University of Pretoria Department of Economics Working Paper Series
Geopolitical Risks and the Predictability of Regional Oil Returns and Volatility Riza Demirer Southern Illinois University Edwardsville Rangan Gupta University of Pretoria Qiang Ji Chinese Academy of Sciences and University of Chinese Academy of Sciences Aviral Kumar Tiwari Montpellier Business School Working Paper: 2018-60 September 2018
______Department of Economics University of Pretoria 0002, Pretoria South Africa Tel: +27 12 420 2413 Geopolitical Risks and the Predictability of Regional Oil Returns and Volatility
Riza Demirer*+, Rangan Gupta**, Qiang Ji*** and Aviral Kumar Tiwari****
*Department of Economics & Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026- 1102, USA. Email: [email protected]
**Department of Economics, University of Pretoria, South Africa. Email: [email protected]
***Center for Energy and Environmental Policy Research, Institutes of Science and Development, Chinese Academy of Sciences, Beijing, 100190, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, 100049, China. Email: [email protected]
****Montpellier Business School, Montpellier, France. Email: [email protected]
Abstract
This paper hypothesizes that global geopolitical risks (GPRs) can predict oil market return and volatility. For our purpose, we use a k-th order nonparametric causality-in- quantiles test, applied to a daily data set covering the period of 15th May, 1996 to 31st May, 2018 of six oil prices (the Nigerian Bonny Light, Brent, Dubai, OPEC, Tapis, and WTI). Our results indicate that the relationship between oil returns and GPRs is highly nonlinear and hence, linear tests of Granger causality cannot be relied upon. Based on the data-driven econometric method, we observe that GPRs have predictability for oil returns of the West African Bonny Light, OPEC and Tapis, while in terms of volatility, causality is observed for all oil prices barring the case of Dubai. In sum, the impact of GPRs is primarily on volatility of oil markets, but more importantly, the impact of GPRs is not uniform across the oil markets. Keywords: Geopolitical Risks, Oil Prices, Nonparametric Causality-in-Quantiles Test JEL Codes: C22, C32, Q41
+ Corresponding author.
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1. Introduction
Oil market movements (in both return and volatility) are known to predict recessions (Hamilton, 1983, 2008, 2009, 2013; Elder and Serletis, 2010), inflation (Stock and Watson, 2003), as well as other commodity and financial markets (Gupta and Yoon, 2018). In addition, oil is indispensable for industrial, transportation, and agricultural sectors, whether used as feedstock in production or as a surface fuel in consumption (Mensi, et al., 2014). Naturally, accurate prediction of oil market movements is of importance to academics, investors and policymakers alike. Understandably, there exists a large literature (see Baumeister, 2014; Lux et al., 2016; Degiannakis and Filis 2017a, b; and Gupta and Wohar, 2017 for detailed reviews) aiming to predict oil price movements using various types of econometric methodologies (univariate and multivariate; linear and nonlinear), and predictors (macroeconomic, financial, behavioural, institutional). In this regard, more recent studies by Bloomberg et al., (2009), Fattouh (2011), Antonakakis et al., (2017a, b), Caldara and Iacoviello (2018), Cunado et al., (2018), Demirer et al., (2018), and Plakandaras et al., (2018) have related oil price movements with geopolitical risks (GPRs).1 These studies point out that, since GPRs affect the economic conditions of both developed and emerging markets, and oil prices are functions of the state of the economy, it is expected that oil market movements are likely to be affected by risks associated with geopolitical events through the oil- demand channel. In addition, with GPRs also affecting financial markets (Balcilar et al., 2018a), and with oil and financial markets closely connected, such risks can also affect the oil prices indirectly through asset markets. All the above-mentioned studies relating GPRs with movements in the oil markets, however, are restricted to either the West Texas Intermediate (WTI) oil price, or a measure of world oil price, via the U.S. Crude Oil Imported Acquisition Cost by Refiners. However, there is widespread evidence that the possibility of a global oil market via integration is at best, sample period- or regime-specific, and does not necessarily hold at all points in time (Ji and Fan, 2015, 2016; Jia et al., 2017; Bhanja et al., 2018). Given this, there is no guarantee that the impact of GPRs on WTI or a measure of global oil price can be generalized to other major crude oil prices like Bonny Light, Brent, Dubai, Organization of the Petroleum Exporting Countries (OPEC), and Tapis. This is an important consideration as regional oil markets can have specific risk and wealth implications for the markets they serve and any external shock could have severe impacts on return and volatility dynamics in regional markets due to the market frictions in these markets that investors do not necessarily experience with the widely traded oil types like Brent or WTI. Against this backdrop, the objective of this paper is to analyze the impact of overall GPRs, and risks originating from geopolitical threats and actual attacks, on returns and volatility of the Nigerian Bonny Light, Brent, Dubai, OPEC, Tapis, and WTI oil markets. For our purpose, we use daily data covering the common sample period of 15th May, 1996 to 31st May, 2018, and conduct the predictability analysis based on the k-th order nonparametric causality-in-quantiles test recently developed by Balcilar et al.,
1 Guo and Ji (2013), and Ji and Guo (2015) are somewhat related papers based on internet (Google) searches of events associated with the oil market, and its impact on oil returns and volatility.
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(2018b). As indicated by Balcilar et al., (2018b), the causality-in-quantile approach has the following novelties: First, it is robust to misspecification errors as it detects the underlying dependence structure between the examined time series, which could prove to be particularly important as it is well-known (based on the literature discussed above) that oil returns display nonlinear dynamics with respect to its predictors – something that we show to exist formally via statistical tests in our case as well. Second, via this methodology, we are able to test not only for causality-in- mean (1st moment), but also for causality that may exist in the tails of the joint distribution of the variables, which in turn, is important if the dependent variable has fat-tails – a feature we show below to hold for oil returns. Finally, we are also able to investigate causality-in-variance and, thus, study impact on volatility. Such an investigation is important because, during some periods, causality in the conditional- mean may not exist while, at the same time, higher-order interdependencies may turn out to be significant. To the best of our knowledge, this is the first paper that evaluates the predictive power of various global geopolitical risks for different oil market returns and volatility based on a nonparametric causality-in-quantiles framework. An important issue to highlight at this stage is the realization that measuring geopolitical risks, which has traditionally been associated with terror attacks only, and hence modelled via a dummy, is much broader and hence, not straight-forward to capture and incorporate into time-series models involving continuous data. However, Caladara and Iacoviello (2018) has constructed indices of GPRs by counting the occurrence of words related to geopolitical tensions in leading international newspapers, and circumvent the above-mentioned issues. Given this, in our predictability exercise, we use the various GPRs indexes developed by these authors. In addition, it is also important to point out that, unlike the existing studies dealing with oil market movements and geopolitical risks based on low-frequency (monthly) data, we rely on daily data. This, we believe, is important, given that oil price is considered to act as a leading indicator for the economy, and hence, prediction of oil price movement, at a higher frequency would provide policymakers information about the future path of lower-frequency variables like output and inflation. The rest of this paper is organized as follows: Section 2 describes the econometric framework involving the higher- moment nonparametric causality-in-quantiles test. Section 3 presents the data and discusses the empirical results, with Section 4 concluding the paper.
2. Econometric Framework
In this section, we briefly present the methodology for the detection of nonlinear causality via a hybrid approach as developed by Balcilar et al., (2018b), which in turn is based on the frameworks of Nishiyama et al., (2011) and Jeong et al., (2012). We start by denoting the six oil returns (considered separately) by yt and the predictor variable (in our case, the various GPR indexes considered one at a time, as discussed in the data segment in detail) as xt. We further let Yt1 (yt1,...,ytp ) , X (x ,..., x ) , Z (X ,Y ) and F (y ,Z ) and F (y ,Y ) denote t 1 t1 t p t t t yt |Zt1 t t1 yt |Yt 1 t t1 the conditional distribution functions of yt given Z t1 and Yt1 , respectively.
Denoting Q (Zt1 ) Q (yt | Zt1 ) and Q (Yt1 ) Q (yt | Yt1 ) , we obtain
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F {Q (Z )| Z } with probability one. As a result, the (non)causality in the yt |Zt1 t1 t1 -th quantile hypotheses to be tested are: H : P{F {Q (Y )|Z } } 1, (1) 0 yt|Zt1 t1 t1 H : P{F {Q (Y )|Z } } 1. (2) 1 yt |Zt1 t1 t1
Jeong et al. (2012) use the distance measure J {t E(t | Zt1) fz (Zt1)}, where t is the regression error term and f z (Zt1) is the marginal density function of Zt1. The regression error t emerges based on the null hypothesis in (1), which can only be true if and only if E[1{yt Q (Yt1) | Zt1}] or, expressed in a different way,
1{yt Q (Yt1)} t , where 1{} is the indicator function. Jeong et al. (2012) show that the feasible kernel-based sample analogue of J has the following format: 1 T T Z Z Jˆ K t1 s1 ˆˆ . (3) T 2 p t s T (T 1)h t p1 s p1,st h where K () is the kernel function with bandwidth h , is the sample size, is the lag ˆ order, and t is the estimate of the unknown regression error, which is given by
ˆt 1{yt Q (Yt1)} . (4) ˆ th Q (Yt1) is an estimate of the conditional quantile of yt given Yt1 , and we ˆ estimate Q (Yt1) using the nonparametric kernel method as Qˆ (Y ) Fˆ 1 ( |Y ) , (5) t1 yt |Yt 1 t1 where Fˆ ( y | Y ) is the Nadarya-Watson kernel estimator given by yt |Yt 1 t t 1 T L (Y Y ) h 1( y y ) t1 s1 s t ˆ s p1,st , (6) Fy |Y ( yt |Yt1) T t t1 L (Y Y ) h s p1,st t1 s1 with L() denoting the kernel function and h the bandwidth.
As an extension of the framework in Jeong et al. (2012), Balcilar et al., (2018b) develop a test for the second moment which allows us to test the causality between the GPRs and oil market volatility. Adapting the approach in Nishiyama et al., (2011), higher order quantile causality can be specified in terms of the following hypotheses as:
H0 : P{F k {Q (Yt1)|Zt1} } 1 for k 1,2,..., K (7) yt |Zt1
H1 : P{F k {Q (Yt1)|Zt1} } 1 for k 1,2,..., K (8) yt |Zt1
The entire framework can be integrated to test whether xt Granger causes yt in quantile up to the kth moment using Eq. (7) by constructing the test statistic in Eq.
(6) for each k . The causality-in-variance test can then be calculated by replacing yt 2 in Eqs. (3) and (4) with yt - measuring the volatility of oil returns. However, one can show that it is difficult to combine the different statistics for each k 1,2,..., K into
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one statistic for the joint null in Eq. (7) because the statistics are mutually correlated (Nishiyama et al., 2011). Balcilar et al., (2018b), thus, propose a sequential-testing method as described in Nishiyama et al. (2011). First, as in Balcilar et al. (2018b), we test for the nonparametric Granger causality in the first moment (i.e., k=1). Nevertheless, failure to reject the null for k 1 does not automatically lead to no- causality in the second moment. Thus, we can still construct the test for k 2, as discussed in detail in Balcilar et al. (2018b).
The empirical implementation of causality testing via quantiles entails specifying three key parameters: the bandwidth (h), the lag order (p), and the kernel type for ∙ and ∙ . We use a lag order of one based on the Schwarz information criterion (SIC), which is known to select a parsimonious model as compared with other lag-length selection criteria, and hence, help us to overcome the issue of the over- parameterization that typically arises in studies using nonparametric frameworks. For each quantile, we determine the bandwidth parameter (h) by using the leave-one-out least-squares cross validation method. Finally, for ∙ and ∙ , we use Gaussian kernels.
3. Data and Results 3.1. The Data
Our analysis relies on data for six oil prices (Brent, Dubai, OPEC, Tapis, Nigerian Bonny Light (West Africa) and WTI), and three indices of GPRs (aggregate, acts, and threats) covering the daily period of 15th May, 1996 to 31st May, 2018 (i.e., 5753 observations), with the start and end dates being driven purely by data availability of the variables under consideration. Five major crude oil markets were selected as representatives, each located in different regions and with specific API gravity and sulphur content. There are three primary benchmarks: WTI, Brent and Dubai. Other well-known crude oils include Tapis crude and Bonny light crude oil. Tapis crude is produced in Malaysia and often used as a price reference for Asia and Australia. Bonny light oil is a Nigerian low sulphur and light crude oil which makes it more preferred to oil refinery due to its lower corrosiveness and environmental impact. Especially, Nigeria is the largest oil producer in Africa where Bonny crude oil is an important import source for American and European refineries. Thus, Bonny crude oil is usually selected as the price representative in Africa. Therefore, the WTI, Brent, Dubai, Nigeria and Tapis crude oil prices were selected based on geographic distribution, data availability and the importance of the market, representing America, Europe, the Middle East, Africa and the Asia Pacific, respectively. We also consider the OPEC Reference Basket (ORB), also referred to as the OPEC Basket, is a weighted average of prices for petroleum blends produced by OPEC members.2 It is used as an important benchmark for crude oil prices. OPEC has often attempted to
2 Since January 1, 2017, the OPEC reference basket consists of a weighted average of the following crudes: Saharan Blend (from Algeria), Girassol (from Angola), Oriente (from Ecuador), Rabi Light (from Gabon), Iran Heavy (from Iran), Basra Light (from Iraq), Kuwait Export (from Kuwait), Es Sider (from Libya), Bonny Light (from Nigeria), Qatar Marine (from Qatar), Arab Light (from Saudi Arabia), Murban (from UAE), and Merey (from Venezuela).
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keep the price of the OPEC Basket between upper and lower limits, by increasing and decreasing production. This makes the measure important for market analysts. The OPEC Basket, including a mix of light and heavy crude oil products, is heavier than both Brent and WTI crude oils. Data on oil prices are obtained from the Datastream database of Thomson Reuters. We work with log-returns to ensure stationarity, as required by our econometric approach.
As mentioned earlier, daily data on geopolitical risk (GPRD) are based on the work of Caldara and Iacoviello (2018). 3 Caldara and Iacoviello (2018) construct the GPR index by counting the occurrence of words related to geopolitical tensions, derived from automated text-searches in 11 leading national and international newspapers (The Boston Globe, Chicago Tribune, The Daily Telegraph, Financial Times, The Globe and Mail, The Guardian, Los Angeles Times, The New York Times, The Times, The Wall Street Journal, and The Washington Post). They then calculate an index by counting, in each of the above-mentioned 11 newspapers, the number of articles that contain the search terms above for every day starting in 1985. The index is then normalized to average a value of 100 in the 2000-2009 decade.
The search identifies articles containing references to six groups of words: Group 1 includes words associated with explicit mentions of geopolitical risk, as well as mentions of military-related tensions involving large regions of the world and a U.S. involvement. Group 2 includes words directly related to nuclear tensions. Groups 3 and 4 include mentions related to war threats and terrorist threats, respectively. Finally, Groups 5 and 6 aim at capturing press coverage of actual adverse geopolitical events (as opposed to just risks) which can be reasonably expected to lead to increases in geopolitical uncertainty, such as terrorist acts or the beginning of a war. Based on the search groups above, Caldara and Iacoviello (2018) further disentangle the direct effect of adverse geopolitical events from the effect of pure geopolitical risks by constructing two indexes. The Geopolitical Threats (GPRD_THREAT) index only includes words belonging to Search groups 1 to 4 above. The Geopolitical Acts (GPRD_ACT) index only includes words belonging to Search groups 5 and 6. The GPR indexes are found to be stationary in their raw level-form.4
The descriptive statistics for all variables are reported in Table A1 in the Appendix. All oil return series are negatively skewed, while the GPR indexes are skewed to the right, with both oil returns and GPRs depicting excess kurtosis, resulting in non- normal distributions. This in turn, provides a preliminary motivation for using the causality-in-quantiles approach rather than a conditional-mean based test of predictability. The average returns among the various oil types range between a high of 2.61% for Dubai and low of 1.99% for WTI. Nigerian oil market experiences the highest return volatility while OPEC is the least volatile, most likely given that it is a basket of various oil types, creating a diversification effect on the volatility of the basket. A plot of the data is presented in Figure A1 in the Appendix.
3The data can be freely downloaded from: https://www2.bc.edu/matteo-iacoviello/gpr.htm. 4 Complete details of the results from the corresponding standard unit root tests are available upon request from the authors.
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3.2. Econometric Results
Before we discuss the findings from the causality-in-quantiles test, for the sake of completeness, we first conducted the standard linear Granger causality test. The resulting 2(1) statistics, as reported in Table 1, indicate that, barring the cases of geopolitical risks due to actual terror attacks (GPRD_ACT) for Brent, Dubai, OPEC, Tapis and WTI oil returns, geopolitical risks predict oil returns.
[INSERT TABLE 1]
Given the possibility that the oil markets are likely to be nonlinearly related with its predictors, we next statistically examine the presence of nonlinearity and structural breaks in the relationship between oil returns and the GPRs. Nonlinearity and regime changes, if present, would motivate the use of the nonparametric quantiles-in- causality approach, as the quantiles-based test would formally address nonlinearity and structural breaks in the relationship between the two variables under investigation. For this purpose, we apply the Brock et al., (1996, BDS) test on the residuals from the oil return equation involving one lag each of oil returns and the GPR indexes. Table 2 presents the results of the BDS test of nonlinearity. As shown in this table, we find strong evidence, at highest level of significance, for the rejection of the null of i.i.d. residuals at various embedded dimensions (m), which in turn, is indicative of nonlinearity in the relationship between oil returns and GPRs. To further motivate the causality-in-quantiles approach, we next used the powerful UDmax and WDmax tests of Bai and Perron (2003), to detect 1 to M structural breaks in the relationship between oil returns and GPRs, allowing for heterogenous error distributions across the breaks. When we apply these tests again to the oil return equation involving one lag of oil return and the various GPRs considered in turn, as shown in Table 3, we detected one break for all the cases considered, except for Tapis and for WTI under GPRD_ACT. These findings indicate that, the positive result of predictability based on the linear Granger causality test, cannot be deemed robust and reliable. [INSERT TABLES 2 and 3]
Given the strong evidence of nonlinearity and structural break(s) in the relationship between oil return and GPRs, we now turn our attention to the causality-in-quantiles test, which is robust to misspecification due to its nonparametric (i.e., data-driven) approach. Besides this, the k-th order test allows us to study the predictability of GPRs on the entire conditional distribution of not only oil return, but also its squared value, i.e., volatility.
Table 4(a) reports the results of the causality-in-quantiles tests for oil returns over the quantile range of 0.05 to 0.95. The values in bold indicate rejection of the null of no- causality at a specific quantile shown at the top of each column. Unlike the results from the linear tests reported in Table 1, the findings in Table 4(a) indicate no causal effects of GPRs on oil returns in the case of WTI, Brent and Dubai with mostly insignificant results for Tapis as well. However, we observe that the null that GPRs do not Granger cause oil returns is rejected at the 5 percent level of significance for the entire conditional distribution of returns for West African Bonny Light and largely for OPEC returns, barring several exceptions at the extreme ends of the conditional
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distribution of OPEC returns and over the quantiles 0.45-0.55. Given that the OPEC reference basket includes the Nigerian Bonny Light, one can argue that the strong causal effects observed in the table are largely driven by the effect of geopolitical risks on the Nigerian Bonny Light. This is an important finding given the evidence in Ji and Fan (2015) that the influence of Nigerian prices on other crude oil prices rose after 2010 and suggests that the Nigerian oil market could serve as the transmitter of possible geopolitical risk effects on other crude oil markets during volatile periods. In sum, when we account for nonlinearity and regime changes, we observe weak evidence in favour of GPRs affecting returns in the oil market, compared to the misspecified linear test results, while the GPR effect on returns is largely concentrated on the Nigerian market.
Next, we turn to Table 4(b), which presents the causality-in-quantiles test results for oil return volatility over the quantile range of 0.05 to 0.95. We observe that the GPR effect on the oil market is generally more widespread in the case of return volatility, which implies significant risk effects. The null that GPRs do not Granger cause oil market volatility is rejected at the 5 percent level of significance for at least one quantile of the conditional distribution of all oil benchmarks, except for the case of Dubai with no significant causal effects observed. Consistent with the findings in Table 4(a), the strongest risk effects of GPRs are observed in the case of OPEC and the Nigerian Bonny Light while return volatility for WTI and Brent is also found to be significantly affected by geopolitical risks. Once again, we observe the weakest evidence of predictability in the case of Dubai and Tapis, the Malaysian benchmark traded in Singapore. It is interesting that these two oil benchmarks are largely catered to Asian and Australian markets and it can be argued that the regional nature of these markets and the lack of a broad trading base with shared and liquid market information available to traders could be driving the heterogeneity of the findings across the different oil benchmarks.5
[INSERT TABLE 3]
In sum, our results confirm the main hypothesis of our paper that GPRs do not necessarily affect the various oil markets in the same manner, and hence, we cannot generalize the impact of GPRs on one market or a global market (which may not actually exist) on to other regional markets.6 While the heterogeneity in the effect of GPRs across the different oil benchmarks could be attributed to the lack of a broad trading base, informational inefficiencies due to liquidity issues depending on the local markets they serve (Reboredo, 2011) and the lack of a price discovery mechanism enabled by active spot and futures markets, the findings reinforce the argument by Ji and Fan (2016) who characterize the world crude oil market as a geographical and organizational structure. Nevertheless, given the finding by Ji and
5 Following the work of Ji and Fan (2015), we computed abnormal returns by subtracting expected oil returns (based on an ARCH model) from actual oil returns. The results for the impact of GPRs on abnormal returns and abnormal squared returns have been reported in Table A2 in the Appendix of the paper. In general, barring the weak evidence of predictability for squared abnormal returns for OPEC, results are qualitative similar to those reported for oil returns and squared returns. 6 Interestingly, unlike Demirer et al., (2018), who found that GPRs affect both oil returns and volatility of the WTI market at monthly frequency over the historical period of 1899:01 to 2017:06, we found that GPRs only affect WTI volatility at daily frequency for shorter sample period.
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Fan (2015) that the influence of Nigerian prices on other crude oil prices rose after 2010, the finding of significant GPR effects on both return and volatility on the Nigerian benchmark could be used to explore the channels by which geopolitical risks are transmitted to the world oil market.
4. Conclusions
In this paper, we study the role of geopolitical risks (GPRs) in affecting the return dynamics of various oil benchmarks including the Nigerian Bonny Light, Brent, Dubai, Organization of the Petroleum Exporting Countries (OPEC) and Tapis. Given the argument that the world crude oil market is characterized as a geographical and organizational structure (Ji and Fan, 2016), we hypothesize that the documented impact of GPRs on WTI or a measure of global oil price, as previous studies have explored, cannot be generalized to other major crude oil prices. To that end, we analyse the impact of overall GPRs (and risks originating from geopolitical threats and actual attacks) on the return and volatility of six oil benchmarks representing various regional markets. Using daily data covering the common sample period of 15th May, 1996 to 31st May, 2018, we conduct the predictability analysis based on the k-th order nonparametric causality-in-quantiles test which addresses non-linearity and structural breaks in the series. Our results indicate that the relationship between oil returns and GPRs is highly nonlinear and hence, linear tests of Granger causality cannot be applied to answer our research question, motivating the use of the nonparametric approach employed in our tests. Based on the data-driven econometric method, we find that the impact of GPRs on the six oil prices is heterogeneous with the strongest causal effect on returns observed in the case of the West African Bonny Light, OPEC and Tapis, while in terms of volatility, causality is observed for all oil prices barring the case of Dubai. The findings generally suggest that the impact of GPRs on the oil market is primarily concentrated on return volatility, rather than returns. Our results highlight that the impact of GPRs is not uniform across the various oil markets and hence, the conclusions derived from a specific market cannot be extended to other markets.
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Table 1: Linear Granger Causality Test Oil Market GPRs 2(1)-statistic p-value GPRD 7.7050 0.0055 BRENT GPRD_ACT 0.2950 0.5871 GPRD_THREAT 9.2288 0.0024 GPRD 5.7271 0.0168 DUBAI GPRD_ACT 2.4665 0.1164 GPRD_THREAT 5.6663 0.0173 GPRD 7.7372 0.0054 OPEC GPRD_ACT 0.8689 0.3513 GPRD_THREAT 8.5793 0.0034 GPRD 5.3716 0.0205 TAPIS GPRD_ACT 2.5403 0.1111 GPRD_THREAT 5.1764 0.0230 GPRD 7.5894 0.0059 WEST AFRICA GPRD_ACT 4.2745 0.0388 GPRD_THREAT 6.9969 0.0082 GPRD 6.3783 0.0116 WTI GPRD_ACT 0.1241 0.7246 GPRD_THREAT 7.8870 0.0050 Note: Bold entries indicate the rejection of the null hypothesis of no-causality at the 5% level of significance.
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Table 2: BDS Test of Nonlinearity GPRs Dimension (m) 2 3 4 5 6 BRENT GPRD 5.434*** 7.865*** 9.094*** 10.147*** 11.021*** GPRD_ACT 5.664*** 8.075*** 9.291*** 10.318*** 11.150*** GPRD_THREAT 5.422*** 7.845*** 9.078*** 10.138*** 11.021*** DUBAI GPRD 8.683*** 10.241*** 11.513*** 12.126*** 12.660*** GPRD_ACT 8.674*** 10.157*** 11.420*** 12.020*** 12.531*** GPRD_THREAT 8.666*** 10.203*** 11.476*** 12.090*** 12.621*** OPEC GPRD 7.505*** 9.813*** 11.424*** 12.584*** 13.708*** GPRD_ACT 7.582*** 9.945*** 11.552*** 12.719*** 13.824*** GPRD_THREAT 7.429*** 9.734*** 11.363*** 12.524*** 13.645*** TAPIS GPRD 7.848*** 10.117*** 11.691*** 12.583*** 13.734*** GPRD_ACT 7.959*** 10.249*** 11.805*** 12.684*** 13.838*** GPRD_THREAT 7.757*** 10.035*** 11.624*** 12.523*** 13.681*** WEST AFRICA GPRD 5.598*** 7.670*** 9.122*** 10.191*** 11.002*** GPRD_ACT 5.734*** 7.793*** 9.232*** 10.277*** 11.080*** GPRD_THREAT 5.593*** 7.653*** 9.101*** 10.175*** 10.982*** WTI GPRD 7.344*** 9.530*** 10.976*** 11.530*** 12.275*** GPRD_ACT 7.566*** 9.721*** 11.168*** 11.713*** 12.443*** GPRD_THREAT 7.241*** 9.419*** 10.880*** 11.446*** 12.193*** Note: Entries correspond to the z-statistic of the BDS test with the null of i.i.d. residuals, with the test applied to the residuals recovered from the oil return equation with one lag each of oil return and various GPRs considered in turn; *** indicates rejection of the null hypothesis at 1 percent level of significance.
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Table 3: Bai and Perron (2003) Test of Multiple Structural Breaks Oil Market GPRs Date GPRD 9/25/2001 BRENT GPRD_ACT 3/24/2003 GPRD_THREAT 9/25/2001 GPRD 9/26/2001 DUBAI GPRD_ACT 4/07/2003 GPRD_THREAT 9/26/2001 GPRD 9/26/2001 OPEC GPRD_ACT 3/24/2003 GPRD_THREAT 9/26/2001 GPRD No break TAPIS GPRD_ACT No break GPRD_THREAT No break GPRD 9/26/2001 WEST AFRICA GPRD_ACT 3/24/2003 GPRD_THREAT 9/26/2001 GPRD 9/25/2001 WTI GPRD_ACT No break GPRD_THREAT 9/25/2001 Note: The test is applied to the oil return equation with one lag each of oil return and various GPRs considered in turn.
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Table 4(a): Causality-in-Quantiles Test for Oil Returns Dependent Independent Quantile variable variable 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 GPRD 1.37 1.61 1.48 1.36 1.46 1.20 1.39 1.56 1.39 0.87 0.71 0.79 1.06 0.94 0.69 0.80 1.12 1.28 1.17 WTI GPRD_THREAT 1.38 1.74 1.51 1.29 1.51 1.29 1.32 1.48 1.36 0.86 0.78 0.83 1.08 0.95 0.68 0.78 1.07 1.25 1.19 GPRD_ACT 1.17 1.31 1.63 1.48 1.23 1.06 1.26 1.21 1.12 0.44 0.32 0.38 0.27 0.27 0.47 0.64 1.41 1.75 1.40 GPRD 0.10 0.12 0.16 0.24 0.21 0.15 0.12 0.06 0.08 0.06 0.05 0.04 0.05 0.04 0.04 0.04 0.01 0.02 0.02 Brent GPRD_THREAT 0.08 0.10 0.14 0.22 0.19 0.15 0.12 0.06 0.08 0.06 0.07 0.06 0.07 0.05 0.05 0.04 0.01 0.02 0.02 GPRD_ACT 0.09 0.10 0.11 0.13 0.09 0.06 0.05 0.03 0.03 0.01 0.01 0.01 0.02 0.03 0.03 0.03 0.04 0.04 0.06 GPRD 1.42 2.33 2.37 3.15 3.27 3.39 3.35 2.81 2.67 2.06 2.61 2.40 2.78 2.17 2.17 2.09 2.82 2.24 1.29 OPEC GPRD_THREAT 1.42 2.39 2.51 3.32 3.25 3.22 3.48 2.94 2.78 2.23 2.91 2.84 2.61 2.21 2.08 1.99 2.74 2.21 1.21 GPRD_ACT 1.75 2.29 3.39 3.82 4.40 3.85 3.14 2.57 1.94 1.45 1.73 2.05 2.85 2.56 2.58 2.58 2.45 2.16 1.38 GPRD 0.48 0.51 0.74 0.70 0.76 0.90 0.83 0.66 0.74 8.16 0.30 0.39 0.45 0.43 0.62 0.63 0.75 0.70 0.53 Tapis GPRD_THREAT 0.43 0.48 0.75 0.71 0.69 0.78 0.72 0.61 0.78 8.10 0.30 0.36 0.41 0.42 0.62 0.63 0.76 0.71 0.53 GPRD_ACT 0.48 0.47 0.51 0.41 0.67 0.88 0.71 0.45 0.26 8.58 0.27 0.28 0.37 0.29 0.48 0.54 0.67 0.59 0.57 GPRD 0.14 0.16 0.18 0.19 0.19 0.16 0.12 0.07 0.05 0.02 0.01 0.03 0.06 0.03 0.03 0.03 0.11 0.10 0.07 Dubai GPRD_THREAT 0.11 0.13 0.16 0.17 0.17 0.15 0.12 0.07 0.05 0.02 0.01 0.04 0.08 0.05 0.04 0.04 0.13 0.12 0.08 GPRD_ACT 0.16 0.23 0.26 0.23 0.23 0.16 0.12 0.07 0.06 0.02 0.01 0.01 0.03 0.05 0.07 0.07 0.07 0.10 0.06 GPRD 3.96 5.94 6.97 8.09 8.38 8.97 9.09 9.33 9.82 10.10 10.41 10.25 9.67 9.18 8.69 7.86 6.41 5.68 3.58 West GPRD_THREAT 3.89 5.94 7.08 8.43 9.41 9.75 9.86 10.03 10.31 9.87 10.25 10.28 9.80 8.96 8.41 7.85 6.44 5.22 3.68 Africa GPRD_ACT 2.62 3.90 4.33 4.54 4.40 4.24 4.60 4.45 4.84 4.67 4.86 5.13 4.75 4.38 4.17 3.99 3.56 3.23 2.26 Note: Values in bold indicate rejection of the null of no-causality at a specific quantile at the 5% level of significance.
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Table 4(b): Causality-in-Quantiles Test for Squared Oil Returns (Volatility) Dependent Independent Quantile variable variable 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 GPRD 2.29 2.25 2.64 2.57 2.67 2.63 2.74 3.22 2.88 3.13 3.03 3.10 3.17 2.41 2.36 2.07 1.34 0.84 0.72 WTI GPRD_THREAT 1.93 1.63 2.34 2.21 2.18 2.62 2.63 2.80 2.70 2.90 2.84 2.82 3.46 2.52 2.73 2.39 1.90 1.24 0.89 GPRD_ACT 0.88 0.84 1.42 1.88 1.68 2.05 1.77 2.14 1.70 1.82 1.74 1.43 1.27 1.42 1.20 1.43 1.05 0.62 0.48 GPRD 1.58 1.85 2.25 2.16 2.15 2.04 2.04 2.23 2.17 2.70 2.73 2.57 2.20 2.26 1.85 1.65 1.69 1.31 1.02 Brent GPRD_THREAT 1.24 1.47 2.26 2.22 2.29 2.23 2.40 2.19 2.30 2.85 2.77 2.56 2.33 2.21 1.84 1.84 1.85 1.59 1.08 GPRD_ACT 1.04 1.10 1.06 1.44 2.00 1.86 1.84 2.12 1.98 1.65 1.77 1.71 1.68 1.77 1.82 1.59 1.43 1.20 0.75 GPRD 1.52 2.34 2.72 2.82 3.56 4.26 3.94 3.72 4.07 4.25 4.35 4.06 3.72 3.90 3.67 2.91 2.57 2.76 2.13 OPEC GPRD_THREAT 1.33 1.95 2.48 2.94 3.74 4.62 4.14 3.69 3.86 4.00 4.10 3.91 3.38 3.56 3.64 3.24 2.62 1.88 1.71 GPRD_ACT 0.93 1.22 2.11 2.69 3.68 4.78 5.26 5.71 8.04 6.53 7.79 6.16 6.15 4.81 4.49 3.52 1.89 2.59 1.04 GPRD 12.73 0.17 0.08 0.14 0.16 0.15 0.12 0.15 0.15 0.19 0.22 0.12 0.08 0.04 0.10 0.06 0.05 0.04 0.02 Tapis GPRD_THREAT 12.74 0.16 0.06 0.12 0.13 0.13 0.10 0.13 0.13 0.16 0.19 0.12 0.07 0.04 0.09 0.05 0.03 0.03 0.01 GPRD_ACT 12.49 0.04 0.06 0.06 0.10 0.08 0.05 0.07 0.04 0.07 0.09 0.02 0.02 0.01 0.04 0.03 0.06 0.05 0.02 GPRD 0.02 0.01 0.02 0.01 0.00 0.02 0.04 0.03 0.04 0.03 0.02 0.02 0.01 0.02 0.02 0.01 0.02 0.01 0.00 Dubai GPRD_THREAT 0.02 0.01 0.02 0.01 0.00 0.03 0.06 0.04 0.05 0.03 0.03 0.02 0.02 0.04 0.03 0.03 0.03 0.01 0.00 GPRD_ACT 0.01 0.00 0.01 0.02 0.03 0.04 0.08 0.09 0.12 0.14 0.12 0.16 0.14 0.10 0.08 0.07 0.04 0.03 0.01 GPRD 4.42 6.38 7.95 9.26 10.05 10.51 10.95 11.07 11.58 11.87 11.81 11.11 10.61 10.06 9.73 9.04 8.32 6.48 4.27 West GPRD_THREAT 4.43 6.70 8.31 9.54 10.45 11.34 11.85 11.94 12.41 12.44 12.02 10.88 10.45 9.61 9.03 8.79 8.24 6.17 4.09 Africa GPRD_ACT 1.57 2.34 2.50 2.64 2.93 3.35 2.85 2.55 2.65 2.89 2.77 2.51 2.65 2.61 2.32 2.00 2.04 1.60 1.25 Note: Values in bold indicate rejection of the null of no-causality at a specific quantile at the 5% level of significance.
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APPENDIX: Table A1. Summary Statistics Variables WEST Statistic BRENT DUBAI OPEC TAPIS AFRICA WTI GPRD GPR_THREAT GPR_ATTACK Mean 0.0237 0.0261 0.0240 0.0233 0.0234 0.0199 96.1640 98.4799 83.8227 Median 0.0000 0.0000 0.0547 0.0000 0.0038 0.0000 73.9089 73.4303 46.8786 Maximum 17.9692 18.2833 15.2555 11.8894 55.0370 16.4137 1025.1220 1199.6650 2044.9120 Minimum -18.7247 -16.5017 -15.6308 -11.5446 -55.0370 -17.0918 0.0000 0.0000 0.0000 Std. Dev. 2.3124 2.3565 1.7957 2.0304 2.4471 2.4011 86.9259 94.7165 117.9761 Skewness -0.0131 -0.0261 -0.1224 -0.0712 0.0288 -0.1041 3.1091 3.3116 3.8928 Kurtosis 7.5773 6.8449 7.7773 6.0772 94.6407 7.7012 18.8014 21.3709 33.9395 Jarque-Bera 5021.6270 3543.6530 5484.0640 2274.3170 2012724.0000 5307.2990 69107.7900 91398.1900 243949.0000 p-value 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Observations 5752 5752 5752 5752 5752 5752 5752 5752 5752 Note: Std. Dev: stands for standard deviation; p-value corresponds to the Jarque-Bera test with the null of normality.
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Table A2(a): Causality-in-Quantiles Test for Abnormal Oil Returns Dependent Independent Quantile variable variable 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 GPRD 1.37 1.61 1.48 1.36 1.45 1.20 1.39 1.60 1.39 0.87 0.71 0.81 1.06 0.94 0.69 0.80 1.11 1.28 1.17 WTI GPRD_THREAT 1.37 1.74 1.51 1.29 1.50 1.29 1.32 1.52 1.36 0.86 0.78 0.85 1.08 0.94 0.67 0.78 1.06 1.25 1.19 GPRD_ACT 1.17 1.31 1.63 1.48 1.21 1.06 1.26 1.24 1.12 0.44 0.32 0.39 0.27 0.29 0.46 0.64 1.42 1.80 1.40 GPRD 0.11 0.12 0.16 0.26 0.21 0.16 0.12 0.06 0.08 0.06 0.05 0.04 0.05 0.03 0.04 0.04 0.01 0.01 0.02 Brent GPRD_THREAT 0.09 0.10 0.14 0.23 0.19 0.16 0.12 0.06 0.09 0.06 0.07 0.06 0.07 0.05 0.05 0.04 0.01 0.01 0.02 GPRD_ACT 0.10 0.10 0.11 0.14 0.09 0.06 0.05 0.03 0.03 0.01 0.01 0.01 0.01 0.02 0.03 0.03 0.04 0.03 0.06 GPRD 1.45 2.35 2.37 3.15 3.24 3.40 3.34 2.73 2.62 1.99 2.52 2.29 2.69 2.09 2.11 2.00 2.75 2.17 1.26 OPEC GPRD_THREAT 1.44 2.40 2.49 3.28 3.21 3.23 3.46 2.86 2.74 2.17 2.83 2.74 2.54 2.13 2.04 1.93 2.68 2.14 1.18 GPRD_ACT 1.71 2.26 3.34 3.76 4.27 3.75 3.05 2.45 1.84 1.35 1.59 1.94 2.70 2.38 2.42 2.44 2.37 2.13 1.33 GPRD 0.48 0.51 0.74 0.72 0.76 0.92 0.82 0.66 0.74 6.95 0.30 0.39 0.45 0.43 0.62 0.63 0.75 0.71 0.51 Tapis GPRD_THREAT 0.43 0.48 0.75 0.73 0.69 0.80 0.71 0.61 0.78 6.89 0.30 0.36 0.41 0.42 0.62 0.63 0.76 0.72 0.50 GPRD_ACT 0.48 0.47 0.51 0.42 0.67 0.90 0.73 0.45 0.26 7.30 0.27 0.28 0.37 0.30 0.48 0.54 0.67 0.60 0.56 GPRD 0.15 0.16 0.18 0.20 0.19 0.16 0.12 0.07 0.05 0.02 0.01 0.03 0.06 0.03 0.03 0.03 0.12 0.10 0.07 Dubai GPRD_THREAT 0.12 0.13 0.16 0.18 0.17 0.15 0.12 0.07 0.05 0.02 0.01 0.04 0.08 0.05 0.04 0.04 0.13 0.12 0.08 GPRD_ACT 0.17 0.24 0.26 0.24 0.23 0.16 0.12 0.07 0.06 0.02 0.01 0.01 0.03 0.05 0.07 0.07 0.07 0.10 0.05 GPRD 3.81 5.74 6.70 7.78 8.03 8.61 8.70 8.92 9.40 9.66 10.00 9.83 9.27 8.79 8.33 7.54 6.12 5.44 3.42 West GPRD_THREAT 3.71 5.72 6.81 8.12 9.07 9.40 9.50 9.66 9.92 9.47 9.87 9.88 9.41 8.56 8.05 7.50 6.16 4.99 3.53 Africa GPRD_ACT 2.57 3.84 4.25 4.45 4.30 4.14 4.48 4.33 4.73 4.55 4.73 5.01 4.66 4.27 4.07 3.90 3.47 3.15 2.22 Note: Values in bold indicate rejection of the null of no-causality at a specific quantile at the 5% level of significance.
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Table A2(b): Causality-in-Quantiles Test for Squared Abnormal Oil Returns (Volatility) Dependent Independent Quantile variable variable 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 GPRD 5.94 4.19 4.89 5.42 5.95 6.33 6.54 6.62 6.75 7.17 7.03 6.97 6.80 6.39 5.84 5.52 4.59 3.23 2.10 WTI GPRD_THREAT 5.43 3.95 4.58 4.90 5.66 5.88 6.31 6.78 6.65 6.62 6.38 6.42 6.59 6.41 6.03 5.63 4.30 3.26 2.41 GPRD_ACT 3.59 1.59 2.47 2.96 2.92 3.40 3.06 3.07 3.24 2.73 2.59 2.38 2.21 2.28 2.08 2.09 1.83 1.58 0.98 GPRD 3.50 5.47 5.86 7.13 7.58 7.85 7.67 8.32 8.04 8.15 8.17 7.73 7.35 7.59 6.96 6.00 5.32 4.29 3.04 Brent GPRD_THREAT 3.22 4.32 5.31 6.50 7.08 7.72 7.85 8.23 8.55 8.80 8.87 8.44 7.85 7.53 6.65 6.03 5.53 4.31 3.00 GPRD_ACT 1.74 1.81 1.99 2.59 2.82 2.85 3.04 3.46 3.19 3.19 3.31 2.86 2.79 2.93 3.10 2.61 2.21 1.83 1.28 GPRD 0.21 0.29 0.50 1.31 1.00 1.04 1.48 1.65 1.51 1.28 1.18 0.89 1.18 1.49 0.94 0.91 0.76 0.52 0.66 OPEC GPRD_THREAT 0.20 0.23 0.55 1.57 1.18 1.22 1.57 1.74 1.71 1.66 1.36 1.02 1.26 1.63 1.13 1.17 0.96 0.58 0.71 GPRD_ACT 0.18 0.21 0.29 0.43 0.37 0.71 0.53 0.79 1.83 1.98 1.95 1.66 1.67 1.24 1.15 0.99 0.33 0.33 0.18 GPRD 3.25 0.16 0.12 0.08 0.14 0.14 0.15 0.16 0.18 0.18 0.17 0.16 0.07 0.05 0.12 0.07 0.05 0.04 0.02 Tapis GPRD_THREAT 3.23 0.15 0.11 0.08 0.12 0.13 0.14 0.13 0.16 0.15 0.14 0.15 0.07 0.04 0.10 0.07 0.04 0.03 0.01 GPRD_ACT 3.22 0.04 0.05 0.03 0.08 0.05 0.05 0.08 0.05 0.07 0.06 0.03 0.01 0.02 0.04 0.03 0.06 0.05 0.02 GPRD 0.01 0.01 0.01 0.01 0.01 0.01 0.03 0.03 0.04 0.03 0.02 0.02 0.02 0.02 0.04 0.01 0.01 0.01 0.00 Dubai GPRD_THREAT 0.01 0.01 0.01 0.01 0.01 0.02 0.05 0.04 0.04 0.03 0.03 0.03 0.03 0.04 0.06 0.02 0.02 0.01 0.00 GPRD_ACT 0.01 0.00 0.01 0.02 0.03 0.05 0.08 0.07 0.11 0.11 0.12 0.11 0.09 0.11 0.05 0.07 0.05 0.04 0.01 GPRD 1.09 1.73 1.87 1.85 1.77 1.63 1.77 1.93 2.37 2.60 2.08 2.06 1.97 1.59 1.73 2.11 3.07 1.35 0.61 West GPRD_THREAT 1.01 2.00 1.60 1.40 1.34 1.16 1.37 1.52 1.97 2.16 1.84 1.90 1.51 1.30 1.76 2.26 3.09 1.30 0.55 Africa GPRD_ACT 0.78 0.84 1.82 1.38 2.09 2.48 2.47 1.85 1.37 1.57 1.69 1.12 1.39 2.16 1.35 1.48 1.71 0.75 0.44 Note: Values in bold indicate rejection of the null of no-causality at a specific quantile at the 5% level of significance.
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Figure A1. Data Plot BRENT DUBAI OPEC
20 20 20
10 10 10
0 0 0
-10 -10 -10
-20 -20 -20 1000 2000 3000 4000 5000 1000 2000 3000 4000 5000 1000 2000 3000 4000 5000
TAPIS WEST AFRICA WTI
15 60 20
10 40 10 5 20
0 0 0
-5 -20 -10 -10 -40
-15 -60 -20 1000 2000 3000 4000 5000 1000 2000 3000 4000 5000 1000 2000 3000 4000 5000
GPRD GPR_THREAT GPR_ATTACK
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1,000 2,000 1,200 800 1,500 600 800 1,000 400 400 500 200
0 0 0 1000 2000 3000 4000 5000 1000 2000 3000 4000 5000 1000 2000 3000 4000 5000
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