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Are the crude oil markets becoming more efficient over time? New evidence from a generalized spectral test
Energy Economics 40 (2013) 875–881
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Energy Economics journal homepage: www.elsevier.com/locate/eneco
Are the crude oil markets becoming more efficient over time? New evidence from a generalized spectral test Bing Zhang ⁎ School of Business, Nanjing University, Nanjing 210093, PR China Department of Finance and Insurance, School of Business, Nanjing University, Hankou road No.22, Nanjing 210093, PR China
a r t i c l e
i n f o
Article history: Received 4 July 2013 Received in revised form 18 October 2013 Accepted 18 October 2013 Available online 29 October 2013 JEL Classification: Q41 Keywords: Crude oil markets Weak-form efficiency Generalized spectral test
a b s t r a c t This paper utilizes the newly developed method of a generalized spectral test to examine the weak-form efficiency of the main worldwide crude oil markets. The generalized spectral test, unlike other methods, can detect both linear and nonlinear serial dependence in the conditional mean and allows for different forms of unknown conditional heteroscedasticity. By using a “rolling sample” approach instead of an analysis of different time periods, we find that the efficiency of oil markets may depend on time periods. The main global crude oil markets reach weak-form efficiency in the long-term and the degree of efficiency of global oil markets changes over time. Among the oil markets examined in this study, the Brent and the WTI oil markets possess the highest efficiency levels, whereas the Daqing oil market has the lowest efficiency level. Apparent anti-synchronization is detected between the efficiency of Brent and WTI markets in recent years, whereas synchronization is found between the efficiency of Daqing and Dubai oil markets during the last decade. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Crude oil is a major commodity and is one of the most significant production factors for many economies. The dynamics of oil markets are a focus of interest for researchers and economists. One of the most examined topics is the concept of informational efficiency because oil price movements substantially affect the performance of most economic sectors at different levels and through various channels (Lescaroux and Mignon, 2008). The testing of market efficiency has significant implications for crude oil markets, and market efficiency is associated with appropriate equilibrium spot prices. The level of market efficiency will determine the trading and other strategies of market participants, and profitable opportunities may be available in an inefficient market. Oil price behavior, especially in the context of a weak-form efficient market, has been a focus of study for academicians and practitioners. The majority of existing studies concerning the behavior of stock market prices have accepted weak-form market efficiency (see Fama, 1970). The recent advances in mathematical modeling have prompted significant re-examination of the behavior of oil returns using new methods. The specificity of the oil market in comparison to other markets is its highly nonlinear serial dependence in the return series (see, e.g., Alvarez-Ramirez et al., 2008, 2010; Tabak and Cajueiro, 2007). This feature must be considered in the testing of oil return predictability. The tests of weak-form efficiency in crude oil spot markets range from single tests of the autocorrelation of price changes to more ⁎ Tel.: +86 25 83370126. E-mail address: [email protected]. 0140-9883/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.eneco.2013.10.012
sophisticated variance ratio tests. This type of weak-form market efficiency effectively condenses into the degree of randomness in historical price changes and the ability of lagged prices to forecast future prices. Tabak and Cajueiro (2007) analyze the efficiency of crude oil markets (Brent and WTI) by estimating the fractal structure of the time series; they conclude that the crude oil spot market has become more efficient over time. Alvarez-Ramirez et al. (2008) apply the detrended fluctuation analysis (DFA) method and find that the oil market exhibits timevarying, short-term, inefficient behavior that becomes efficient in the long term. The authors claim that the random walk behavior in energy futures prices is still unresolved in the existing research. Maslyuk and Smyth (2008) use LM root tests with one and two structural breaks to demonstrate that spot and future oil markets are efficient in the weak form. The study results suggest that future spot and futures prices cannot be predicted on the basis of previous prices. Wang and Liu (2010) combine a multiscale analysis with the rolling window method to analyze the efficiency of the WTI crude oil market employing DFA; they find that the small fluctuations of the WTI crude oil market could be forecasted; however, the large fluctuations demonstrate high instability, both in the short- and long-term. Charles and Darne (2009) test the weak-form efficient market hypothesis for two crude oil markets (UK Brent and US West Texas Intermediate) during the period 1982 to 2008 with non-parametric variance ratio tests. They find that the Brent crude oil market exhibits weak-form efficiency, whereas the WTI crude oil market appears to be inefficient during the 1994 to 2008 sub-period, which suggests that the deregulation did not improve the efficiency of the WTI crude oil market with respect to less predictable returns. Fernandez (2010) demonstrates that oil returns series
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may exhibit either anti-persistence or persistence over the sample period. Several studies test weak-form market efficiency in crude oil futures markets. For example, Lean et al. (2010) test the market efficiency of crude oil spot and futures prices using both the mean-variance and stochastic dominance approaches. They find that the spot and futures crude oil markets were efficient and rational. However, Wang and Yang (2010) use high-frequency (intraday) data on crude oil (along with heating oil, gasoline, and natural gas) futures markets to find evidence of weak-form market inefficiency, but succeed only with respect to heating oil and natural gas, not crude oil. Therefore, although several new methods have been applied, the empirical literature findings remain controversial. In fact, the most appropriate approach to testing for weakform market efficiency is to examine whether the returns are a martingale difference sequence (MDS). However, the MDS tests have been largely ignored by previous studies because the empirical analysis is dominated by variance ratio (VR) tests and independent and identically distributedbased (IID) nonlinearity tests. A direct test for testing MDS series is required. The main contributions of this paper are as follows. First, this paper employs a recently introduced generalized spectral test (Hong and Lee, 2005) to analyze the return predictability of the main global crude oil markets. This method can detect any linear or nonlinear serial dependence in the conditional mean, allow for any unknown form of conditional heteroskedasticity, and verify all the lags and thus possesses sufficient power in testing EMH. Second, this study is innovative and utilizes a “rolling sample” approach rather than analyzing different periods. There is no need to identify an event date, which is often subject to criticism. The implementation of the generalized spectral test within a rolling window approach facilitates inferential outcomes that are robust to possible structural changes. Chen and Hong (2003) use the generalized spectral test and apply it to the Chinese stock market. Lv and Pan (2009) also use the method, but they don't use it within a rolling window approach. The advantage of the generalized spectral analysis, in comparison to the popular detrended fluctuation analysis (DFA) (see,e.g., AlvarezRamirez et al., 2008; Wang and Liu, 2010) and the detrending moving average (DMA) (Wang and Wu, 2012) that are used in recent studies, is that it provides a statistic from which we can directly infer whether the departure from EMH is statistically significant. The DFA or DMA method provides only a quantitative result without statistical inference. The bootstrap method that is proposed by Tabak and Cajueiro (2007) may provide the significance of DFA-based results, but the procedure is burdensome with respect to calculations. The markets to be tested in this paper include Europe, the US, OPEC and China. The results indicate that the WTI from the US, and Brent from Europe have reached weak-form efficiency. OPEC's Dubai and China's Daqing oil markets are not efficient as whole samples, but they are becoming more efficient over time. Section 2 of this paper introduces the generalized spectral test method. Section 3 provides an empirical analysis of the results, and Section 4 presents the study's conclusions and further explanations. 2. The methodology: generalized spectral test (GS test) Let It − 1 denote the collection of oil returns available at time t − 1; i.e., It − 1 = {Xt − 1, Xt − 2, …}. The dynamics of stock return Xt and its changes over time can be analyzed with the following significant hypothesis: the weak form of EMH (Fama, 1970), which can be formally stated as H0 : EðX t jIt−1 Þ ¼ EðX t Þ almost surely ða:s:Þ;
ð1Þ
or, we can write: EðX t −μ jIt−1 Þ ¼ Eðε t jIt−1 Þ ¼ 0: ð2Þ The unconditional mean μ is the long-run average oil return, and the conditional mean E(Xt|It − 1) is the optimum one-step-ahead oil return
that can be expected by fully and efficiently utilizing It − 1, the information with respect to the entire history of oil returns. The EMH test can be viewed in the same way as a test that determines whether series {εt} is a martingale difference series. Hong and Lee (2005) propose a specification test for the adequacy of a time series conditional mean model with estimated parameters. It is based on the generalized spectral method, which is an analytic tool for nonlinear time series. Suppose {εt} is a strictly process with the marginal charac stationary teristic functionφðuÞ ¼ E eiuεt , εt and εt − |j| pairwise pffiffiffiffiffiffiffiffi joint characteristic iuε þivε t−j jj ,where i ¼ function is φ j ðu; vÞ ≡ E e t −1, u, v ∈ (−∞, + ∞), j = 0, ± 1, ± 2, ⋯ ± T. The basic idea of the generalized spectral test is to first transform the data via an exponential function, εt → exp(iuεt),and then consider the spectrum of the transformed series eiuεt : f ðω; u; vÞ ≡
∞ 1 X −ijω σ ðu; vÞe ω∈½−π; π; 2π j¼−∞ j
ð3Þ
where ω is the frequency, and σ j(u,v)is the covariance function of the transformed series: iuε ivε σ j ðu; vÞ ≡ cov e t ; e t−j jj j ¼ 0; 1; 2; 3;
ð4Þ
we can derive the following: σ j ðu; vÞ ¼ ϕ j ðu; vÞ−ϕðuÞϕðvÞ:
ð5Þ
The function f(ω,u,v) can capture any type of pairwise serial dependence in {εt} n i.e., dependence between εt and εt − j for any nonzero lag j, including the dependent processes with zero autocorrelation. To capture (and only capture) the serial dependence on the conditional mean, we can use the derivative: T−1 X 1=2 ^f ð0;1;0Þ ðω; 0; vÞ ¼ 1 ^ ðj1;0Þ ð0; vÞe−ijω ð1−j jj=T Þ kð j=pÞσ 2π j¼1−T
ð6Þ
ω∈½−π; π;
where, ^ ðj1;0Þ ð0; vÞ ≡ σ
^ j ðu; vÞ ∂σ ∂u
ju¼0 :
ð7Þ
(1 − |j|/T)1/2 is a finite-sample correction. k(·) is a symmetric kernel function that assigns a weight to each lag j. Examples of k(·)include the Bartlett kernel and the Parzen kernel. This paper uses the Bartlett kernels. p ≡ p(T) is a bandwidth. The resulting test statistic that is robust to conditional heteroskedasticity and other time-varying higher order conditional moments of unknown form, is given as follows: 2 3 Z 2 TX −1 1 2 ð 1;0 Þ ^ ðpÞ5; ð8Þ ^ j ð0; vÞ dW ðvÞ−C M1 ðpÞ ¼ qffiffiffiffiffiffiffiffiffiffiffiffi 4 k ð j=pÞðT− jÞ σ 1 ^ ðpÞ j¼1 D 1 where W : R → R+ is a nondecreasing function that weighs sets symmetric about zero equally. An example of W(·) is the N(1, 0) CDF, which is commonly used in the characteristic function literature. The ^ ðpÞ and D ^ ðpÞ are approximately the mean and the variance factors C 1 1 2 ð 0;1;0 Þ ∧ ð0;1;0Þ π ∧ ðω; 0; vÞ− f 0 ðω; 0; vÞ dωdW ðvÞ. of T∫∫−π f We have taken into account the impact of conditional heteroskedasticity and other time-varying higher order conditional moments. Under EMH, we have M1 ðpÞ→Nð0; 1Þ in distribution:
ð9Þ
B. Zhang / Energy Economics 40 (2013) 875–881
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The M1(p) test has other appealing features. The generalized spectral derivative f (0,1,0)(ω,0,v) only focuses on verifying serial dependence on the conditional mean and thus is especially suitable to test EMH. It will not falsely reject EMH when volatility clustering exists and serial dependence in the higher order conditional moment. f (0,1,0)(ω,0,v) can also detect both linear and nonlinear departures from EMH. Thus, it has more power against a wider range of departures from EMH than any autocorrelation-based test, even if the test were applicable in the presence of conditional heteroskedasticity of an unknown form. Moreover, the M1(p) test can verify a significant number of lags, which discounts higher order lags via the kernel k(·). The downward weighting by k(·) ensures good power of M1(p) in practice because it is consistent with the stylized fact that financial markets are usually affected by recent past events to a greater extent than remote past events. This is one of the advantages of frequency domain analysis over time domain analysis. Time domain analysis usually gives equal weighting to each lag, which is not efficient when a large number of lags are used. Moreover, if we want to determine if {εt} is a white noise series, we can test. For any j: Fig. 1. Graphical illustration of oil price dynamics. ð1;1Þ
σj
ð0; 0Þ ¼
∂2 σ j ðu; vÞ ∂u∂v
ju¼0;v¼0 ¼ cov iε t ; iεt−j jj ¼ 0:
3. The data and empirical results 3.1. The data and descriptive analysis The paper utilizes the daily closing price data of global oil markets. Currently, there are two major benchmarks for global oil prices, West Texas Intermediate (WTI) crude oil and Brent crude oil. WTI and Brent crude oil data are obtained from the Energy Information Administration (EIA) of the US (www.eia.gov). The WTI price data starts from January 2, 1986, and the Brent data starts from May 20, 1987. Dubai crude oil is produced in the Emirate of Dubai, part of the United Arab Emirates. Dubai's only refinery, located at Jebel Ali, accepts condensates as feedstock and therefore all of Dubai's crude production is exported. For many years this was the only freely traded oil in the Middle East, but gradually a spot market has developed in Oman crude. For many years, the majority of oil producers in the Middle East used the monthly spot price average of Dubai and Oman as the benchmark for sales to the Far East (the WTI and Brent futures prices are used for exports to the Atlantic Basin). The Daqing oil field is the largest in China and its crude oil output accounts for approximately 25% of national production. Because its price represents the level of crude oil prices in China, this paper uses the Daqing crude oil spot prices as representative of Chinese crude oil prices. The Daqing prices and Dubai oil prices are obtained from the Wind database (www.wind.com.cn). The sample period covers 11 years from December 28, 2001 to April 18, 2013. From an econometric point of view, over 11 years of daily data are adequate to yield meaningful estimation results without a small sample bias. A focus on the most recent 11 years increases the relevance of this study. The daily returns are computed as the percent logarithmic difference in the daily index, i.e., Xi,t = 100 ln(Pi,t/Pi,t − 1). Fig. 1 presents a graphical illustration of our data. There are four distinct characteristics: 1) The oil prices of WTI, Brent, Dubai and Daqing are highly correlated. 2) There is a general upward trend throughout the sample period until August 2008. Since then, the financial crisis heavily influenced the global markets. The oil prices dropped and then gradually began to increase. 3) The volatility is high, especially in the last five years, and 4) The price difference between various oil markets has widened in recent months. According to the descriptive statistics and the ARCH test, all of the return series are highly non-normally distributed with conditional
heteroskedasticity. The details of the ARCH test are not reported for simplicity. Table 1 provides the summary statistics of crude oil returns in four countries. The Dubai oil price has the highest average oil returns and the largest extreme value over the sample period. The Dubai oil market also exhibits positive skewness, which demonstrates that it has undergone substantial price hikes. The non-zero skewness and positive excess kurtosis implies the fat-tail distribution of crude oil returns. The WTI oil market has the highest volatility. 3.2. Analysis of crude oil market efficiency We use the generalized spectral test to obtain the empirical results of the EMH test of the main worldwide crude oil markets of the whole samples. We choose the lag length to be 25, 50, and 75 trading days and calculate the M1 statistics for the WTI, Brent, Daqing and Dubai return series. The results that are presented in Table 2 are based on the sample period from December 28, 2001 through April 18, 2013 with the non-match data deleted. With respect to WTI and Brent, we cannot reject the EMH hypothesis according to the results. The conclusion is consistent with the existing research that finds that these two oil markets are weak-form efficient. However, with respect to the Daqing and Dubai oil markets, the results are mixed. The M1(25) statistic demonstrates a rejection of the EMH at the 10% significance level, whereas the M1(50) and M1(75) statistics are different. The reason for this result might be that the efficiency of these two markets is complex and time-varying for the sample period as a whole. To be more explicit, we examined the subsamples before and after October 15, 2008 separately. The results in Table 3 contribute to an explanation of the contradictory conclusion. The strong rejection of the EMH in the early stage may be a result of an imperfect institutional arrangement, frequent government policy intervention, and the irrational investor behavior in the Table 1 Summary statistics of crude oil returns (%) (2001.12.28–2013.4.18).
Mean Median Maximum Minimum Std. Dev. Skewness Excess Kurtosis
Daqing
WTI
Brent
Dubai
0.059 0.083 12.43 −13.22 2.151 −0.189 3.825
0.049 0.126 16.41 −15.19 2.447 −0.090 4.654
0.058 0.085 18.13 −16.83 2.215 −0.026 4.698
0.059 0.132 31.73 −28.36 2.178 0.105 28.125
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Table 2 The M1 statistics of the GS test for four oil markets.
Table 4 The M1 statistics of the GS test for residuals after linear filtering.
Lags
25
50
75
Lags
25
50
75
WTI Brent Daqing Dubai
−0.52 [0.70] −0.27 [0.61] 1.64 [0.05] 1.41 [0.08]
−0.41 [0.66] −0.29 [0.61] 0.86 [0.20] 0.86 [0.19]
−0.40 [0.65] −0.35 [0.64] 0.47 [0.32] 0.36 [0.36]
WTI Brent Daqing Dubai
−0.62 [0.73] −0.29 [0.61] 0.46 [0.32] 1.62 [0.05]
−0.46 [0.68] −0.30 [0.62] 0.18 [0.43] 1.10 [0.14]
−0.47 [0.68] −0.35 [0.64] −0.02 [0.51] 0.58 [0.28]
Note: The numbers in brackets are the corresponding p-values.
Note: The numbers in brackets are the corresponding p-values.
Daqing oil market. Following the financial crisis, the Daqing oil market became weak-form efficient. This finding suggests that the Daqing oil market efficiency changed over time. This type of change also exists in the Dubai market. To detect this type of change, a rolling test is required. This test is outlined in the next section. For robustness, we filter linear serial dependence through ARMA modeling and use GARCH to filter conditional heteroskedasticity. We then use the GS test for the corresponding residuals. The lag orders are selected via the AIC criteria. The results are presented in Table 4. After linear filtering, the statistic for the residuals of the Daqing market cannot reject the EMH, which implies that the non-efficiency is partly caused by linear serial dependence. Unlike the Daqing market, the non-efficiency of the Dubai market may result from conditional heteroskedasticity, which can be seen in Table 5. The results further prove the effectiveness of the GS method in dealing with any serial dependence.
representations of the p values are presented in Fig. 2 (Brent and WTI) and Fig. 3(Dubai and Daqing) in 100, 250 and 1000 days window respectively.
3.3. The rolling sample analysis The evolution and turbulence of the global oil markets, especially the collapse of Lehman Brothers, increase the likelihood of structural breaks in crude oil markets during the sample period. It has been demonstrated that the determinants of oil price dynamics change over time (Baumeister and Peersman, forthcoming) and that the effects of oil price shocks on economic activity are time varying (Baumeister and Peersman, 2013). Therefore, it seems impossible that any model with constant parameters can adequately capture oil market dynamics over time. Moreover, the level of market efficiency may depend on time periods. The empirical results in Alvarez-Ramirez et al. (2010) suggest that crude oil markets exhibit complex dynamics, with stochastic (relative to fluctuation scaling) price properties depending on the time-scale, sampling frequency and time. Therefore, although the complete sample table provides a summary of “average” market efficiency behavior, it might lack potentially significant secular and cyclical efficiency movements. To test for the evolution of market efficiency, we use a rolling window technique. After determining a sample size, the start and end sample date moves forward at the same time. As the window moved forward, we re-calculate the corresponding p values of the M1 test with respect to each subsample using the generalized spectral analysis. The technique of rolling windows is an effective method that can be used to test the robustness of empirical results, which is critical for time series models (Swanson, 1998). We choose three different window lengths, 100 days (approximately 4 months), 250 days (approximately one year) and 1000 days (approximately 4 years). The choice of observations (approximately 1 or 4 years of data) was designed to ensure comparability with previous studies that have focused on general indices. We assess the extent and the nature of the M1 statistics variations over time via the corresponding time series of p values. The graphical
3.3.1. The WTI and Brent oil markets If the p-values of the M1 statistics are smaller than 0.05, we confirm that the market is not efficient at the 5% significance level. With respect to the 100-day and 250-day window lengths, the p-values for the WTI and Brent return series are always higher than 0.05, which indicates that these two markets are weak-form efficient most of the time. Certain extreme events cause inefficiency in a short period of time (e.g., the Gulf War in 1991 and the recent financial crisis in 2008). This finding is consistent with the evidence in Wang and Wu (2012) that extreme events can cause lower levels of short-term market efficiency. With respect to the 1000-day window, the p-values are also higher than the 0.05 level at most time periods. Regardless of which window length is used, the crude oil markets do not exhibit higher levels of efficiency during the 1990s than during the later 1980s. This implies that the crude oil market deregulation of the US did not significantly improve the market's efficiency. The exceptions were during the periods 1991 to 1992 and 1997 to 1999. Our results confirm the findings in AlvarezRamirez et al. (2010) that persistence is not displayed for the subperiod 1992 to 1997. 3.3.2. The Dubai and Daqing oil markets Fig. 3 presents the p-values of the M1 statistics for the Dubai oil return series. With respect to the 100-day and 250-day windows, the pvalues are larger than 0.05 most of the time. Overall, the Dubai markets are always efficient in weak form. With respect to the 1000-day window, the p-values display an uptrend after 2005, which demonstrates that the Dubai oil market is becoming efficient over time. There is an appearance of market inefficiency in 2004 for the Dubai market, but not for the WTI or Brent markets during the same period. In March 2004, OPEC agreed to cut production. April 2004 witnessed the first major terrorist attack on government installations in Riyadh, the capital of Saudi Arabia and the largest crude oil producer in the world. These events led to persistent increases in the price of oil that was traded in the Middle East, which could explain the market inefficiency because the Dubai oil price is the benchmark for oil pricing in this area. For Daqing, with respect to the 100-day and 250-day windows, the p-values for the Daqing returns are always higher than 0.05. Certain exceptions can be explained by the influence of supply or demand shocks as discussed in the case of Dubai. With respect to the 1000-day window, a distinguishing feature is that the Daqing oil market is becoming more efficient, especially after 2004. This phenomenon can be explained by recent Chinese government efforts to deregulate its crude oil markets.
Table 5 The M1 statistics of the GS test for residuals after filtering for conditional heteroskedasticity. Table 3 The M1 statistics of the GS test for Daqing before and after the financial crisis. Lags
25
50
75
Before crisis After crisis
4.50 [0.00] −1.20 [0.88]
2.83 [0.00] −1.00 [0.84]
2.01 [0.02] −0.83 [0.80]
Note: The numbers in brackets are the corresponding p-values.
Lags
25
50
75
WTI Brent Daqing Dubai
−0.49 [0.69] −0.27 [0.61] 0.69 [0.25] 0.19 [0.43]
−0.36 [0.64] −0.29 [0.61] 0.26 [0.40] 0.13 [0.45]
−0.35 [0.64] −0.35 [0.64] 0.02 [0.49] −0.16 [0.56]
Note: The numbers in brackets are the corresponding p-values.
B. Zhang / Energy Economics 40 (2013) 875–881
Fig. 2. The p-values of the WTI and Brent M1 statistics series under the conditions of 100-, 250- and 1000-day window lengths separately.
3.3.3. A comparison of different efficiency levels With respect to the levels of efficiency of different markets, the WTI and Brent markets are more efficient than the Dubai and Chinese Daqing oil markets. This can be explained by the following two reasons. First, the Brent and WTI markets have well-established perfect oil futures and derivatives markets. Second, after 2000, the investment banks and securities firms swarmed into the oil markets. Commodities index purchases increased from $15 billion in 2003 to at least
879
Fig. 3. The p-values of Daqing and Dubai M1 statistics series under the conditions of 100-, 250- and 1000-day window lengths separately.
$200 billion by mid-2008. This increased interest from index investors accelerated the synchronized financialization of oil. Moreover, the efficiency of the Brent oil market is not always higher than that of the WTI market, which is demonstrated in Fig. 2. These findings are not consistent with those of Charles and Darne (2009). With respect to the efficiency of the Dubai and Daqing oil markets, Dubai always exhibits a
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B. Zhang / Energy Economics 40 (2013) 875–881
higher efficiency level than the Daqing market. The Daqing oil market is inefficient in the early periods. The early stage inefficiency of the Chinese domestic crude oil market can be attributed to the distribution system and pricing mechanism. There are only two types of traders, oil fields and refinery plants, in the crude oil trading business. Therefore, even if economic agents predict that oil is overvalued or undervalued, there is no access to arbitrage transactions. Therefore, the Daqing oil price did not reflect the known information, which led to the departure from the EMH. China has become a net importer of crude oil since 1996 and the import dependence has exceeded 51% in 2008. In this sense, Chinese oil prices are largely affected by oil prices in other international markets. The market integration has improved over time, therefore, the market efficiency has improved greatly in recent years. Detailed explanations are provided in Section 4. 4. Conclusions and further explanations This paper investigates the efficiency of four major crude oil markets worldwide. A new statistical procedure, the generalized spectral test method, is utilized that can capture linear and nonlinear serial dependence. This method is robust to unknown forms of conditional heteroskedasticity and all of the lag lengths and thus possesses adequate power against departures from market efficiency. Based on the rolling sample technique, our results suggest that oil market efficiency is dependent on the time period. This finding implies that time-varying parameter models might be superior to constantparameter models in capturing oil market dynamics and the relationships between oil prices and economic variables. The related empirical investigations are presented in Baumeister and Peersman (2013, forthcoming). Overall, the WTI, Brent and Dubai oil markets are efficient in the majority of periods, which implies low predictability levels of crude oil prices. From an empirical study using a 1000-day window length, there is evidence that the crude oil market with respect to Brent has been less efficient than the WTI since 2005. An apparent anti-synchronization is detected between the Brent and WTI markets in recent years. This can be explained as follows: relative changes in supply and demand forces affect the prices of crude oil, which is thought to be the main factor that contributes to oil market efficiency in the long-term. The volatility of crude oil price will increase when deviations occur in supply and demand. The crude oil price of the WTI is more a reflection of the supply and demand situation of US domestic oil, whereas the Brent crude oil price is mainly affected by the relationship between the supply and demand with respect to Asia and Europe. During the period from 2001 to early 2005, oil supplies of the WTI market were severely depleted due to multiple factors. In 2001, the “9.11” incident had a significant impact on the WTI market and by the end of 2002, the U.S. crude oil inventories had fallen to their lowest level in the past 20 years. In 2003, the United States launched a military operation in Iraq. Subsequently, “refinery problems”, a “hurricane” and “Iran's nuclear issue” all had a significant impact on the U.S. oil supply while there was a greater domestic demand for U.S. crude oil. With respect to the period 2001 to early 2005, a serious imbalance between supply and demand led to significantly weaker efficiency of the WTI market compared to that of Brent. However, after 2005 the U.S. Cushing crude oil inventory increased rapidly and the U.S. imports of crude oil from Canada also began to increase. After the 2008 financial crisis the U.S. crude oil inventories had been at a higher level, and at the beginning of 2011 the Cushing inventories rose to a record high. In comparison to the increasing supply, the weakness of the US economy post-financial crisis and the rapid increase of US coal bed methane, shale gas and other alternative energy relieved the overheated demand for crude oil. An adequate supply eased the upward pressure on the oil prices of the WTI market. However, Brent's situation had been worrying since 2005: European crude oil production had plummeted by 41% within 10 years, which led to a tight supply of crude oil and increased external dependency. Moreover, because nearly 40% of crude oil in Europe was transported from the
Middle East area through the Suez canal, unrest in the Middle East such as the Arab–Israeli conflict, the Israeli–Palestinian dispute, instability in Lebanon, the war in Libya and Syria, terrorism and religious extremism all had a substantial impact on Europe's crude oil supply. Contrastingly, the oil price of the WTI was less affected by the situation in the Middle East. Additionally, after the financial crisis, certain investment funds were withdrawn from the WTI market and transferred to the Brent market. Consequently, driven by speculative funds, the Brent crude oil market often contained more irrational premium ingredients and Brent's market efficiency decreased. Prior to 2005, an imbalance between supply and demand led to weaker efficiency of the WTI market compared to the Brent market and, after 2005, a sharp decline in Europe's crude oil supply and turmoil in the Middle East in addition to other external factors resulted in significantly weaker efficiency of the Brent market compared to the WTI market. The Chinese Daqing oil market was not efficient prior to 2004, but has become more efficient in recent years. Because the oil market in China was not yet fully competitive, and the ability of society to bear high oil prices remained weak, the government persisted in regulating oil product prices. With respect to the price mechanism of the Daqing oil market, China's oil price reforms aimed to move gradually towards more marketoriented prices with the ultimate goal of price formation through competitive markets. In 1998, China established a mechanism for domestic oil prices that was consistent with the international market. Oil products prices could now be set based on international crude oil prices, and taking into consideration processing costs, the quality of crude oil, taxes and appropriate profit margins. The Chinese Daqing crude oil price was based on the Asia market and the main Chinese oil companies use Dubai prices to calculate their company taxes. Moreover, the prices of domestic petroleum products have been integrated with international markets since June, 2000. The Middle East is a vital oil source for China and the Dubai oil market affects the Daqing oil market. However, the oil demand of China has been a major force in supporting the price of oil that is produced in the Middle East because the energy link and trade between the two has gradually increased in recent years. Due to recent developments in the close oil relationship between China and the Middle East, the Daqing and Dubai oil markets are mutually advantageous. These changes in price setting mechanisms cause similar pricing, and we therefore find evidence of market efficiency synchronization between the Daqing and Dubai oil markets during the last decade. Acknowledgments The authors acknowledge the financial support from the China National Science Fund 71171108 and 71371096 and we thank two referees for providing us suggestions and Dr Huifeng Pan for providing us the Gauss codes. References Alvarez-Ramirez, J., Alvarez, J., Rodriguez, E., 2008. Short-term predictability of crude oil markets: a detrended fluctuation analysis approach. Energy Econ. 30 (5), 2645–2656. Alvarez-Ramirez, J., Alvarez, J., Solis, R., 2010. Crude oil market efficiency and modeling: insights from the multiscaling autocorrelation pattern. Energy Econ. 32 (5), 993–1000. Baumeister, C., Peersman, G., 2013. The role of time-varying price elasticities in accounting for volatility changes in the crude oil market. J. Appl. Econ. (forthcoming). Baumeister, C., Peersman, G., 2013. Time-varying effects of oil supply shocks on the US economy. Am. Econ. J. Macroecon. 5 (4), 1–28. Charles, A., Darne, O., 2009. The efficiency of the crude oil markets: evidence from variance ratio tests. Energy Policy 37 (11), 4267–4272. Chen, Max, Hong, Yongmiao, 2003. Has Chinese stock market become efficient? Evidence from a new approach. Lect. Notes Comput. Sci 2658, 90–98. Fama, E.F., 1970. Efficient capital markets: a review of theory and empirical work. J. Financ. 25, 383–417. Fernandez, V., 2010. Commodity futures and market efficiency: a fractional integrated approach. Resour. Policy 35, 276–282. Hong, Y., Lee, Y.J., 2005. Generalized spectral tests for conditional mean models in time series with conditional heteroscedasticity of unknown form. Rev. Econ. Stud. 72 (2), 499–541.
B. Zhang / Energy Economics 40 (2013) 875–881 Lean, H.H., McAleer, M., Wong, W.-K., 2010. Market efficiency of oil spot and futures: a mean-variance and stochastic dominance approach. Energy Econ. 32, 979–986. Lescaroux, F., Mignon, V., 2008. On the influence of oil prices on economic activity and other macroeconomic and financial variables. OPEC Energy Rev. 32, 343–380. Lv, W.D., Pan, H.F., 2009. Weak form market efficiency test of oil markets based on generalized spectrum method. ICMSE 2009. Maslyuk, S., Smyth, R., 2008. Unit root properties of crude oil spot and futures prices. Energy Policy 36, 2591–2600. Swanson, N.R., 1998. Money and output viewed through a rolling window. J. Monet. Econ. 41 (3), 455–474.
881
Tabak, B.M., Cajueiro, D.O., 2007. Are the crude oil markets becoming weakly efficient over time? A test for time-varying long-range dependence in prices and volatility. Energy Econ. 29 (1), 28–36. Wang, Y., Liu, L., 2010. Is WTI crude oil market becoming weakly efficient over time? New evidence from multiscale analysis based on detrended fluctuation analysis. Energy Econ. 32 (5), 987–992. Wang, T., Yang, J., 2010. Nonlinearity and intraday efficiency tests on energy futures markets. Energy Econ. 32 (2), 496–503. Wang, Y., Wu, C., 2012. Long memory in energy futures markets: further evidence. Resour. Policy 37, 261–272.
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Energy Economics journal homepage: www.elsevier.com/locate/eneco
Are the crude oil markets becoming more efficient over time? New evidence from a generalized spectral test Bing Zhang ⁎ School of Business, Nanjing University, Nanjing 210093, PR China Department of Finance and Insurance, School of Business, Nanjing University, Hankou road No.22, Nanjing 210093, PR China
a r t i c l e
i n f o
Article history: Received 4 July 2013 Received in revised form 18 October 2013 Accepted 18 October 2013 Available online 29 October 2013 JEL Classification: Q41 Keywords: Crude oil markets Weak-form efficiency Generalized spectral test
a b s t r a c t This paper utilizes the newly developed method of a generalized spectral test to examine the weak-form efficiency of the main worldwide crude oil markets. The generalized spectral test, unlike other methods, can detect both linear and nonlinear serial dependence in the conditional mean and allows for different forms of unknown conditional heteroscedasticity. By using a “rolling sample” approach instead of an analysis of different time periods, we find that the efficiency of oil markets may depend on time periods. The main global crude oil markets reach weak-form efficiency in the long-term and the degree of efficiency of global oil markets changes over time. Among the oil markets examined in this study, the Brent and the WTI oil markets possess the highest efficiency levels, whereas the Daqing oil market has the lowest efficiency level. Apparent anti-synchronization is detected between the efficiency of Brent and WTI markets in recent years, whereas synchronization is found between the efficiency of Daqing and Dubai oil markets during the last decade. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Crude oil is a major commodity and is one of the most significant production factors for many economies. The dynamics of oil markets are a focus of interest for researchers and economists. One of the most examined topics is the concept of informational efficiency because oil price movements substantially affect the performance of most economic sectors at different levels and through various channels (Lescaroux and Mignon, 2008). The testing of market efficiency has significant implications for crude oil markets, and market efficiency is associated with appropriate equilibrium spot prices. The level of market efficiency will determine the trading and other strategies of market participants, and profitable opportunities may be available in an inefficient market. Oil price behavior, especially in the context of a weak-form efficient market, has been a focus of study for academicians and practitioners. The majority of existing studies concerning the behavior of stock market prices have accepted weak-form market efficiency (see Fama, 1970). The recent advances in mathematical modeling have prompted significant re-examination of the behavior of oil returns using new methods. The specificity of the oil market in comparison to other markets is its highly nonlinear serial dependence in the return series (see, e.g., Alvarez-Ramirez et al., 2008, 2010; Tabak and Cajueiro, 2007). This feature must be considered in the testing of oil return predictability. The tests of weak-form efficiency in crude oil spot markets range from single tests of the autocorrelation of price changes to more ⁎ Tel.: +86 25 83370126. E-mail address: [email protected]. 0140-9883/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.eneco.2013.10.012
sophisticated variance ratio tests. This type of weak-form market efficiency effectively condenses into the degree of randomness in historical price changes and the ability of lagged prices to forecast future prices. Tabak and Cajueiro (2007) analyze the efficiency of crude oil markets (Brent and WTI) by estimating the fractal structure of the time series; they conclude that the crude oil spot market has become more efficient over time. Alvarez-Ramirez et al. (2008) apply the detrended fluctuation analysis (DFA) method and find that the oil market exhibits timevarying, short-term, inefficient behavior that becomes efficient in the long term. The authors claim that the random walk behavior in energy futures prices is still unresolved in the existing research. Maslyuk and Smyth (2008) use LM root tests with one and two structural breaks to demonstrate that spot and future oil markets are efficient in the weak form. The study results suggest that future spot and futures prices cannot be predicted on the basis of previous prices. Wang and Liu (2010) combine a multiscale analysis with the rolling window method to analyze the efficiency of the WTI crude oil market employing DFA; they find that the small fluctuations of the WTI crude oil market could be forecasted; however, the large fluctuations demonstrate high instability, both in the short- and long-term. Charles and Darne (2009) test the weak-form efficient market hypothesis for two crude oil markets (UK Brent and US West Texas Intermediate) during the period 1982 to 2008 with non-parametric variance ratio tests. They find that the Brent crude oil market exhibits weak-form efficiency, whereas the WTI crude oil market appears to be inefficient during the 1994 to 2008 sub-period, which suggests that the deregulation did not improve the efficiency of the WTI crude oil market with respect to less predictable returns. Fernandez (2010) demonstrates that oil returns series
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may exhibit either anti-persistence or persistence over the sample period. Several studies test weak-form market efficiency in crude oil futures markets. For example, Lean et al. (2010) test the market efficiency of crude oil spot and futures prices using both the mean-variance and stochastic dominance approaches. They find that the spot and futures crude oil markets were efficient and rational. However, Wang and Yang (2010) use high-frequency (intraday) data on crude oil (along with heating oil, gasoline, and natural gas) futures markets to find evidence of weak-form market inefficiency, but succeed only with respect to heating oil and natural gas, not crude oil. Therefore, although several new methods have been applied, the empirical literature findings remain controversial. In fact, the most appropriate approach to testing for weakform market efficiency is to examine whether the returns are a martingale difference sequence (MDS). However, the MDS tests have been largely ignored by previous studies because the empirical analysis is dominated by variance ratio (VR) tests and independent and identically distributedbased (IID) nonlinearity tests. A direct test for testing MDS series is required. The main contributions of this paper are as follows. First, this paper employs a recently introduced generalized spectral test (Hong and Lee, 2005) to analyze the return predictability of the main global crude oil markets. This method can detect any linear or nonlinear serial dependence in the conditional mean, allow for any unknown form of conditional heteroskedasticity, and verify all the lags and thus possesses sufficient power in testing EMH. Second, this study is innovative and utilizes a “rolling sample” approach rather than analyzing different periods. There is no need to identify an event date, which is often subject to criticism. The implementation of the generalized spectral test within a rolling window approach facilitates inferential outcomes that are robust to possible structural changes. Chen and Hong (2003) use the generalized spectral test and apply it to the Chinese stock market. Lv and Pan (2009) also use the method, but they don't use it within a rolling window approach. The advantage of the generalized spectral analysis, in comparison to the popular detrended fluctuation analysis (DFA) (see,e.g., AlvarezRamirez et al., 2008; Wang and Liu, 2010) and the detrending moving average (DMA) (Wang and Wu, 2012) that are used in recent studies, is that it provides a statistic from which we can directly infer whether the departure from EMH is statistically significant. The DFA or DMA method provides only a quantitative result without statistical inference. The bootstrap method that is proposed by Tabak and Cajueiro (2007) may provide the significance of DFA-based results, but the procedure is burdensome with respect to calculations. The markets to be tested in this paper include Europe, the US, OPEC and China. The results indicate that the WTI from the US, and Brent from Europe have reached weak-form efficiency. OPEC's Dubai and China's Daqing oil markets are not efficient as whole samples, but they are becoming more efficient over time. Section 2 of this paper introduces the generalized spectral test method. Section 3 provides an empirical analysis of the results, and Section 4 presents the study's conclusions and further explanations. 2. The methodology: generalized spectral test (GS test) Let It − 1 denote the collection of oil returns available at time t − 1; i.e., It − 1 = {Xt − 1, Xt − 2, …}. The dynamics of stock return Xt and its changes over time can be analyzed with the following significant hypothesis: the weak form of EMH (Fama, 1970), which can be formally stated as H0 : EðX t jIt−1 Þ ¼ EðX t Þ almost surely ða:s:Þ;
ð1Þ
or, we can write: EðX t −μ jIt−1 Þ ¼ Eðε t jIt−1 Þ ¼ 0: ð2Þ The unconditional mean μ is the long-run average oil return, and the conditional mean E(Xt|It − 1) is the optimum one-step-ahead oil return
that can be expected by fully and efficiently utilizing It − 1, the information with respect to the entire history of oil returns. The EMH test can be viewed in the same way as a test that determines whether series {εt} is a martingale difference series. Hong and Lee (2005) propose a specification test for the adequacy of a time series conditional mean model with estimated parameters. It is based on the generalized spectral method, which is an analytic tool for nonlinear time series. Suppose {εt} is a strictly process with the marginal charac stationary teristic functionφðuÞ ¼ E eiuεt , εt and εt − |j| pairwise pffiffiffiffiffiffiffiffi joint characteristic iuε þivε t−j jj ,where i ¼ function is φ j ðu; vÞ ≡ E e t −1, u, v ∈ (−∞, + ∞), j = 0, ± 1, ± 2, ⋯ ± T. The basic idea of the generalized spectral test is to first transform the data via an exponential function, εt → exp(iuεt),and then consider the spectrum of the transformed series eiuεt : f ðω; u; vÞ ≡
∞ 1 X −ijω σ ðu; vÞe ω∈½−π; π; 2π j¼−∞ j
ð3Þ
where ω is the frequency, and σ j(u,v)is the covariance function of the transformed series: iuε ivε σ j ðu; vÞ ≡ cov e t ; e t−j jj j ¼ 0; 1; 2; 3;
ð4Þ
we can derive the following: σ j ðu; vÞ ¼ ϕ j ðu; vÞ−ϕðuÞϕðvÞ:
ð5Þ
The function f(ω,u,v) can capture any type of pairwise serial dependence in {εt} n i.e., dependence between εt and εt − j for any nonzero lag j, including the dependent processes with zero autocorrelation. To capture (and only capture) the serial dependence on the conditional mean, we can use the derivative: T−1 X 1=2 ^f ð0;1;0Þ ðω; 0; vÞ ¼ 1 ^ ðj1;0Þ ð0; vÞe−ijω ð1−j jj=T Þ kð j=pÞσ 2π j¼1−T
ð6Þ
ω∈½−π; π;
where, ^ ðj1;0Þ ð0; vÞ ≡ σ
^ j ðu; vÞ ∂σ ∂u
ju¼0 :
ð7Þ
(1 − |j|/T)1/2 is a finite-sample correction. k(·) is a symmetric kernel function that assigns a weight to each lag j. Examples of k(·)include the Bartlett kernel and the Parzen kernel. This paper uses the Bartlett kernels. p ≡ p(T) is a bandwidth. The resulting test statistic that is robust to conditional heteroskedasticity and other time-varying higher order conditional moments of unknown form, is given as follows: 2 3 Z 2 TX −1 1 2 ð 1;0 Þ ^ ðpÞ5; ð8Þ ^ j ð0; vÞ dW ðvÞ−C M1 ðpÞ ¼ qffiffiffiffiffiffiffiffiffiffiffiffi 4 k ð j=pÞðT− jÞ σ 1 ^ ðpÞ j¼1 D 1 where W : R → R+ is a nondecreasing function that weighs sets symmetric about zero equally. An example of W(·) is the N(1, 0) CDF, which is commonly used in the characteristic function literature. The ^ ðpÞ and D ^ ðpÞ are approximately the mean and the variance factors C 1 1 2 ð 0;1;0 Þ ∧ ð0;1;0Þ π ∧ ðω; 0; vÞ− f 0 ðω; 0; vÞ dωdW ðvÞ. of T∫∫−π f We have taken into account the impact of conditional heteroskedasticity and other time-varying higher order conditional moments. Under EMH, we have M1 ðpÞ→Nð0; 1Þ in distribution:
ð9Þ
B. Zhang / Energy Economics 40 (2013) 875–881
877
The M1(p) test has other appealing features. The generalized spectral derivative f (0,1,0)(ω,0,v) only focuses on verifying serial dependence on the conditional mean and thus is especially suitable to test EMH. It will not falsely reject EMH when volatility clustering exists and serial dependence in the higher order conditional moment. f (0,1,0)(ω,0,v) can also detect both linear and nonlinear departures from EMH. Thus, it has more power against a wider range of departures from EMH than any autocorrelation-based test, even if the test were applicable in the presence of conditional heteroskedasticity of an unknown form. Moreover, the M1(p) test can verify a significant number of lags, which discounts higher order lags via the kernel k(·). The downward weighting by k(·) ensures good power of M1(p) in practice because it is consistent with the stylized fact that financial markets are usually affected by recent past events to a greater extent than remote past events. This is one of the advantages of frequency domain analysis over time domain analysis. Time domain analysis usually gives equal weighting to each lag, which is not efficient when a large number of lags are used. Moreover, if we want to determine if {εt} is a white noise series, we can test. For any j: Fig. 1. Graphical illustration of oil price dynamics. ð1;1Þ
σj
ð0; 0Þ ¼
∂2 σ j ðu; vÞ ∂u∂v
ju¼0;v¼0 ¼ cov iε t ; iεt−j jj ¼ 0:
3. The data and empirical results 3.1. The data and descriptive analysis The paper utilizes the daily closing price data of global oil markets. Currently, there are two major benchmarks for global oil prices, West Texas Intermediate (WTI) crude oil and Brent crude oil. WTI and Brent crude oil data are obtained from the Energy Information Administration (EIA) of the US (www.eia.gov). The WTI price data starts from January 2, 1986, and the Brent data starts from May 20, 1987. Dubai crude oil is produced in the Emirate of Dubai, part of the United Arab Emirates. Dubai's only refinery, located at Jebel Ali, accepts condensates as feedstock and therefore all of Dubai's crude production is exported. For many years this was the only freely traded oil in the Middle East, but gradually a spot market has developed in Oman crude. For many years, the majority of oil producers in the Middle East used the monthly spot price average of Dubai and Oman as the benchmark for sales to the Far East (the WTI and Brent futures prices are used for exports to the Atlantic Basin). The Daqing oil field is the largest in China and its crude oil output accounts for approximately 25% of national production. Because its price represents the level of crude oil prices in China, this paper uses the Daqing crude oil spot prices as representative of Chinese crude oil prices. The Daqing prices and Dubai oil prices are obtained from the Wind database (www.wind.com.cn). The sample period covers 11 years from December 28, 2001 to April 18, 2013. From an econometric point of view, over 11 years of daily data are adequate to yield meaningful estimation results without a small sample bias. A focus on the most recent 11 years increases the relevance of this study. The daily returns are computed as the percent logarithmic difference in the daily index, i.e., Xi,t = 100 ln(Pi,t/Pi,t − 1). Fig. 1 presents a graphical illustration of our data. There are four distinct characteristics: 1) The oil prices of WTI, Brent, Dubai and Daqing are highly correlated. 2) There is a general upward trend throughout the sample period until August 2008. Since then, the financial crisis heavily influenced the global markets. The oil prices dropped and then gradually began to increase. 3) The volatility is high, especially in the last five years, and 4) The price difference between various oil markets has widened in recent months. According to the descriptive statistics and the ARCH test, all of the return series are highly non-normally distributed with conditional
heteroskedasticity. The details of the ARCH test are not reported for simplicity. Table 1 provides the summary statistics of crude oil returns in four countries. The Dubai oil price has the highest average oil returns and the largest extreme value over the sample period. The Dubai oil market also exhibits positive skewness, which demonstrates that it has undergone substantial price hikes. The non-zero skewness and positive excess kurtosis implies the fat-tail distribution of crude oil returns. The WTI oil market has the highest volatility. 3.2. Analysis of crude oil market efficiency We use the generalized spectral test to obtain the empirical results of the EMH test of the main worldwide crude oil markets of the whole samples. We choose the lag length to be 25, 50, and 75 trading days and calculate the M1 statistics for the WTI, Brent, Daqing and Dubai return series. The results that are presented in Table 2 are based on the sample period from December 28, 2001 through April 18, 2013 with the non-match data deleted. With respect to WTI and Brent, we cannot reject the EMH hypothesis according to the results. The conclusion is consistent with the existing research that finds that these two oil markets are weak-form efficient. However, with respect to the Daqing and Dubai oil markets, the results are mixed. The M1(25) statistic demonstrates a rejection of the EMH at the 10% significance level, whereas the M1(50) and M1(75) statistics are different. The reason for this result might be that the efficiency of these two markets is complex and time-varying for the sample period as a whole. To be more explicit, we examined the subsamples before and after October 15, 2008 separately. The results in Table 3 contribute to an explanation of the contradictory conclusion. The strong rejection of the EMH in the early stage may be a result of an imperfect institutional arrangement, frequent government policy intervention, and the irrational investor behavior in the Table 1 Summary statistics of crude oil returns (%) (2001.12.28–2013.4.18).
Mean Median Maximum Minimum Std. Dev. Skewness Excess Kurtosis
Daqing
WTI
Brent
Dubai
0.059 0.083 12.43 −13.22 2.151 −0.189 3.825
0.049 0.126 16.41 −15.19 2.447 −0.090 4.654
0.058 0.085 18.13 −16.83 2.215 −0.026 4.698
0.059 0.132 31.73 −28.36 2.178 0.105 28.125
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Table 2 The M1 statistics of the GS test for four oil markets.
Table 4 The M1 statistics of the GS test for residuals after linear filtering.
Lags
25
50
75
Lags
25
50
75
WTI Brent Daqing Dubai
−0.52 [0.70] −0.27 [0.61] 1.64 [0.05] 1.41 [0.08]
−0.41 [0.66] −0.29 [0.61] 0.86 [0.20] 0.86 [0.19]
−0.40 [0.65] −0.35 [0.64] 0.47 [0.32] 0.36 [0.36]
WTI Brent Daqing Dubai
−0.62 [0.73] −0.29 [0.61] 0.46 [0.32] 1.62 [0.05]
−0.46 [0.68] −0.30 [0.62] 0.18 [0.43] 1.10 [0.14]
−0.47 [0.68] −0.35 [0.64] −0.02 [0.51] 0.58 [0.28]
Note: The numbers in brackets are the corresponding p-values.
Note: The numbers in brackets are the corresponding p-values.
Daqing oil market. Following the financial crisis, the Daqing oil market became weak-form efficient. This finding suggests that the Daqing oil market efficiency changed over time. This type of change also exists in the Dubai market. To detect this type of change, a rolling test is required. This test is outlined in the next section. For robustness, we filter linear serial dependence through ARMA modeling and use GARCH to filter conditional heteroskedasticity. We then use the GS test for the corresponding residuals. The lag orders are selected via the AIC criteria. The results are presented in Table 4. After linear filtering, the statistic for the residuals of the Daqing market cannot reject the EMH, which implies that the non-efficiency is partly caused by linear serial dependence. Unlike the Daqing market, the non-efficiency of the Dubai market may result from conditional heteroskedasticity, which can be seen in Table 5. The results further prove the effectiveness of the GS method in dealing with any serial dependence.
representations of the p values are presented in Fig. 2 (Brent and WTI) and Fig. 3(Dubai and Daqing) in 100, 250 and 1000 days window respectively.
3.3. The rolling sample analysis The evolution and turbulence of the global oil markets, especially the collapse of Lehman Brothers, increase the likelihood of structural breaks in crude oil markets during the sample period. It has been demonstrated that the determinants of oil price dynamics change over time (Baumeister and Peersman, forthcoming) and that the effects of oil price shocks on economic activity are time varying (Baumeister and Peersman, 2013). Therefore, it seems impossible that any model with constant parameters can adequately capture oil market dynamics over time. Moreover, the level of market efficiency may depend on time periods. The empirical results in Alvarez-Ramirez et al. (2010) suggest that crude oil markets exhibit complex dynamics, with stochastic (relative to fluctuation scaling) price properties depending on the time-scale, sampling frequency and time. Therefore, although the complete sample table provides a summary of “average” market efficiency behavior, it might lack potentially significant secular and cyclical efficiency movements. To test for the evolution of market efficiency, we use a rolling window technique. After determining a sample size, the start and end sample date moves forward at the same time. As the window moved forward, we re-calculate the corresponding p values of the M1 test with respect to each subsample using the generalized spectral analysis. The technique of rolling windows is an effective method that can be used to test the robustness of empirical results, which is critical for time series models (Swanson, 1998). We choose three different window lengths, 100 days (approximately 4 months), 250 days (approximately one year) and 1000 days (approximately 4 years). The choice of observations (approximately 1 or 4 years of data) was designed to ensure comparability with previous studies that have focused on general indices. We assess the extent and the nature of the M1 statistics variations over time via the corresponding time series of p values. The graphical
3.3.1. The WTI and Brent oil markets If the p-values of the M1 statistics are smaller than 0.05, we confirm that the market is not efficient at the 5% significance level. With respect to the 100-day and 250-day window lengths, the p-values for the WTI and Brent return series are always higher than 0.05, which indicates that these two markets are weak-form efficient most of the time. Certain extreme events cause inefficiency in a short period of time (e.g., the Gulf War in 1991 and the recent financial crisis in 2008). This finding is consistent with the evidence in Wang and Wu (2012) that extreme events can cause lower levels of short-term market efficiency. With respect to the 1000-day window, the p-values are also higher than the 0.05 level at most time periods. Regardless of which window length is used, the crude oil markets do not exhibit higher levels of efficiency during the 1990s than during the later 1980s. This implies that the crude oil market deregulation of the US did not significantly improve the market's efficiency. The exceptions were during the periods 1991 to 1992 and 1997 to 1999. Our results confirm the findings in AlvarezRamirez et al. (2010) that persistence is not displayed for the subperiod 1992 to 1997. 3.3.2. The Dubai and Daqing oil markets Fig. 3 presents the p-values of the M1 statistics for the Dubai oil return series. With respect to the 100-day and 250-day windows, the pvalues are larger than 0.05 most of the time. Overall, the Dubai markets are always efficient in weak form. With respect to the 1000-day window, the p-values display an uptrend after 2005, which demonstrates that the Dubai oil market is becoming efficient over time. There is an appearance of market inefficiency in 2004 for the Dubai market, but not for the WTI or Brent markets during the same period. In March 2004, OPEC agreed to cut production. April 2004 witnessed the first major terrorist attack on government installations in Riyadh, the capital of Saudi Arabia and the largest crude oil producer in the world. These events led to persistent increases in the price of oil that was traded in the Middle East, which could explain the market inefficiency because the Dubai oil price is the benchmark for oil pricing in this area. For Daqing, with respect to the 100-day and 250-day windows, the p-values for the Daqing returns are always higher than 0.05. Certain exceptions can be explained by the influence of supply or demand shocks as discussed in the case of Dubai. With respect to the 1000-day window, a distinguishing feature is that the Daqing oil market is becoming more efficient, especially after 2004. This phenomenon can be explained by recent Chinese government efforts to deregulate its crude oil markets.
Table 5 The M1 statistics of the GS test for residuals after filtering for conditional heteroskedasticity. Table 3 The M1 statistics of the GS test for Daqing before and after the financial crisis. Lags
25
50
75
Before crisis After crisis
4.50 [0.00] −1.20 [0.88]
2.83 [0.00] −1.00 [0.84]
2.01 [0.02] −0.83 [0.80]
Note: The numbers in brackets are the corresponding p-values.
Lags
25
50
75
WTI Brent Daqing Dubai
−0.49 [0.69] −0.27 [0.61] 0.69 [0.25] 0.19 [0.43]
−0.36 [0.64] −0.29 [0.61] 0.26 [0.40] 0.13 [0.45]
−0.35 [0.64] −0.35 [0.64] 0.02 [0.49] −0.16 [0.56]
Note: The numbers in brackets are the corresponding p-values.
B. Zhang / Energy Economics 40 (2013) 875–881
Fig. 2. The p-values of the WTI and Brent M1 statistics series under the conditions of 100-, 250- and 1000-day window lengths separately.
3.3.3. A comparison of different efficiency levels With respect to the levels of efficiency of different markets, the WTI and Brent markets are more efficient than the Dubai and Chinese Daqing oil markets. This can be explained by the following two reasons. First, the Brent and WTI markets have well-established perfect oil futures and derivatives markets. Second, after 2000, the investment banks and securities firms swarmed into the oil markets. Commodities index purchases increased from $15 billion in 2003 to at least
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Fig. 3. The p-values of Daqing and Dubai M1 statistics series under the conditions of 100-, 250- and 1000-day window lengths separately.
$200 billion by mid-2008. This increased interest from index investors accelerated the synchronized financialization of oil. Moreover, the efficiency of the Brent oil market is not always higher than that of the WTI market, which is demonstrated in Fig. 2. These findings are not consistent with those of Charles and Darne (2009). With respect to the efficiency of the Dubai and Daqing oil markets, Dubai always exhibits a
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B. Zhang / Energy Economics 40 (2013) 875–881
higher efficiency level than the Daqing market. The Daqing oil market is inefficient in the early periods. The early stage inefficiency of the Chinese domestic crude oil market can be attributed to the distribution system and pricing mechanism. There are only two types of traders, oil fields and refinery plants, in the crude oil trading business. Therefore, even if economic agents predict that oil is overvalued or undervalued, there is no access to arbitrage transactions. Therefore, the Daqing oil price did not reflect the known information, which led to the departure from the EMH. China has become a net importer of crude oil since 1996 and the import dependence has exceeded 51% in 2008. In this sense, Chinese oil prices are largely affected by oil prices in other international markets. The market integration has improved over time, therefore, the market efficiency has improved greatly in recent years. Detailed explanations are provided in Section 4. 4. Conclusions and further explanations This paper investigates the efficiency of four major crude oil markets worldwide. A new statistical procedure, the generalized spectral test method, is utilized that can capture linear and nonlinear serial dependence. This method is robust to unknown forms of conditional heteroskedasticity and all of the lag lengths and thus possesses adequate power against departures from market efficiency. Based on the rolling sample technique, our results suggest that oil market efficiency is dependent on the time period. This finding implies that time-varying parameter models might be superior to constantparameter models in capturing oil market dynamics and the relationships between oil prices and economic variables. The related empirical investigations are presented in Baumeister and Peersman (2013, forthcoming). Overall, the WTI, Brent and Dubai oil markets are efficient in the majority of periods, which implies low predictability levels of crude oil prices. From an empirical study using a 1000-day window length, there is evidence that the crude oil market with respect to Brent has been less efficient than the WTI since 2005. An apparent anti-synchronization is detected between the Brent and WTI markets in recent years. This can be explained as follows: relative changes in supply and demand forces affect the prices of crude oil, which is thought to be the main factor that contributes to oil market efficiency in the long-term. The volatility of crude oil price will increase when deviations occur in supply and demand. The crude oil price of the WTI is more a reflection of the supply and demand situation of US domestic oil, whereas the Brent crude oil price is mainly affected by the relationship between the supply and demand with respect to Asia and Europe. During the period from 2001 to early 2005, oil supplies of the WTI market were severely depleted due to multiple factors. In 2001, the “9.11” incident had a significant impact on the WTI market and by the end of 2002, the U.S. crude oil inventories had fallen to their lowest level in the past 20 years. In 2003, the United States launched a military operation in Iraq. Subsequently, “refinery problems”, a “hurricane” and “Iran's nuclear issue” all had a significant impact on the U.S. oil supply while there was a greater domestic demand for U.S. crude oil. With respect to the period 2001 to early 2005, a serious imbalance between supply and demand led to significantly weaker efficiency of the WTI market compared to that of Brent. However, after 2005 the U.S. Cushing crude oil inventory increased rapidly and the U.S. imports of crude oil from Canada also began to increase. After the 2008 financial crisis the U.S. crude oil inventories had been at a higher level, and at the beginning of 2011 the Cushing inventories rose to a record high. In comparison to the increasing supply, the weakness of the US economy post-financial crisis and the rapid increase of US coal bed methane, shale gas and other alternative energy relieved the overheated demand for crude oil. An adequate supply eased the upward pressure on the oil prices of the WTI market. However, Brent's situation had been worrying since 2005: European crude oil production had plummeted by 41% within 10 years, which led to a tight supply of crude oil and increased external dependency. Moreover, because nearly 40% of crude oil in Europe was transported from the
Middle East area through the Suez canal, unrest in the Middle East such as the Arab–Israeli conflict, the Israeli–Palestinian dispute, instability in Lebanon, the war in Libya and Syria, terrorism and religious extremism all had a substantial impact on Europe's crude oil supply. Contrastingly, the oil price of the WTI was less affected by the situation in the Middle East. Additionally, after the financial crisis, certain investment funds were withdrawn from the WTI market and transferred to the Brent market. Consequently, driven by speculative funds, the Brent crude oil market often contained more irrational premium ingredients and Brent's market efficiency decreased. Prior to 2005, an imbalance between supply and demand led to weaker efficiency of the WTI market compared to the Brent market and, after 2005, a sharp decline in Europe's crude oil supply and turmoil in the Middle East in addition to other external factors resulted in significantly weaker efficiency of the Brent market compared to the WTI market. The Chinese Daqing oil market was not efficient prior to 2004, but has become more efficient in recent years. Because the oil market in China was not yet fully competitive, and the ability of society to bear high oil prices remained weak, the government persisted in regulating oil product prices. With respect to the price mechanism of the Daqing oil market, China's oil price reforms aimed to move gradually towards more marketoriented prices with the ultimate goal of price formation through competitive markets. In 1998, China established a mechanism for domestic oil prices that was consistent with the international market. Oil products prices could now be set based on international crude oil prices, and taking into consideration processing costs, the quality of crude oil, taxes and appropriate profit margins. The Chinese Daqing crude oil price was based on the Asia market and the main Chinese oil companies use Dubai prices to calculate their company taxes. Moreover, the prices of domestic petroleum products have been integrated with international markets since June, 2000. The Middle East is a vital oil source for China and the Dubai oil market affects the Daqing oil market. However, the oil demand of China has been a major force in supporting the price of oil that is produced in the Middle East because the energy link and trade between the two has gradually increased in recent years. Due to recent developments in the close oil relationship between China and the Middle East, the Daqing and Dubai oil markets are mutually advantageous. These changes in price setting mechanisms cause similar pricing, and we therefore find evidence of market efficiency synchronization between the Daqing and Dubai oil markets during the last decade. Acknowledgments The authors acknowledge the financial support from the China National Science Fund 71171108 and 71371096 and we thank two referees for providing us suggestions and Dr Huifeng Pan for providing us the Gauss codes. References Alvarez-Ramirez, J., Alvarez, J., Rodriguez, E., 2008. Short-term predictability of crude oil markets: a detrended fluctuation analysis approach. Energy Econ. 30 (5), 2645–2656. Alvarez-Ramirez, J., Alvarez, J., Solis, R., 2010. Crude oil market efficiency and modeling: insights from the multiscaling autocorrelation pattern. Energy Econ. 32 (5), 993–1000. Baumeister, C., Peersman, G., 2013. The role of time-varying price elasticities in accounting for volatility changes in the crude oil market. J. Appl. Econ. (forthcoming). Baumeister, C., Peersman, G., 2013. Time-varying effects of oil supply shocks on the US economy. Am. Econ. J. Macroecon. 5 (4), 1–28. Charles, A., Darne, O., 2009. The efficiency of the crude oil markets: evidence from variance ratio tests. Energy Policy 37 (11), 4267–4272. Chen, Max, Hong, Yongmiao, 2003. Has Chinese stock market become efficient? Evidence from a new approach. Lect. Notes Comput. Sci 2658, 90–98. Fama, E.F., 1970. Efficient capital markets: a review of theory and empirical work. J. Financ. 25, 383–417. Fernandez, V., 2010. Commodity futures and market efficiency: a fractional integrated approach. Resour. Policy 35, 276–282. Hong, Y., Lee, Y.J., 2005. Generalized spectral tests for conditional mean models in time series with conditional heteroscedasticity of unknown form. Rev. Econ. Stud. 72 (2), 499–541.
B. Zhang / Energy Economics 40 (2013) 875–881 Lean, H.H., McAleer, M., Wong, W.-K., 2010. Market efficiency of oil spot and futures: a mean-variance and stochastic dominance approach. Energy Econ. 32, 979–986. Lescaroux, F., Mignon, V., 2008. On the influence of oil prices on economic activity and other macroeconomic and financial variables. OPEC Energy Rev. 32, 343–380. Lv, W.D., Pan, H.F., 2009. Weak form market efficiency test of oil markets based on generalized spectrum method. ICMSE 2009. Maslyuk, S., Smyth, R., 2008. Unit root properties of crude oil spot and futures prices. Energy Policy 36, 2591–2600. Swanson, N.R., 1998. Money and output viewed through a rolling window. J. Monet. Econ. 41 (3), 455–474.
881
Tabak, B.M., Cajueiro, D.O., 2007. Are the crude oil markets becoming weakly efficient over time? A test for time-varying long-range dependence in prices and volatility. Energy Econ. 29 (1), 28–36. Wang, Y., Liu, L., 2010. Is WTI crude oil market becoming weakly efficient over time? New evidence from multiscale analysis based on detrended fluctuation analysis. Energy Econ. 32 (5), 987–992. Wang, T., Yang, J., 2010. Nonlinearity and intraday efficiency tests on energy futures markets. Energy Econ. 32 (2), 496–503. Wang, Y., Wu, C., 2012. Long memory in energy futures markets: further evidence. Resour. Policy 37, 261–272.