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Price dynamics of crude oil and the regional ethylene marketsMansur Masih a, , Ibrahim Algahtani b , Lurion De Mello ca Department of Finance and Economics, Center of Research Excellence in Renewable Energy, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabiab Department of Finance and Economics, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabiac Faculty of Business and Economics, Macquarie University, Sydney, Australia

a b s t r a c ta r t i c l e i n f o

Article history:Received 1 December 2006Received in revised form 16 March 2010Accepted 18 March 2010Available online 28 March 2010

Keywords:Crude oil pricesPetrochemical pricesEthylene pricesPrice dynamicsLong Run Structural Modelling (LRSM)

This paper is the rst attempt to investigate: (i) is the crude oil (WTI) price signi cantly related to theregional ethylene prices in the Naphtha intensive ethylene markets of the Far East, North West Europe, andthe Mediterranean? (ii) What drives the regional ethylene prices? The paper is motivated by the recent andgrowing debate on the lead-lag relationship between crude oil and ethylene prices. Our ndings, based onthe long-run structural modelling approach of Pesaran and Shin, and subject to the limitations of the study,tend to suggest: (i) crude oil (WTI) price is cointegrated with the regional ethylene prices (ii) our within-sample error-correction model results tend to indicate that although the ethylene prices in North WestEurope and the Mediterranean were weakly endogenous, the Far East ethylene price was weakly exogenousboth in the short and long term. These results are consistent, during most of the period under review(2000.1 2006.4) with the surge in demand for ethylene throughout the Far East, particularly in China andSouth Korea. However, during the post-sample forecast period as evidenced in our variance decompositionsanalysis, the emergence of WTI as a leading player as well, is consistent with the recent surge in WTI price(fuelled mainly, among others, by the strong hedging activities in the WTI futures/options and re ningtightness) re ecting the growing importance of input cost in determining the dynamic interactions of inputand product prices.

2010 Elsevier B.V. All rights reserved.

1. Introduction

In the last decade, prices of petrochemicals have become verysensitive to movement in crude oil prices. There is also a debateamong researchers and policy makers as to what factors might bedriving up crude oil prices. Some blame it on geo-political events,while others point the nger to the booming economies of India,China and Korea. While some studies have tested the price dynamicsofcrude oil and re nedpetroleum products such as heating oil, gasoil,diesel and gasoline, the market dynamics of petrochemical prices andcrude oil has never been tested before.

Markets for petrochemicals remain small and opaque compared tooil but the trade ow concentrated between Middle East swingproducersand the giant consumers of Asia is on theincrease. Since the1997 Asian nancial crisis, there has been a surge in demand forpetrochemicals from Asia, in particular amongst the Far East (FE)economies of China, Korea, Japan and Taiwan. The demand has alsoincreased in other major centres like North West Europe (NWE) andthe Mediterranean (MED). The increase in sensitivity of petrochem-ical prices to crude oil prices has resulted in rms taking price riskmanagement very seriously in a bid to reduce their costs. Some

analysts have stated that there is as much as an 80% correlationbetween crude oil and petrochemical products. In addition, there is anongoing debate as to the drivers of crude oil prices in addition to thespeculation and geopolitical tensions in the Middle East and constantsupply disruptions in countries such as Nigeria and Venezuela. Inaddition to demand from re ned petroleum products (heating oil,electricity and transportation fuels) policy makers are also interestedin having a better understanding of the dynamics of crude oil pricesand demand for petrochemicals (mainly ole ns and aromatics),which are key feedstocks in the production of nal products such asplastics, polymers, nylon and rubber.

There are two issues that need testing in the petrochemical andcrude oil chemistry; rstly we need to model the dynamics betweencrude oil and petrochemicals which has been largely ignored in theliterature (excepting some petrochemical industry consultancyreports). Secondly, the transparency and availability of petrochemicalprices gives us an opportunity to assess the dynamics between majorpetrochemical markets by using ethylene, which is the barometer forthe petrochemicalmarket. 1 Ourresearch is in line with studies that tryto capture price dynamics between various commodity spot pricesand in particular major between crude oil barometers like the UnitedStates West Texas Intermediate (WTI), U.K North Sea Brent (Brent)

Energy Economics 32 (2010) 1435 1444

Corresponding author. Tel.: +966 3 8602135; fax: +966 3 8602585.E-mail address: [email protected] (M. Masih).

1 To our knowledge the most accurate and independent petrochemical prices arereported by organisations such as www.icis.com and www.platts.com .

0140-9883/$ see front matter 2010 Elsevier B.V. All rights reserved.

doi: 10.1016/j.eneco.2010.03.009

Contents lists available at ScienceDirect

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and the Middle East Dubai (now constituted of Dubai/Oman blend) ina bid to determine the lead lag structure in the crude oil market. 2

Our research is important to policy makers on various fronts.Information on the degree of sensitivity in major ethylene spotmarkets by incorporating the role of crude oil prices is important. If the analysis shows a strong relationship in the intra-ethylene pricedynamics and not with crude oil price, then rms should not hedgeagainst oil prices movements, but if crude oil prices play a signi cant

role in explaining ethylene prices then appropriate risk managementpolicies have to be implemented by ethylene producers andconsumers. Unlike oil markets, petrochemical markets (ethyleneand propylene) do not have derivative instruments like futurescontracts although in recent times contracts in further downstreamproducts like polyethylene and polypropylene are available forhedging and inventory requirements. These contracts are still veryilliquid and rms are still dependent on heating oil and crude oilprices in attempting to manage their risk. 3

The varying degree of sensitivity of different ethylene markets tocrude oil will also help crude oil suppliers in strategic locations liketheMiddle East to swing their shipments from oneregion to theother.If shocks to the variance of ethylene in a particular market areexplained greater by crude oil then there is a possibility of a drop indemand for ethylene in that particular market if oil prices continue torise. This is of interest to oil producers as they can increase their focusto petrochemical markets that are less sensitive to crude oil increases.This also gives suppliers of crude oil opportunity to forecast marketconditions based on movements in prices in various markets and if favourable conditions prevail, re ners can adjust their output byproducing more naphtha which is the main feedstock in thepetrochemical process. Our focus is on the European and Asianmarkets because production of ethylene in Europe and Asia is about80%geared towards using naphtha (product of crude oil), while in theUnited States majority (70%) of its ethylene is from ethane andpropane (product captured mainly from natural gas).

The analysis is also important to traders in commodity plasticssuch as polyethylene (which is three times removed from crude oil inthe production chain) and according to Platts (Insight magazine, Dec2005) traders now track spot-market prices of polyethylene againstcrude oil prices. The old saying that petrochemical prices arederivative driven (downstream demand), i.e., petrochemical pricesare determined mostly by the prices of the nished products they gointo, has been replaced by the realization that the price of crude oilmay have a big impact on petrochemical prices.

Our study is organised as follows. In Section 2 , we give a brief overview of the petrochemical process. Section 3 looks at the role of Asian demand for petrochemicals using China as a case study. Theliterature review in Section 4 is followed by the methodologyemployed in Section 5 . The data, empirical results and discussionstake place in Section 6 . Thestudyends with theconclusionsand policyimplications in Section 7 .

2. The petrochemical process

Before we explain the petrochemical market, a brief discussion onhow petrochemicals come about or rather what happens to the oilwhen it is extracted from the rocks (carbonates) and oil wells iswarranted. In most cases, crude oil is extracted together with pocketsof associated gas. After some application of heat to crude oil in adistillation unit, products such as heavy oil, fuel oil, naphtha, off gases,propane and lique ed petroleum gas (LPG) are produced and these

are classi edas re ned products. Products of a more gaseous state arecaptured rst after exposure to minimal temperature and productslike heavy fuel oil are captured last.

Natural gas in its pure form on the other hand is separated usingvarious methods. The separation process leads to products likepropane and ethane (LPG). Since most of the natural gas is methane,an oxidation coupling method is used to convert methane to ethane.This ethane is then fed through an ethane cracker to produce

petrochemicals like ethylene. Steam reforming is also used to producemethanol that is another key ingredient in family of chemicalproducts.

Re ned products such as naphtha, ethane, propane, butane andfuel oil are classi ed as feedstocks used in producing ole ns such asethylene and propylene. Ethylene and propylene are in turn classi edas petrochemical feedstocks used in the production of polymers andplastics (low density polyethylene, linear-low density polyethyleneand polypropylene). In comparison to propylene, ethylene is easier toproduce and therefore is a popular petrochemical used in manufac-turing products such as polystyrene and plastics. There are variousmethods used to manufacture ole ns such as ethylene and propylene.The easiest and simplest process is through steam cracking of products like naphtha, LPG and ethane. This makes sense since theseare the rst products extracted in the crude oil and natural gasre ning process and due to the gaseous states, only steam is requiredto break the hydrogen and carbon atoms in producing the ole ns.Ole ns produced from heavy fuel oil and other specialised productslike propane go through a different process. For example heavy oil isfed through a deep catalytic cracking , while propane is put througha propane dehydrogenation process. However, not all these stepsresult in ole ns unlikethe simpleproduction by cracking naphtha andpropane. A process called ole n metathesis is required before anyethylene, propylene and other products are produced. A more indepth look at this whole process is given in Fig. 3 (A)( Ren etal.,2006 ).The production process of ethylene through crude oil derivatives likenaphtha makes it crucial to test the dynamics between crude oil andethylene since themarket demandfor ole ns has grownconsiderably.Another step would be to test the dynamics between natural gas andpropylene since propane is the common feedstock in the productionof propylene.

3. Petrochemical demand from China

China's entry into the World Trade Organization (WTO) inSeptember 2001 has enabled the country's chemical industry toharness the bene ts of globalization. Its growing role as a producerand consumer of chemicals and petrochemicals is having a profoundimpact on the global industry. China is the third largest producer of chemicals and petrochemicals globally with growth rates of 10%annually since 1994 and9% through 2005. Throughout 2005, China ledgrowth in the Asia Paci c chemical sector. As the global economygradually recovered, economic growth in China accelerated. Accord-

ing to China's National Bureau of Statistics (NBS), industrial outputgrew 16.1% year-on-year to RMB632 billion (US$78.2 billion) in the rst ten months of 2005 indicating continued strong demand forexports. The rapid development of the country's economy, coupledwith the growth in its industrial output, considerably boosted itsdemand for chemicals.

China consumes more than 9% of the world's chemicals and thecountry's booming economy is responsible for surging chemicals andpetrochemicals growth, with demand exceeding supply across theboard. KPMG forecasts that China's chemical and petrochemicaloutput would increase by 34.5% to US$162.4 billion by 2008. By 2010,ACC expects thesize of thechemicalindustryin China to be equivalentto that of the total European chemical industry, while Merrill Lynch,an international nancial and advisory company, forecasts that by

2015, China will be the second largest chemical market after the US.

2 See Otero and Milas (2001); Fattouh (2010) and references within.3 The National Commodity and Derivative Exchange (NCDEX) in India trade futures

contracts on PP, LLDPE and PVC while the London Metals Exchange (LME) trades PPand LLDPE. (http://www.ncdex.com/product/ProductIndex.aspx http://www.lme.

com/plastics/index.asp ).

1436 M. Masih et al. / Energy Economics 32 (2010) 1435 1444
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Foreign investment into the chemical industry should continue to rise,bene ting domestic production.

After surviving high oil prices that reached US$70.85 a barrel inAugust 2005, the widespread impact of the Asian tsunami and socialand economic problems, the Asia Paci c chemical sector showedgreat progress. It emerged stronger at the end of 2005, thanks to theupswing of global and regional economies. During the year, the sectorpro ted from robust economic growth and surging manufacturing

activity in the region, which resulted in rising prices and soaringdemand for chemicals. The second half of the year saw somesigni cant changes in the sector, including company consolidations,very low downstream inventories and delays in new investments.

Throughout 2005, China led growth in the Asia Paci c chemicalsector. As the global economy gradually recovered, economic growth inChina accelerated at an even greater rate. Although its economy isexpected to slow fractionally in 2006, its annualized rate of growth inthe rstnine monthsof 2005 was9.4%,accordingto theNationalBureauof Statistics, strengthening the demand for chemicals. According to theNational Development and Reform Commission, China's industrialoutput increased by 16.3% year-on-year to RMB5.69 trillion (US$704.4 billion) in the rst ten months of the year. China is anincreasingly important consumer and supplier of chemical products.This is largely due to strong demand within the country and costadvantages over Western industrial countries, both in production andsetting up plants. According to Deutsche Bank (FSE: DBK), chemicalturnover in China is forecast to grow by 10% annually to nearly400 billion (US$473.2 billion) by 2015, compared with 3.5% in the USand 3% in Germany. With this growth, China's chemical industry willincreaseits share of the world market from 8%to 13%, making China thesecond biggest producer after the US by 2015 ( Table 1 ).

Although China is now the world's fourth-largest chemicalproducer, domestic output is unable to cover demand in all segments.According to Deutsche Bank in 2004, China imported chemicals worth44 billion (US$52 billion), which is almost equivalent to Belgium'stotalchemical turnover.Since 2000, China's imports of chemicals havegrown by more than 70%, while exports have risen by more than 50%.While the chemical industry in China is under increasing pressure toexpand production capacity to maintain its share of the domesticmarket, the rapid upsurge in Chinese demand has bolstered therecovery of the chemical sectors of other countries in the region. Theincrease in exports to China has contributed to improvements inpro tability of the chemical sector in several countries, particularlythose of Japan, South Korea and Taiwan.

These three countries were the major suppliers of a large portionof the chemicals that China needed to fuel its industrial growth.According to Japan's Ministry of Finance, Japanese exports to Chinarose for the sixth straight year in 2004, surging by 29%year-on-year toUS$73.83 billion.

The Middle East in particular Iran and Saudi Arabia has plans tobuild even bigger petrochemical facilities in the next few years. Forexample, in August 2005, Japan's third largest chemical producer

behind Mitsubishi Chemical and Asahi Kasei, Sumitomo Chemicalagreed to a joint venture with Saudi Aramco in the development of alarge, integrated re ning and petrochemical complex in the Red Seatown of Rabigh, Saudi Arabia. The US$4.3 billion project represents anopportunity for the world's largest producer of hydrocarbons tocollaborate with a leading global petrochemical producer. Whencompleted in late 2008, the Rabigh project will produce a total of 2.4 million tons of petrochemical solids and liquids a year ( Table 1 ).

According to research by Parpinelli Tecnon, an internationalindependent consulting organization based in Italy, and Royal DutchShell Plc, trading of Polyethylene (PE) worldwide will undergo a hugechange from 1996 to 2010. In 1996, the US exported almost twomillion tonnes of PE, but by 2010 it will be a net importer of onemillion tonnes a year. Europe is showing a similar pattern movingfrom being a net exporter to an importer over the period. The growthof PE exports from the Middle East will be signi cant, increasing fromone million tonnes to almost ten million tonnes a year, with Asia

Paci c being the main destination. Despite imports from the MiddleEast, Asia Paci c countries will shift from being a net importer to asigni cant exporter, selling ten million tonnesa year, mainly to China.Asia Paci c will account for a major share of global demand growthand the Middle East will be the center for new capacity investmentaimed primarily at the Asian market. According to Parpinelli Tecnon,15% of global ethylene production will be from the Middle East in2015 ( Table 2 ).

A breakdown of the global ethylene capacity in 2007 and projectcapacity by 2012 is given below. We can see that Asia Paci c region islikely to remain stable at approximately 32% with Western Europeand NAFTA declining by a substantial amount. South America fuelledmainly by Brazil and Venezuela will experience a capacity increasefrom 4.3% to 7.1%. The most signi cant increase is likely to come fromthe Middle East with 22.6% of global ethylene capacity with SaudiArabia and Qatar in particular ramping up capacities with a number of world scale projects. According to BMI research by 2012, the MiddleEast and Africa will have nearly twice the capacity of Western Europe.Due to this strong growth in capacity in the Gulf region, investors areless reluctant to expand capacity in North America and WesternEurope.

4. Literature review

To our knowledge, no study has looked at the relationshipbetween crude oil prices and ole ns products. However, severalstudies model the dynamics between crude oil prices and re neryproducts. Using VAR techniques with a single equation framework,Serletis (1994); Grima and Paulson (1999); Gjlberg and Johnsen(1999) nd long run relationships between crude oil prices andre nery products but they assume weak exogeneity of the explana-tory variables. The problem with such methodology is that we are

Table 1Top 10 ethylene complexes in Jan 2006.

Company Location Capacity, tpy

1 Nova Chemicals Corp Joffre, Alta, Canada 2,812,0002 Arabian Petrochemical Co Jubai l, Saudi Arabia 2 ,250,0003 Exxon Mobil Chemical Co Baytown, Texas 2,197,0004 Chevron Ph illips Chemical Co Sweeny, Texas 1,868,0005 Dow Chemical Co Terneuzen, Netherlands 1,800,0006 Ineos Ole ns & Polymers Chocolate Bayou, Texas 1,752,0007 Equi star Chemicals LP Channelview, Texas 1,750,0008 Yanbu Petrochemical Co Yanbu, Saudi Arabia 1 ,705,0009 Down Chemical Co Freeport, Texas 1,640,00010 Shell Chemicals Ltd. Norco, Los Angeles 1,556,000

Source: Oil and Gas Journal, March 27, 2006; 104, 12; Academic Research Library.

Table 2

Summary statistics.Ethylene Ethylene Ethylene Crude oilNWE MED FE WTI

(CIF) U$/MT (CIF) U$/MT (CFR) U$/MT U$/BBL

Mean 693.5 694.2 641.8 36.5Standard error 29.0 29.0 28.2 1.5Median 625.0 625.0 550 31.5Mode 400.0 402.5 475 31.8Standard deviation 252.8 252.8 245.6 13.3Kurtosis 0.435 0.467 0.7 0.04Skewness 0.6 0.5 0.7 1.1Range 975.0 972.5 900.0 49.7Minimum 350.0 352.5 297.5 19.8Maximum 1325.0 1325.0 1197.5 69.5Observations 76 76 76 76

Notes: CIF= Cost, Insurance and Freight, CFR= Cost and Freight.

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assuming that the crude oil price (naturally being the explanatoryvariable) is the driver or the leader in determining other prices. Thisassumption says that crude oil prices are not driven or are notin uenced by products prices. Generally, when investigating therelationship between prices, the economic theory does not provideany guidance with respect to which variable should be chosen asexogenous ( Goodwin et al. 1990a,b ). It is therefore just as wellpossible that demand for petrochemicals (which in turn drives up the

price of ethylene) is feeding back up the chain putting some pressureon crude oil pricesor that the relationship goes in both directions. Thebene t of treating crude oil prices as endogenous gives us anopportunity to conduct out-of-sample tests on how shocks in othermarkets are felt through the oil sector.

In more recent times Adrangi et al. (2001) analyze the pricedynamics of a speci c crude (Alaska North Slope)and its relation withUS West Coast diesel fuel price using a VAR methodology and abivariate GARCH model to show the casual relationship between thetwo prices. They test the causal relationship owing from productprice to crude oil price based on the theory of derived demand, whichstates that the price of an input (crude oil) should be determined byits contribution to the market value of the output re ected in itsmarket price. Asche et al. (2003) also test the long run relationshipbetween crude oil prices and re ned products in a multivariateframework in the North Western Europe market. They nd threecointegrating relationship between crude oil prices, gas oil, kerosene,naphtha and heavy fuel oil. They then do pair wise cointegration teststhat long run relationship does not exist between heavy fuel oil andcrude oil. Their results do indicate a long runrelationship between gasoil, kerosene and naphtha, implying that demand changes for one of these products will in uence the optimal re nery mix.

The literature on international market integration for hydrocarbonproducts is relatively scarce. Siliverstovs et al. (2005) for example,looks at the market integration for natural gas in particular Lique edNatural Gas (LNG) markets in Europe, North America and Japan. Theyuse principal component analysis and the Johansen test to show co-movement within the European/Japanese and the North Americanprices. Their results, however, show the absence of cointegrationbetween the two groups, which shows that gas markets were notcointegrated across continents thus showing the divide betweenNorth American, andEuropeangas markets in the 1990s.Our researchfound a lack of studies focusing on the price dynamics of petrochem-icals and crude oil with a focus on the regional markets. In this studywe intend to ll in this gap in the literature.

5. Methodology

It is now well known that the traditional regression analysis thathas been applied for many decades in time-series studies eitherestimated a spurious relationship (if the original level form of thevariables was non-stationary) or estimated a short-run rather than along-run theoretical relationship (if the original level form of the

variables were differenced to make them stationary).This damaging limitation of the traditional regression analysis (i.e.,

either spurious or not testing theory) has been addressed by the recentand ongoing cointegration time series techniques. The signi cantcontributions made by the time series cointegration techniques startingwith the publication of the seminal paper by Engle and Granger (1987)has been recognized through the recent award of the Nobel Prize inEconomic Science to Engle and Granger in 2003 (for further details seeDiebold (2003, 2004) and Granger (2003) ).

The recent time-series studies based on cointegration haveappliedeither vector error-correction and/or variance decomposition meth-ods for testing the long run relationship among variables and testingfor Granger causality or commonly known as a lead-lag relationship.Although the standard time series cointegrating techniques are

improvements on the cross-sectional studies and the traditional

regression approach in testing relationship over time, one of themajor limitations of error-correction/variance decompositions meth-ods is that they are based on the estimates of the standardcointegrating vectors which are mostly atheoretical in nature. Therecently developed long run structural modelling (LRSM) technique(Pesaran and Shin, Econometric Reviews, 2002 ) takes care of thatmajor limitation of the standard cointegrating estimates.

In the LRSMprocedure, before applying the vector error-correction

procedure and/or variance decompositions techniques for testingGranger-causality, the estimates given by the standard cointegratingvector(s) is(are) tested against theoretical and apriori information of the economies. The LRSM procedure, therefore, endeavours toestimate theoretically meaningful long-run (or cointegrating) rela-tions by imposing on those long-run relations and then testing bothidentifying and over-identifying restrictions based on theories andapriori information of the economies. Pesaran and Shin (2002)suggest to initially impose a few (or even single) restriction(s) beforeadding additional restrictions and with each restriction one shouldcheck the likelihood ratio statistic in deciding whether the null of restriction(s) should be rejected or accepted . The resulting cointe-grating vectors would then pass through the rigorous scrutiny of economic theories and apriori information of the economy rather thanemploying a pure mechanical approach.

Prior to applying theLRSM technique, we would follow thestandardsteps in testing the long run relationship among variables and test theGranger-causality or lead-lag relationship among variables.

After examining the unit root tests and the order of the VAR, the Johansen cointegration tests will be applied. The test of cointegrationis designed to examine the long run theoretical or equilibriumrelationship and to rule out spurious relationship amongthe variables .The cointegrating estimated vectors will then be subjected to exactlyidentifying and overidentifying restrictions based on theoretical andapriori information of the economy.

The evidence of cointegration, however, cannot tell us thedirection of Granger-causality among the variables, i.e., whichvariables are leading and which variables are lagging. That can bedone by the test of vector error-correction model (VECM) that canindicate the direction of Granger-causality in both the short and longrun. However, the VECM only indicates whether the variables areweakly exogenous or endogenous and cannot tell us which variable isrelatively more exogenous or endogenous. The variance decomposi-tion (VDC) technique is designed to indicate the relative exogeneity/endogeneity of a variable by decomposing (or partitioning) thevariance of the forecast error of a variable into proportionsattributable to shocks (or innovations) in each variable in the systemincludingits own. Theproportion of the variance explainedby its ownpast shocks can determine the relative exogeneity/endogeneity of avariable. The variable that is explained mostly by its own shocks (andnot by others) is deemed to be the most exogenous of all. If required,the variance decompositions can also be equivalently represented bythe impulse response functions (IRF). They are designed to map out

the dynamic response path of a variable due to a one-period standarddeviation shock to another variable. The IRF is a graphical way of exposing the relative exogeneity or endogeneity of a variable. Finally,one can observe the persistence pro les which are designed toestimate the speed with which the variables get back to equilibriumwhen there is a system-wide shock to the long run equilibrium, i.e.,the cointegrating vector. This step gives us further insight in additionto the IRF, which traces out the effects of a variable-speci c shock onthe long run relationship.

6. Data, empirical results and discussions

In this study, we use monthly data from January 2000 to April 2006sourcedfrom Thomson Reuters Datastream. Theseries used in thestudy

are ethylene prices for NWE, MED and FE. The NWE prices areCIF (cost,



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insurance and freight) and for shipment to any ports in Amsterdam,Rotterdam, Antwerp, and the MED CIF prices are for shipments to anyports ontheWestMediterranean inparticularthe west coast of ItalyandSouthernFrance.The FEpricesare CFR(costand freight only) shipmentsto Japan, Taiwan, Korea and Hong Kong. Our crude oil price is the WestTexas Intermediate spot price at Cushing Oklahoma. We use the WTIprice due to the high API gravity and low sulphur level making it moreappropriate in manufacturing of petrochemicals. WTI is also generallyaccepted as a global barometer for crude oil.

In Fig. 1, we can see the large price differential between ethyleneand crude oil prices in late 2004 and early 2005. Our investigationrevealed that this was due to a surge in ethylene demand and re neryproblems. In the latter half of 2005 there was a sharp decline inethylene prices as inventories were boosted due to re neries rampingup production back to around 2004 levels. In Fig. 2 we also see asigni cant increase in the manufacturing of ethylene production inChina and South Korea. This increase has put pressure on the crude oilprice (via demand for naphtha and fuel oil) as we noted earlier thatmost ethylene in NWE and FE is manufactured by naphtha crackerswith supplies coming from the Middle East and Africa. The pressureon crude oil from petrochemicals is also contributed by the limitedre nery capacity of ethylene, which results in the continuous need tohave large amounts of crude oil inventories. This trend reinforces theneed to investigate whether the regional ethylene prices arecointegrated (share a common stochastic trend) with the crude oilprice and which price(s) tend to lead and lag.

Before testing for cointegration between the price of crude oil andthe three regional ethylene prices, we paired each ethylene price withthe crude oil to test the dynamics in a bivariate setting. The resultsshowed that crude oil price was cointegrated only with the MEDethylene price and not with the other two markets. In the subsequentVECM, both prices (MED and WTI) were found to be weaklyexogenous, therefore indicating that neither price was the driver.This result wassupported by the variance decompositions where bothprices remained strongly exogenous in the short and long horizons.This result reiterated the fact that the stochastic dynamics orinterdependency between ethylene prices and crude oil had to becaptured in a global context in order to address the original twoquestions that we hypothesised in this paper. 4

Beforetesting forcointegrationamongst thefour variableswe testedtheunit root properties of the data and found that they were I(1) on thebasis of the ADF and PP test. In the next step we estimated anunrestricted VAR (5) model and found theoptimal order was a VAR (3)based on theAIC andSBC criteria reported in Table3 . We then estimatedtheVAR (3)modelin thewell accepted standard Johansen cointegrationframework and results reported in Table 4 indicate one cointegratingvector at the 90% signi cance level based on the trace statistic.

Since there are four I(1) variables, theoretically there could bethree cointegrating vectors. Our nding of one cointegrating vector,

therefore, needs some discussion. Cointegration analyses of timeseries are often not based on fully speci ed theoretical models. Oftentheory suggests a guide to the number of relations or implied vectorswe may expect in a system of alternative time series variables. In ourpaper we employ a theoretical approach to scrutinize commonprocedures in applied cointegration analysis. We nd the presence of a singlecointegrating relationrather than the three implied by theory.This result, while not consistent with our prior expectations does notnecessarily imply a rejection of theory. This result could arise due to anumber of possibilities: (i) the single relation dominates all otherrelations; (ii) zero restrictions implying the non-presence of the twoother relations cannot be rejected, however the variables that enter

Fig. 1. Ethylene and crude oil prices.

Fig. 2. Ethylene production in South Korea, China and Japan from 1990 to 2006. Notes:quantities are in million metric tonnes. Data is obtained from Thomson Reuters

Datastream.

4 In the interest of saving space the estimated bivariate results are not reported

here.

Table 3Selection of the order of the VAR.

Order LL AIC SBC LR test ( p-value) LR test ( p-value)

5 125.687 116.687 106.199 None None4 124.689 116.689 107.366 1.996 (0.158) 1.760 (0.185)3 124.660 117.660 109.503 2.053 (0.358) 1.810 (0.405)2 121.570 115.570 108.578 8.234 (0.041) 7.259 (0.064)1 113.975 108.975 103.148 23.424 (0.000) 20.650 (0.000)0 108.402 104.402 99.741 34.569 (0.000) 30.476 (0.000)

Notes: AIC=Akaike Information Criterion; SBC=Schwarz Bayesian Criterion.

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these relationships must be included since we do have evidence of along-run binding relationship over all four variables; (iii) the presenceof other (omitted) variables that are not important for the nding of cointegration across all four variables are required in the regionalcrude oil relations to form signi cant evidence of cointegration.

An evidence of cointegration implies that the relationship amongthe variables is not spurious, i.e., there is a theoretical relationshipamong the crude oil and regional ethylene prices and that they are inequilibrium in the long run (although their prices could deviate fromeach other in the short run). The evidence of a cointegratingrelationship implies that there is a common force (such as, arbitrageactivity) which brings the crude oil and regional ethylene pricestogether in the long term. A test of cointegration, therefore, can alsobe considered as a test of the extent of the level of arbitrage activity inthe long term. Cointegration implies that these input and productmarkets are interdependent and highly integrated (as if they are

constituents of one integrated market). Non-cointegration, in theory,implies that the arbitrage activity to bring the markets together in thelong term is zero ( Schwarz and Szakmary, 1994 ). Cointegration alsoimplies that each variable contains information for the prediction of other variables. Moreover, the evidence of cointegration has implica-tions for portfolio diversi cation by the investors. In a cointegratedmarket the possibility of gaining abnormal pro ts in the long termthrough diversifying investment portfolio is very limited indeed.Finally, cointegration has implications for the coordination of policiesby multinational rms.

However, in order to make the coef cients of the cointegratingvector consistent with the theoretical and apriori information of theeconomy, we applied theLRSM procedure. Foronecointegrating vector,we imposed an exact identifying restriction of unity by normalizing on

the coef

cient of NWE price ( Table 5 , Vector 1) and found that all thecoef cients of the cointegrating vector were signi cant. We then

imposed an over-identifying restriction of zero on the coef cient of WTI price to test whether WTI prices enter the cointegrating vectorsigni cantly, but the zero restriction was rejected by the chi-squarestatistic with a p valueof 0.003 ( Table 5 , Vector 2). Thisimplies that theWTIpricecannotbe droppedfrom thecointegrating vectorandthat it issigni cantly related to the regional ethylene prices. Cointegration,however, cannot tell us the direction of Granger-causality as to whichvariable is leading and which variable is lagging (i.e., which variable is

weakly exogenous and which variable is weakly endogenous). Fordiscerning the weak endogeneity/exogeneity of the variables weapplied the vector error-correction modelling technique ( Table 6 ).

Looking at the signi cance or otherwise of the error-correctioncoef cients, we nd that FE price appears to be weakly exogenous andall other pricesare weaklyendogenous since theerror-correction termsof these are signi cant. It implies that the deviation of these prices(represented by the error-correction term) has a signi cant feedbackeffecton theMED, NWEethyleneand WTIprices thatbear theburdenof short-run adjustment to bring about the long-term equilibrium.

The above nding that three prices including WTI are endogenousbut only the FE ethylene price is exogenous needs further analysis.Wehave to admit that our model is partial in that the model does notincorporate the inter relationshipsbetween all relevant variables suchas, the crude oil and all related product prices. Intuitively, it is a bitdif cult to believe that a big variable such as, WTI price would bedriven by one regional product price such as, the FE ethylene price.We thought that it was worth investigating the exogeneity/endo-geneity of WTIin relation to some other product prices. We, therefore,tested the behaviour of heating oil, gasoline, WTI price in the UnitedStates (New York Harbour), and gas oil, gasoline and WTI for Europeusing the ARA (Amsterdam, Rotterdam and Antwerp) prices. Weconducted the cointegration analysis using the exact time frameapplied in our data and the results are summarized below. 5

For the United States market we model the price dynamicsbetween heating oil, gasoline and WTI in a cointegrating frameworkusing the same time period as that used in our paper. Following thesame steps mentioned earlier, we nd that a VAR 4 model is selectedand we nd one cointegrating vector. Using the LRSM technique we nd that the over identifying restriction of zero on WIT is rejectedand we proceed with the initial normalizing restriction of 1 on theheating oil series. The ECM model shows that NYHEAT is weaklyendogenous with a speed of adjustment occurringfrom the bottomup( 0.73321). However, Gasoline and WTI are found to be exogenous.This is in contrast to our paper where WTI is found to be weaklyendogenous when modelled with global ethylene prices.

The out of sample variance decomposition analysis showsvariances of all three variables are in uencing each other wheneach variable is shocked. When we shock NYHEAT after 1 month41.61% of the variance is self explained, with 26.49% being explainedby NYGASO and 35.20% being explained by WTI. As we increase thenumber of horizons we nd that crude oil plays a bigger role inexplaining the variance of NY heating oil. For example, after

10 months (horizons) 31.10% of NYHEAT is self explained, with28.90% and 44.30% being explained by Gasoline and WTI.

For the NWE (Amsterdam, Rotterdam and Antwerp) market wemodel the price dynamics between gas oil, gasoline and WTI based ona VAR 3 model. Again the overidentifying restriction of zero on thecrude oil price is rejected. The ECM model shows that gasoline isweakly endogenous with gas oil and WTI showing weak exogenousproperties. This is again in contrast to our weak endogeneity in WTIwhen modelled with global ethylene prices.

From the above ndings we tend to believe that no single productappears to be a constant leader/lager and also there is no single leaderor driver of crude oil prices during the sample period under review.

Table 5Maximum likelihood estimates of exact and over-identifying restrictions.

Vector 1 Vector 2

FE 0.141 (0.075) 0.142 (0.084)NWE 1.000 (None)) 1.000 (None)MED 1.190 (0.082) 1.137* (0.076)WTI 0.104 (0.046) 0.000 (None)Trend 0.001 (0.000) 0.000 (0.000)Log likelihood 678.220 673.885Chi-square None 8.680 (.003)

Notes: and indicate signi cant at 5% and 1% level respectively, standard

errors in brackets.

5 For saving space these results which contain twenty two pages are not reported

but available on demand.

Table 4 Johansen ML results for multiple cointegrating vectors.

H 0 H 1 Statistic 95% critical 90% critical

Maximum eigen value statisticsr =0 r =1 47.179 31.79 29.13r 1 r =2 25.472 25.42 23.10r 2 r =3 8.943 19.22 17.18

Trace statistic

r =0 r 1 85.077 63.00 59.16r 1 r 2 37.898 42.34 39.34r 2 r 3 12.427 25.77 23.08

Rank Maximized LL AIC SBC HQC

r =0 654.635 618.635 576.682 601.869r =1 678.225 634.225 582.949 613.733r =2 690.961 640.961 582.692 617.674r =3 695.432 641.432 578.502 616.283r =4 697.174 641.174 575.913 615.093

Notes: AIC=Akaike Information Criterion; SBC=Schwarz Bayesian Criterion. indicates signi cant at 5% level.

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However, it is worth noting here that in all cases, evidence from thepost-sample variance forecast period (given by variance decomposi-tion) tends to indicate that oil is relatively exogenous when modelledwith the closer gasoline, gas oil and heating oil market. In our paper,

the variance decompositions indicate that the crude oil price isrelatively exogenous which implies that downstream markets likepetrochemicals (ethylene) feel the effects of crude oil price move-ments and it would be interesting to see how crude would behavewhen modelled with further downstream plastics and polymersmarkets.

Our ECM results tend to indicate that the FE ethylene price isweakly exogenous. These results are consistent, during most of theperiod under review (2000.1 2006.4), with the surge in demand forethylene throughout theFE, particularlyin China and South Korea and

the other two ethylene markets (NWE and MED) appear to haveborne the brunt of adjustment endogenously.

The ethylene industry is highly cyclical. Strong domestic orregional demand will require larger volumes of either domestically-

produced or imported ethylene and trends in end-user industries likestrong demand for motor vehicles, construction materials, packagingproducts and pharmaceuticals imply rising demand for basicchemicals like ethylene.

The sizeof the coef cient of the error-correction termindicatesthespeed of adjustment of the weakly endogenous dependent variablesand the sign of that coef cient indicates whether the dependentvariable adjusts upward (positive) or downward (negative) to bringabout equilibrium. The error-correction model also helps us distin-guish between the short term and long term Granger-causality. Theerror-correction term stands for the long-term relations among thevariables. The impact of each variable in the short term is given by theF test of the joint signi cance or insigni cance of the lags of each of the differenced variables. The results tend to indicate that in the

short run the FE variable has an impact on the WTI variable, whereasthe NWE and MED variables affect each other. In the WTI equation,convergence to equilibrium takes place rapidly compared to

Fig. 3. CUSUM(top) and CUSUMSQ (bottom). Notes:the straight lines represent critical

bounds at 5% signi cance level.

Table 7Generalised variance decompositions.

Percentage of forecast variance explained by innovations in

Months FE NWE MED WTI

FE 1 56.102 13.238 15.084 15.5763 58.265 11.908 15.192 14.6355 58.154 11.869 15.556 14.42110 59.875 11.008 14.904 14.213

NWE 1 16.792 38.605 36.809 7.7943 24.922 31.343 30.508 12.3615 26.059 30.921 29.752 13.26810 26.770 30.834 29.221 13.175

MED1 18.640 35.854 36.693 8.8133 26.397 29.143 30.044 14.4165 27.921 28.480 28.973 14.62610 28.839 28.300 28.287 14.574

WTI 1 19.128 4.187 7.059 69.6263 18.078 5.235 10.114 66.5735 16.441 7.809 14.299 61.45210 16.880 8.363 15.277 59.480

Table 6Vector error correction estimates.

Dependant variables

DFE DNWE DMED DWTI

DFE(1) 0.252 (0.127) 0.119 (0.101) 0.124 (0.094) 0.256 (0.088)DNWE(1) 0.653 (0.726) 1.110 (0578) 0.900 (0.541) 0.527 (0.503)DMED(1) 0.542 (0.763) 1.409 (0.609) 1.275 (0.569) 0.640 (0.529)DWTI(1) 0.289 (0.180) 0.201 (0.143) 0.172 (0.134) 0.168 (0.125)DFE(2) 0.373 (0.130) 0.047 (0.103) 0.120 (0.097) 0.093 (0.090)DNWE(2) 1.228 (0.669) 1.438 (0.533) 1.533 (0.498) 0.618 (0.464)DMED(2) 0.963 (0.691) 1.508 (0.551) 1.622 (0.515) 0.837 (0.479)DWTI(2) 0.259 (0.179) 0.134 (0.143) 0.101 (0.134) 0.281 (0.125)ECM( 1) 0.638 (0.576) 1.561 (0.460) 1.828 (0.430) 1.366 (0.400)Chi-sq SC(1) 1.142(0.285) 1.115(0.291) 0.029(0.864) 0.441(0.507)Chi-sq FF(1) 0.009(0.924) 7.505(0.006) 6.859(0.009) 0.449(0.503)Chi-sq N (2) 0.021(0.989) 1.948(0.378) 1.574(0.455) 2.459(0.292)Chi-sq Het(1) 0.292(0.588) 3.777(.052) 1.086(0.297) 2.024(0.155)

Notes: SC= Serial Correlation; FF =Functional Form; N =Normality; Het=Heteroscedasticity.* Signi cant at 5% level.



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equations containing NWE and MED prices. This smaller coef cient( 1.366) may imply that other ethylene markets adjust to US crudeavailability and prices rapidly, thus the burden of convergence tobring about equilibrium is on the nal products negating the theory of derived demand. As to the negative coef cient, given the upwardpressure on crude oil prices during the period of the study it is notsurprising that WTI prices adjust from the bottom to bring about along term equilibrium. The increasing pace of the FE ethylene markettogether with downstream heavy demand for plastic and polymerscould also play a greater role in causing the faster adjustment in theWTI price. The MED and NWE prices take a longer time to adjust tobring about long term equilibrium. The large positive (1.561, 1.828)coef cients could suggest that these markets are more susceptible toinput costs and are affected severely from factors such as re neryshutdowns and routine maintenance which causes prices to remainhigh. The increase in downstream demand for plastic and polymers inthe FE could also cause higher instability in the NWE and MEDethylene markets.

The diagnostics of all the equations of the error-correction model(testing for the presence of autocorrelation, functional form, normalityand heteroscedasticity) tend to indicate that the equations are wellspeci ed.

We also checked the stability of thecoef cients of theFE variable bythe cumulative sum of recursive residuals (CUSUM) and cumulativesum of squares of recursive residuals (CUSUMSQ) tests in Fig. 3 whichindicate that they are stable. We check the stability of the FE variablebecause unlike the NWE and the MED ethylene markets, the FE marketcould be more unstable being driven relatively more by a surge indemandcoming from China andKorea.The Asia Paci c includingthe FEis taking an increasing share of global chemical production, as its rapidgrowth continues to exceed that of much of the rest of the world. Withgreater focuson growing agriculture,manufacturing and durable goods,

the region's demand is more chemical-intensive than that of thedeveloped economiesof Europe andperhaps the Mediterranean, wherechemical-intensive manufacturing might be on the decline, with theservice sector playing a larger role. Growth in Asia Paci c's chemicalsector was also supported by the dynamism in emerging countries of industries such as electronics, textiles, construction, leather and plasticsprocessing. The developing countries in Asia Paci c are striving tobecome global leaders in these sectors, which are important end-usersof chemicals. China is a bigplayer in increasingthe demandfor ethylenein the contemporary world.

Although the error-correction model tends to indicate the weakendogeneity/exogeneity of a variable,we had to apply the generalisedvariance decomposition technique ( Table 7 ) to discern the relativedegree of endogeneity or exogeneity of the variables. The generalisedvariance decompositions analysis is not sensitive to the order of variables unlike the orthogonalized variance decompositions analysis.The relative exogeneity or endogeneity of a variable can bedetermined by the proportion of the variance explained by its ownpast. The variable that is explained mostly by its own shocks (and notby others) is deemed to be the most exogenous of all. In Table 7 , attheend of the forecast horizon number ten, the contributions of ownshocks towards explaining the forecast error variance of each variableare as follows: the FE variable (60%), NWE variable (31%), MEDvariable (28%) and WTI variable (60%).

The variable that is explained mostly by its own shocks anddepends relatively lesson othervariables is the leading variable. Theseresults tend to indicate that, unlike the error-correction model, boththe FE variable and the WTI are the most exogenous of all. These out-of-sample variance forecast results given by the generalized variancedecompositions further strengthen our earlier within-sample resultsgiven by the error-correction model that the FE ethylene price plays adominant role being driven by demand mainly from China and South

Korea. However, unlike the within-sample (2000.1 2006.4) results,the WTI variable also appears to have become dominant in the post-sample variance forecast period. The emergence of WTI as a leadingplayeras well, is consistent with the recentsurge in WTI price (fuelledmainly, among others, by the strong hedging activities in the WTIfutures/options and re ning tightness) re ecting the growing impor-tance of input cost in determining the dynamic interactions of inputand product prices in the post-sample period.

We then applied the generalized impulse response functions(Figs. 4 7) and found that, consistent with the earlier results, the FEvariable is the least sensitive to a one standard deviation shock toother variables. Finally, an application of the persistence pro leanalysis ( Fig. 8) indicates that if the whole cointegrating relationshipis shocked, it will take about eight months for the equilibrium to be

restored.Fig. 5. Generalized impulse response when we shock NWE ethylene price.

Fig. 6. Generalized impulse response when we shock MED ethylene price.Fig. 4. Generalized impulse response when we shock FE ethylene price.



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7. Conclusion

This study made an initial attempt to investigatethe followingtwoquestions: (i) is the crude oil (WTI) price signi cantly related to theregional ethylene markets of the FE, NWE and the MED? (ii) whatdrives the regional ethylene prices ? The paper is motivated by therecent and growing debate on the lead-lag relationship betweencrude oil and ethylene prices.

We investigated the above questions by applying the error-correction and variance decomposition techniques based on theapproach of long-run structural modelling by Pesaran and Shin,Econometric Reviews (2002) which by imposing exactly identifyingand overidentifying restrictions on the cointegrating vector has takencare of a major limitation of the conventional cointegrating estimatesin that they were in many cases atheoretical in nature. To the best of our knowledge this recent method has not yet been applied to issuesin Energy Economics. The stability of the functions has also beentested by CUSUM and CUSUMSQ.

Our ndings based on the above rigorous techniques and subjectto the limitations of the study, tend to suggest: (i) crude oil (WTI)price is cointegrated with the regionalethylene prices(ii) Our within-sample error-correction model results tend to indicate that althoughthe ethylene prices in NWE and the MED are weakly endogenous, theFE ethylene price was weakly exogenous both in the short and longterm. These results are consistent, during most of the period underreview (2000.1 2006.4) with the surge in demand for ethylenethroughout the FE, particularly in China and South Korea. However,during the post-sample forecast period as evidenced in our variancedecompositions analysis, the emergence of WTI as a leading player aswell, is consistent with the recent surge in WTI price (fuelled mainly,among others, by the strong hedging activities in the WTI futures/options and re ning tightness) re ecting the growing importance of input cost in determining the dynamic interactions of input andproduct prices.

During most of theperiodunder review(2000.1 2006.4), therate of increase inethylenepricesfar exceededtherate of increasein WTIprice.Asia's ethylene prices touched 16-year high in 2006 (Platts, Insightmagazine, December 2006). Our nding that unlike the NWE and theMED markets, the FE ethylene market is exogenous is consistent withthe surge in demand for petrochemicals in countries like Taiwan, SouthKorea and China which led to higher prices of ethylene leading to itshigher production. During the last few years there have been majorcapacity increases in petrochemical plants in China where plasticsconsumption is around 20 22 million tonnes and growing at about 7%per year. Major global petrochemical players like BP, Shell, etc. havemoved into China and started up ethylene plants in collaboration withthe Chinese State-owned petrochemical companies.

The Asia Paci c including the FE is taking an increasing share of

global chemical production, as its rapid growth continues to exceed

that of much of the rest of the world. With greater focus on growingagriculture, manufacturing and durable goods, the region's demand ismore chemical-intensive compared to that of developed economiessuch as theNWE and perhaps theMED where theservice sector mighthave played a relatively larger role and the manufacturing sectormight be on the decline leading to less demand for ethylene. Growthin Asia Paci c's chemical sector was also supported by the dynamismin emerging countries of industries such as electronics, textiles,construction, leather and plastics processing. The developingcountries in Asia Paci c are striving to become global leaders inthese sectors, which are important end-users of chemicals. China is abig player in increasing the demand for ethylene in the contemporaryworld.

Hence during the within-sample period as evidenced in our ECMmodel, the nding of weak exogeneity in the FE ethylene marketappears to be consistent with the surge in demand for ethylenethroughout the FE driven mainly by China and South Korea and theother two ethylene markets (NWE and MED) bearing the brunt of adjustment with weak endogeneity.

However, during the post-sample forecast period as evidenced inour variance decompositions analysis, the emergence of WTI as aleading player as well, is consistent with the recent surge in WTI price(fuelled mainly, among others, by the strong hedging activities in theWTI futures/options and re ning tightness) re ecting the growingimportance of input cost in determining the dynamic interactions of input and product prices. The rising crude oil and naphtha pricesimpacted on the petrochemical (ethylene) sector pro ts, since therapid rise in crude oil and naphtha could not be easily passedon to theconsumer product prices.

Future research looking at the dynamics between the price of thecloser feedstock naphtha and prices of other ole ns and aromatics iswarranted. A closer look at the link between the major input prices of ethylene and propylene and the prices of downstream plastics,polymers and co-polymers needs investigating. As an example, price

dynamics between further downstream products like ethylene andpolyethylene and between aromatics like benzene and polyvinylchloride (PVC) could show interesting results for policy makers inthese markets.

Acknowledgement

The authors are deeply grateful to the editor (Professor RichardTol), two anonymous referees for their very helpful comments and tothe Center of Research Excellence in Renewable Energy, King FahdUniversity of Petroleum & Minerals, Dhahran, Saudi Arabia, for thegenerous support. Financial support from MacquarieUniversity is alsoacknowledged. We would also like to thank Ronald Ripple and Stefan

Trck for their helpful comments and suggestions.

Fig. 8. Persistence pro le when we shock the cointegrating vector.Fig. 7. Generalized impulse response when we shock WTI crude oil price.



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