oil production and exports. The U.S. Energy Information Administration (2014) reports, "OPEC member countries produce approximately 40 percent of the world's crude oil and exports approximately 60 percent of the total petroleum traded internationally. " In particular, the study allows us to answer whether the crack spread price data from the U.S. derivative markets can significantly explain OPEC crude oil price movements. In summary, given the limited empirical investigations of the link between the OPEC crude oil spot and refined product derivatives markets, the results from this study should provide useful information for both oil refiners and energy investors regarding portfolio investment and risk management.
The remainder of this paper is organized as follows.
Section 2 contains a brief discussion of the theoretical background on predicting oil price movements using crack spread derivatives. Section 3 describes the data.
Section 4 presents the methodology used in forming the forecasting models. Section 5 discusses the empirical results, and finally, Section 6 concludes.

Predicting oil price movements using crack spread derivatives
The idea of forecasting oil price movements using information from the crack spread futures market is based on two different arguments. The first argument relies on the proposition that the price of crude oil largely depends on the demand from oil refiners (Verleger, 1982;Verleger, 2011). The rationale behind this proposition is that oil refiners are most concerned with the crack spread, and therefore, they cut their levels of production when the price of crude oil is too high compared with the prices of their refined products (i.e., when the crack spread is too low). A decrease in production of refined products will, in turn, lower the price of crude oil through the lower demand for input (Verleger, 2011). This relationship suggests that we should be able to forecast future oil price movements based on information from the crack spread markets.
Assuming that the efficient market hypothesis holds, Oil Price Forecasting Using Crack Spread Futures and Oil Exchange Traded Funds then the prices of futures contracts based on crack spreads are the optimal forecasts of the crack spreads (e.g., Chinn & Coibion, 2014;Lean, McAleer & Wong, 2010;Ma, 1989). Accordingly, information contained in crack spread futures should at least partially explain future oil price movements.
The second argument relies on the proposition that there is a positive relationship between convenience yield (the benefit from physically holding a commodity rather than holding a futures contract for that commodity) and marginal production costs (Heinkel, Howe, & Hughes, 1990). The reasoning behind this proposition is that, to maximize their profits, oil refiners respond to increased demand through immediate production when the marginal production costs are low and through the stock kept in inventory when the marginal costs of production are high. This strategy implies that when the marginal production costs are relatively inexpensive (expensive), the convenience yields are low (high). Because low marginal costs imply high profit margins or crack spreads, the proposition therefore suggests that there is a negative relationship between the convenience yields and crack spreads. This negative relationship is empirically verified by Edwards and Ma (1992) and Kocagil (2004).
Given the Theory of Storage (Kaldor, 1939), commodity spot prices and commodity futures prices are related through the convenience yield. Thus, assuming the efficient market hypothesis, one should expect the existence of a long-run equilibrium relationship between crude oil spot and crack spread futures markets and that variations in crack spread futures can help explain crude oil price movements (see, for example, Asche et al., 2003;Gjolberg & Johnsen, 1999;Haigh & Holt, 2002;Lanza et al., 2005;Murat & Tokat, 2009;Seletis, 1994).  Because the entry barrier in the ETF market is not as strict as in the futures markets, more diverse types of investors (i.e., not only oil refiners and institutional investors) can enter the ETF market, which raises the question of whether the ETF crack spread is better at explaining spot oil price movements than the crack spread futures. Given the recent increased investor interest in oil and refined product ETFs and the convenient trading system, we expect that the ETF market should contribute more to the forecasting models. To the best of our knowledge, no empirical research has yet directly addressed the role of the ETF crack spread in predicting the movements of the crude oil spot price. Therefore, this paper examines the forecasting power of both crack spread futures and crack spread ETFs for the first time.

Data
The analysis uses daily closing spot price data for the The most common ratio of the crack spread for light oil is 3:2:1 (three crude oil, two gasoline, and one heating oil). However, OPEC crude oil is considered a representative of heavier oil compared with lighter oil such as WTI and Brent. Consequently, the 2:1:1 crack spread is a better description for the case of ORB prices because heavier crude oil usually yields less gasoline than the lighter oil such as WTI. Given that crude oil is quoted in dollars per barrel and refined products are quoted in cents per gallon, gasoline and heating oil prices are converted to dollars per barrel by multiplying the cents-per-gallon price by 42. Accordingly, the 2:1:1 crack spread futures are calculated as

Methodology
The aim of the analysis is to examine the contribution of two different derivatives products, crack spread futures and the derivative-based ETF crack spread, in explaining and forecasting crude oil price movements.
Because the sample period includes the financial crisis in 2008, we first test for structural breaks in the data.
We adopt a Zivot and Andrews (1992)   Similar to Murat and Tokat (2009), we explore the long-run equilibrium relationships between the crude oil spot and each crack spread derivative using the Error Correction Model (ECM). Given the timing consideration discussed above, the ECM framework is appropriate because the model is specified such that the current daily return in a particular market depends on its own past returns and past returns of the other market. Let t s denote the log of the crude oil price at time t and t cs denote the log of the crude oil futures price at time t. According to the Engle and Granger (1987) representation theorem, if both t s and t cs are integrated of order one, I(1), but the stochastic error terms are stationary (integrated of order zero, I(0)), the two variables are said to be cointegrated. Cointegration between the two variables could then be established through the error correction representation as ( ) ( ) where t ε is the stationary error term, and 1 t ECT − is the error correction term. s τ and cs τ are the adjustment coefficients. The ECM for the OPEC crude oil spot and the ETF crack spread follows a similar representation as equations (4) and (5) but with ETF in place of cs .
We also conduct Granger causality tests to examine the lead-lag between markets and to determine whether the crack spread derivatives prices are useful for forecasting the movements of crude oil spot prices.
In the forecasting exercise, we also use the MGARCH model, which was introduced by Bollerslev, Engle, and Wooldridge (1998) (4) and (5)), following Kroner and Sultan (1993) where t ε is the standardized disturbance vector, ρ is the unconditional correlation of the standardized residual ( t ε ), and 1 λ and 2 λ are parameters that capture the dynamics of a conditional quasi-correlation. is that the MGARCH model could reduce the unexplained variance (or residual) in the ECM; thus, this benefit should also be taken into account. In addition, the Diebold-Mariano (Diebold & Mariano, 1995) test is also conducted to assess whether the differences between the two rival forecasts are statistically significant.

Empirical results
Descriptive statistics are presented in Table 1. The presence of skewness, leptokurtosis, and non-normality (implied by the significant Jarque-Bera statistics) in all of the series suggest that the unconditional distributions of the crude oil spot, crack spread futures, and ETF crack spread prices and returns are asymmetric, fat tailed-tailed, and non-normal. Plots of the entire data series for the daily prices and daily log returns are presented in Figures 1 and 2 Table 4 reports the Wald test statistics. For the whole sample, we find that the crack spread futures market has no significant impact on the crude oil market, but an impact of the oil spot market on the crack spread futures market is observed. Similar results are obtained for the pre-crisis and crisis periods. For the post-crisis period, however, the dynamic between the two markets is reversed; instead, a unidirectional relationship from the crack spread futures market to the crude oil spot market is detected. This result is consistent with that of Murat and Tokat (2009), who study the relationship between WTI crude oil and the 3:2:1 crack spread futures markets. For the relationship between crude oil spot prices and ETF crack spread prices, the block exogeneity test indicates a strong unidirectional relationship from the ETF crack spread market to the OPEC crude oil market. In summary, in the post-crisis period, the price changes in the crack spread futures and ETF crack spread markets led to future OPEC crude oil spot price changes. This result implies that, for the post-crisis period, the crack spread futures and the ETF crack spread are useful for forecasting the movements of the OPEC crude oil spot prices.

Estimation of the ECM and MGARCH models
The estimation results from the four alternative error correction models are provided in Table 5. The first three models correspond to the relationship between the crude oil spot and crack spread futures markets during (i) the entire sample period, (ii) the pre-crisis and crisis periods (given the Granger-causality results, these two periods are combined), and (iii) the post-crisis period. The last model corresponds to the relationship between the crude oil spot and ETF crack spread markets during the post-crisis period.
The estimated coefficients on the error correction terms (i.e., To compare and discuss the forecasting performance of the crack spread futures and ETF crack spread, we only focus on the post-crisis data. Taking into account the possibility of heteroskedasticity in the residuals of the estimated error correction models, the Breusch-Pagan (BP) and White tests are used to test for the presence of heteroskedastic disturbances. The results of both tests confirm that the null hypothesis of homoscedasticity is rejected at the 5% significance level for all residuals from the estimated error correction models. Therefore, the multivariate GARCH-type models are applied to account for the time-varying variance characteristic in the data.
The estimation results from the CCC and DCC MGARCH models are provided in Table 6. The sum of the coefficients of arch(1) and garch(1) are close to 1, implying that shocks cause a high persistence in the volatility. For both the ECM-MGARCH 1 (CCC and DCC models for oil spot and crack spread futures) and ECM-MGARCH 2 (CCC and DCC models for oil spot and ETF crack spread), the sum in the crack spread equation is higher than in the crude oil spot equation. This result suggests that the shock effect of both the crack spread futures and the ETF crack spread is more persistent than the shock effect of the crude oil spot on those two crack spread derivatives. Moreover, the 1 2 λ λ + estimates of the DCC ECM-MGARCH 2 model are close to (but less than) 1, meaning that a shock can move the correlation away from its long-run average for a considerable amount of time. Therefore, the DCC MGARCH model may capture the variation in the correlation between the crude oil spot and ETF crack spread markets more effectively than the CCC MGARCH model.

Forecasting performance
The forecasting results are reported in   Issue 1 29-44 2015 addition, on average, the ETF crack spread is a better predictor of OPEC crude oil price movements than the crack spread futures both in-sample and out-ofsample. This result suggests that the ETF crack spread market contributes more to the forecasting models than the crack spread futures market.
Our findings provide the following practical implications for policymakers and investors. First, the results suggest that shocks in refined product futures and ETF markets could easily spread to the crude oil spot market, which could impact oil production decisions. Policymakers should therefore design policies to prevent extreme fluctuations in the prices of derivative products caused by speculators. Second, our results suggest that investors could (partly) predict crude oil spot price movements using information flows from the crack spread futures and ETF crack spread markets. In addition, the ETF crack spread price is a better predictor in that the ETF market incorporates new information regarding crude oil spot prices faster than the futures market. Hence, our findings are useful for institutional and individual investors who are interested in understanding and forecasting OPEC crude oil dynamics.
However, our results are not without limitations.