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Mauro, A. and Peri, A. Price dynamic, volatility and information flow in the oil industry: a multivariate analysis.

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Price dynamic, volatility and information flows in the oil

industry: a multivariate analysis.

Alessandro Mauro

Andrea Peri

Original version: July - 2003

This Draft: November - 2011

Information:

[email protected]

[email protected]


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1. Introduction

The goal of this paper is to analyse the relationship between crude oils and refined

products, in terms of price and volatility dynamics. We do not try to explain the behaviour

of single prices in isolation but instead investigate the information contained in the links

between prices. These relationships between crude and product prices are crucial throughout

oil markets and especially so within the refining industry, where they define the refinery

margin between cost of inputs (crudes) and value of outputs (products).

The oil market is global but regional factors are also relevant, creating local variations in

crude / product relationships. These relationships are often ambiguous, with limited in depth

study to date. Therefore, we think it is important to test and understand if there are feedback

mechanisms from product to crude markets in the short run, investigating, in particular,

weather shocks affecting the former also affect the latter. Consequently, the findings of this

paper present relevant issues for oil market participants and their management of price risk.

We will introduce and utilize statistical tools which allow us to simultaneously model

the behaviour of several prices. The econometric literature of the past three decades

suggests an appropriate statistical framework which is useful to investigate these topics:

Vector Error Correction Model (VECM) and Multivariate Garch Model (MGARCH).

VECM is often used to explain price dynamics and interactions in a multivariate framework.

The necessary attention to the short-run should not lead us to underestimate the possible

relevance of the long run relationship between crude and product prices. In the long-run

there is a substantial parallelism between relative crude and product price changes. A

similar connection can be also established between financial futures and spot prices for both

crude oils and refined products, due to well known non-arbitrage conditions. VECM models

are quite powerful since they allow to consider both long term equilibriums and short run

price dynamics.

The second statistical framework, MGARCH models, can be utilized in order to

study volatility transmission. Our attention will be centered on volatility shock transmission

between crude and product markets. MGARCH models are still evolving and many

theoretical questions remain without a clear answer as of today. Consequently we have


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decided to implement a MGARCH model with a BEKK1 specification, as this has two

important features. Firstly, this specification produces covariance matrices which are semi-

definite positive. Secondly, the applied models seem to be able to explain the relevant

characteristics of the phenomenon we are going to investigate.

The paper is organized as follows. In the next section, the basic features of the oil

industry are introduced, especially outlining the aspects which are useful in order to

understand the successive statistical analysis. The third section contains a broad report of

previous works which are in some way precursors of the present paper. In the fourth section

we introduce in deeper detail the statistical models we have applied. After outlining the data

set in the fifth section, finally we present and discuss the results of the models applied to the

data. The Appendix shows details of the statistical results.

2. The oil market and the refinery industry

The foundations of the oil industry are based on the exploration for crude oil and

consequent extraction from the ground. Crude oil is not a homogenous product, as it differs

according to physical and chemical features such as density (usually measured in API

grades) and content of undesired elements (Sulphur, metals, etc). Crude oil is rarely used as

it is found, but instead undergoes various industrial transformations through the refining

process in order to yield a range of refined products. The mix and quality of the output, and

consequently its commercial value, depends on the crudes used as input, on the available

technology and on the refinery configuration chosen by the refiner. Table A in the Appendix

reports the most important technologies available in the U.S.A. and North West Europe

together with some crude oil average qualities.

The most general taxonomy of refined products divides them in light, middle and

heavy distillates. Essentially, the light distillates comprise Gasoline and Naphtha, middle

distillates comprise Kerosene, Jet Fuel, Diesel and Gas Oil, whilst heavy distillates

comprise mainly Fuel Oils, usually further differentiated according to the Sulphur content

1 See Engle and Kroner (1995)


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(Low Sulphur Fuel Oil and Heavy Sulphur Fuel Oil). Generally speaking, the refined

petroleum products value increases as we move from heavy distillates to light distillates and

from high to low sulphur contents. Considering that crude oils of good quality (high API

grades, low Sulphur content) give an higher percentage of light-middle low sulphur

products compared to lower quality crudes, their market values tend to be greater ceteris

paribus. Regarding final uses, it is worth mentioning that Naphtha is the main input for

petrochemicals industry, Jet Fuel (similar to kerosene) is used by the airline industry, Gas

Oil and Gasoline are used for automotive transportation, Heating Oil for heating and finally

Fuel Oil is used mainly for the generation of electricity and marine transportation.

Oil refineries are usually characterized by economies of scale and so tend to occupy

major sites which include their own substantial storage facilities or are situated close to third

party storage facilities. In addition, since transportation can be a substantial cost in the

refining value chain, refineries are often situated close to key demand centres. In fact, the

World refining capacity is concentrated in North America, North-West and Southern Europe

and the Far East.

With regard to the structure of crude markets, supplies originate from private firms2

and National Oil companies state-owned by producing countries. Some of the most

important among these countries are grouped in a cartel, OPEC, which attempts to manage

crude oil supply in order to keep crude prices at pre-determined “acceptable” levels. The

market for crude oils produced by OPEC Countries is therefore an oligopoly: few suppliers

and several buyers. Crude oil markets outside OPEC comprise several suppliers and buyers

and are generally competitive and efficient. Major non-OPEC benchmark crude markets

include Brent crude oil from the North Sea, WTI and WTS in the United States and Dubai-

Oman in the Middle East. Prices of these crudes react in real time to changes in supply and

demand conditions. On the product side, markets are generally even more competitive, with

many refineries located around the world on the supply side and a wide variety of wholesale

marketers and end-users on the demand side.

As a result of different transportation costs and peculiar demand and supply

specializations, global markets for both crude oil and refined products may be divided into

regional groups. Each region has its own characteristics, which determines particular

2 Oil majors like Exxon Mobil, Shell, British Petroleum, Total and Eni.


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relationships between crude and products markets. The econometric analysis implemented

in this study deals with the relationship between crudes and refined products for three

important industrial regions: the Gulf Coast district in the United States, the North West

Europe coast and the Mediterranean basin. As far as North West Europe is concerned, we

focus on two areas, the first known as ARA (i.e. Amsterdam, Rotterdam and Antwerp) and

the United Kingdom. For the Mediterranean area, our attention is mainly focused on

countries belonging to the European Union.

Various factors have led each area to develop their own particular supply and

demand characteristics for refined products. For the Mediterranean region it has to be

pointed out the important role played by Fuel Oil, as it had been widely used in the Italian

electricity industry, and the insufficient refinery capacity for low sulphur products. Local

refineries are struggling to satisfy increasing gas oil demand for automotive diesel, so this

demand must be met by foreign imports. For North West Europe a critical role is also

played by Gas Oil, whose local supply is again inadequate to face the increasing demand,

while there is a structural surplus of high specification gasoline for export to the US and

other parts of the world. In the Gulf Coast district, middle distillates play a fundamental role

as main driver of the production value. This area exports the majority of its production to

industrial districts of the US East Coast, Europe and the Far East.

It is important to mention the increasing “financialization” of energy markets, as

paper traded volume is often far greater than that for physical exchange of goods.

Consequently we have also studied, utilizing the same instruments and models, a

relationship which is outside the physical crude-product interaction. In fact, while Europe is

a great exporter of gasoline toward the United States, there is no European gasoline future

contract listed for trade. As a result, the price of the gasoline future listed on the New York

Mercantile Exchange (NYMEX) is often used as the basis for pricing physical trades and for

price risk management in European markets3. In this case, the relationship to be analysed is

therefore between European gasoline spot price and NYMEX gasoline future price.

3 Swap and forward prices in the Over-the-Counter European gasoline markets are usually evaluated taking into

consideration the New York Mercantile Exchange future price.


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3. Previous Studies

There are many research works constituting the foundations of the econometric

theory which have been applied in this paper. In this paragraph we discuss some of these

works.

Duffie, Gray ed Hoang (1999) presents a quite comprehensive review of stochastic

volatility models applied to energy prices. Restricting ourselves to the topics contained in

our study, the most interesting part of the article is the estimation and forecasting

performance evaluation of some well known financial models. The average future squared

volatility is adopted as the volatility benchmark in order to assess the goodness of

prediction. The models taken into consideration in their article are: historical volatility,

implied volatility as defined in the model of Lu and Yu (1993), GARCH(1,1),

EGARCH(1,1), MGARCH(1,1) with VECH specification and a Threshold GARCH(1,1).

The sample is composed of daily observations of energy financial Futures prices. The

Authors demonstrate a good performance of implied volatility in forecasting actual

volatility and a decent performance for GARCH models in predicting commodity volatility,

with the exception of electricity markets, which require a more sophisticated statistical

framework.

Several empirical works tested long run relations, both between spot and Future

prices and among different energy products. Examples include Herbert and Serletis (1999),

Kellard et al. (1999) and Ng and Pirrong (1996). Asche, Gjolberg, Volker (2001) analyses

the relation between crude oil and many products for North West Europe using a sample of

monthly observations covering a period from February 1992 to November 2000. Unlike

previous works, they use the Johansen cointegration framework (see Johansen (1988) and

(1991)), which allows for adequate consideration of exogenity. In fact, previous studies

applied the Engle-Granger cointegration framework instead (see, Engle and Granger

(1987)), in which exogenity is chosen ex ante by the researcher. The authors show that

Brent, Kerosene, Gas Oil and Naphtha prices are cointegrated. This result is found by

estimating both a unique multivariate system and separate bivariate systems. Moreover

Brent crude price is a weakly exogenous variable with respect to these refined products

prices, meaning that crude oil price changes lead to product price changes in the long run.


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However, this does not hold true in the short run, as oil and product prices interact

reciprocally. Heavy Fuel Oil is the exception, as the authors did not find significant

cointegration either with crude oil or any refined product, meaning that there is not a clear

mechanism linking Heavy Fuel Oil to wider oil market price actions.

The latest result could have various causes. Crude oil quality and technology

standards for the North West Europe area under consideration result in a refinery output of

outstanding quality, in which fuel oil becomes a residue. Moreover, environmental

legislation forced the substitution of Heavy Fuel Oil with Low Sulphur alternatives. The

result is a reduced importance for Heavy Fuel Oil markets. However, as our analysis will

show later, there is cointegration between Low Sulphur Fuel Oil and crude oil in North West

Europe.

The increasing integration in financial and commodity markets has increased

volatility transmission mechanisms from one market to another. For example, Engle et al.

(1990) argues that volatility shocks from a single currency market affect all currency

markets and define this phenomenon as a “meteor shower”. MGARCH framework has been

used to model volatility transmission in Kearney and Patton (2000) for currency markets

and in Chou et al. (1999) for U.S.A. and Taiwan stock markets. Volatility is increasingly

interpreted as a proxy of information flow (see for example, Chan et al., 1991).

Ewing, Farooq and Ozfidan (2002) assess whether there is a volatility interaction

between natural gas and oil industrial sectors. The authors do not utilize energy commodity

prices, but instead American Stock Exchange indexes for oil and gas industries, with daily

values covering a period from April 1996 to October 1999. The most interesting point in the

work is the proposed methodology for modelling volatility transmission along different

markets. Covariance matrix of returns in a time-varying contest are specified as a BEKK

MGARCH4. As a consequence of estimation results, the authors discover volatility

transmission between oil and natural gas industries. Furthermore, the direction of the

information flow is not unique: there is transmission from oil to natural gas and also in the

opposite direction.

4 See Engle and Kroner (1995)


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Adrangi, Chatrath, Raffie and Ripple (2001) studies the relation between Alaska

North Slope crude oil and Los Angeles Diesel5. Because of the limited trading interaction

between East and West US coasts, Alaska North Slope crude oil is a key factor for refiners

in the West coast, especially in California. Price logarithms of crude and diesel are

cointegrated and the long run relation is well approximated by their price difference. By

implementing a Bivariate Garch volatility model the authors demonstrate, considering both

price and volatilities dynamics, a relevant information flow from crude oil market to product

market, while the vice versa was not proved true. Moreover, there is evidence of

asymmetric effects in Los Angeles diesel volatility. Conclusions are supported by West

coast market characteristics. Alaska North Slope is one of the most important sources of

crude oil for the West coast, as it is available in suitable quantities and in a short time frame.

The crude supply constraints into the West coast suggest that the available crude supplies

are crucial in explaining market dynamics not only in the long run, but even in the short

term.

It is worth summarizing the principal findings of the discussed studies, adding some

additional considerations about the markets we are analysing. There is no doubt that crude

oil and product prices present similar long run dynamics. In the short run, as a consequence

of several factors, there may be spillover effects from crude oil to refined product markets

and vice versa. Indeed, in certain cases the relevant short-term information flow seems to

move from products to crude oil markets. For example, in the US Gulf Coast the price of

middle distillates is essential, since the region is a major producer and a net exporter to

other regions.

The relationships between crude oil and refined products are the results of the

interaction between supply and demand conditions in their respective markets. In the short

run it is reasonable to suggest that there may be an information flow from refined product

markets to crude oil markets in those areas for which crude oil is in good supply and where

one or more refined products have a critical role. On the other hand, in areas with crude oil

5 Diesel is a Gas Oil quality specification used for motor vehicles.


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supply shortages, either in terms of quantity or quality, it is reasonable to suggest that the

direction of information flow is from crude oil market to the product market.

In conclusion, the only common finding in the cited studies is the importance of

discriminating between short and long term dynamics in energy markets. More detailed

characterization must be tested at a regional market level and general rules holding true

across all regional markets are hard to find, especially in the short run. This paper attempts

to address the problem for the key regions mentioned earlier. Long run relationships will be

analysed in a cointegration framework while short term dynamics will be addressed with the

instruments of Granger Causality and Multivarite GARCH Volatility.

4. Price and volatility models

4.1 Price Models

Following Adrangi et al. (2001) and Ng and Pirrong (1996), we propose a Vector

Error Correction Model (VECM) in order to explain price dynamics. Firstly, let us consider

the essential conditions that must be met to be able to apply VECM: all data series must be

first order integrated and there must be at least one stationary linear combination of them.

An example of VECM for the price logarithm of two assets S and F is represented by the

following system of equations:

tt

n

iiti

n

iitit eFSS ,111

111 lnlnln

(1)

tt

n

iiti

n

iitit eFSF ,212

112 lnlnln

(2)

Cointegration (i.e. long run) relationship is considered in the term et:

ttt FSe lnln 210 . (3)


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The variables ε1,t and ε2,t are random errors whose distribution will be addressed in next

paragraphs. The model may be extended to consider deterministic trends both in the

cointegration relation (3) and/or in equations (1) and (2). Our analysis suggests that the best

specification is the one described in (1) – (3), also neglecting the constant terms ω1 and ω2

in some cases. Coefficients κi give a measure of the speed of adjustment to the long run

equilibrium. We estimated system (1) – (3) for different pairs of crude/product prices in the

three considered regions.

The number of lags, i.e. “n”, is chosen in order to eliminate or reduce to a minimum

the autocorrelation of errors considered as a joint bivariate stochastic process. Residuals

obtained from system estimation are utilized as data input for the estimation of MGARCH

models. From this point of view, it seems more important to remove residual autocorrelation

rather than adopting other criteria in order to select the lag order (for example, the Akaike

information Criterion) as GARCH models cannot remove autocorrelation for asset returns

but for squared returns. However, in our estimations the lag order is never bigger than ten,

so that the two goals of removing autocorrelation and building parsimonious models may be

achieved simultaneously.

We used two main tools in order to investigate interaction among prices: Granger

Causality and Weak Exogenity. The first tool allows testing to see weather past values of

one variable are important in explaining present values of another variable. For example, in

our system (1) – (3), if λi coefficients are jointly significant while γi coefficients are not,

then one can say that past values of F are relevant in explaining S or “F causes S in the

sense of Granger”. Obviously other cases are also possible. For example past values of each

variable could be significant in both equations, so one cannot tell “what causes what” in

terms of Granger Causality. However, if there is Granger Causality then this gives us a

good idea about the direction of the information flow from one market to another.

The second econometric tool we used is Weakly Exogenity. In a bivariate framework

this technique is very simple. In equations (1) – (2), if the long run relationship (3) is

statistically significant for one dependent variable only, then the other one may be defined

as weakly exogenous. For example, if κ1 is significant whilst κ2 is not, then F may be

considered weakly exogenous with respect to S. In a VECM framework we can think of

weak exogenity as referring to the long run relationship among endogenous variables. If a


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variable is considered exogenous in the bivariate case, then it will lead the other variable in

the long run.

4.2 Modelling Volatility

The choice of volatility Models adopted in this paper tries to find a balance in the

trade-off between goals of the analysis and methodological difficulties. In particular, we are

interested in models which are capable of illustrating the short run information flow from

one market to another.

In general, the measure of the impact of news on volatility is represented by the

squared value of the error term (or shock). We see a large error, and hence a corresponding

impact on volatility, when the model used for the “mean” (VECM, in our case) doesn’t

precisely predict the last variation in prices. Researchers usually attribute this variation to

new information which modifies the behaviours of industry operators.

There is a quite simple way to investigate whether news related to one market also

affects the volatility of another market. It involves adding the shock related to the non-target

market as explicative variable in the volatility equation of the target market.

Another consideration is of the asymmetric responses to volatility shocks. In order to

produce parsimonious model we decided to consider asymmetric effects only for the

dependent variable in each volatility equation.

To be consistent with formulas in previous paragraphs we set:

t

t

t,2

,1

The assumption for εt distribution is:

ttt HN ,01

where

0

00

and

tt

tt

t hh

hhH

,22,21

,12,11.

In order to complete the model, it is necessary to specify Ht. The proposed models belong to

MGARCH(1,1) class with BEKK specification (Engle e Kroner, 1995).


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The first specification considered is:

22

11

1

22

11

21

12

11

21

12

22

11

11

22

11

0

0

0

0

0

0

0

0

0

0

0

0'

tttttt HCCH

leading to the following representation:

1,1222111,21,1211222111211,12

1,22222

21,1

212

21,2

222

222

212,22

1,112

112

1,2221

21,1

211

211,11

tttt

tttt

tttt

hcch

hcch

hch

(4)

The volatility equation of asset “1”, h11,t , also includes the shock related to the asset “2”

market, ε2,t-1, as an explicative term and vice versa for the volatility equation of asset “2”.

Coefficients δij measure volatility transmission from one market to the other. When at least

one of these coefficients is significant, we can say that there is in place an information

transmission mechanism between the two markets. As already shown for Granger

Casuality, when only one of these coefficients is significant, we can infer that the direction

of information flow is unique in the short run.

System (4) may be extended in order to catch asymmetric effects in dependent variable

shocks. In matrix form, this may be accomplished by adding the following term:

22

11

11

22

11

0

0

0

0

tt where

elsewhere

if tt

t0

0

leading to the following representation:

1,21,122111,1222111,21,1211222111211,12

21,2

2221,22

222

21,1

212

21,2

222

222

212,22

21,1

2111,11

211

21,2

221

21,1

211

211,11

tttttt

ttttt

ttttt

hcch

hcch

hch

(5)

It needs to be pointed out that the product between the ξi,t-1 elements is included in the

covariance equation. This term is non-zero only when both shocks εi are negative. Therefore


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the η11η22 coefficient measures the extent to which negative variations of both prices have an

impact on covariance with respect to other possible situations.

The second class of models we introduce attempts to model the possible “interaction

effects” among news coming from two different markets. In matrix forms:

22

11

1

22

11

2221

1211

11

2221

1211

0

0

0

0'

tttt HCCH

which leads to the following representation:

1,1222112

1,222211,21,1211222112

1,112111211,12

1,22222

21,2

2221,21,12212

21,1

212

222

212,22

1,112

112

1,22211,21,12111

21,1

211

211,11

2

2

tttttt

tttttt

tttttt

hcch

hcch

hch

(6)

The term ε1ε2 should catch the news interaction effect between markets “1” and “2”. This

term may be interpreted as a local proxy of covariance between shocks; i.e. it aims to

measure the presence of a “common response” to emerging news within different markets.

As we have already done for model (4), we extend model (6) in order to consider

asymmetrical effects. Model (7) has the following representation:

1,21,122111,1222112

1,222211,21,1211222112

1,112111211,12

21,2

2221,22

222

21,2

2221,21,12212

21,1

212

222

212,22

21,1

2111,11

211

21,2

2211,21,12111

21,1

211

211,11

2

2

tttttttt

ttttttt

ttttttt

hcch

hcch

hch

(7)

Is model (4) nested in model (6)? The answer is no because if we delete ε1ε2 terms by setting

some coefficients equal to zero, we would also eliminate other terms.

The proposed models have been estimated for each pair under consideration. Model

selection has been performed with the Akaike and Schwarz criterions.


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Some final comments are required for the practical implementation of the described

models. Maximum likelihood estimation becomes more and more difficult when the number

of parameters increases. We have chosen to adopt a two step estimation procedure. The first

step is the estimation of the “mean part”, which is the VECM model. Residuals from the

VECM model, which should not be affected by autocorrelation but which do display

heteroskedasticity, are the input data for MGARCH models. This two-step estimation

procedure has been adopted in Pagan e Schwert (1990) and Gallant (1992). Lin (1992)

compares joint estimation of mean and volatility and the alternative two-step procedure,

concluding that the results are quite similar. Joint estimation should lead to a more efficient

mean-parameters estimation but it is computationally difficult (Ng e Pirrong 1996).

In this work we adopt a VECM model for the mean with a number of lags up to the

10th. In the bivariate case this means that we may already have more than 40 parameters

without even considering GARCH structure. From this point of view the two-step procedure

seems reasonable, and estimation software for VAR and VECM is suitably efficient.

5. The data set

The dataset consists of time series of oil prices assessments published by Platt's.

Although the original data is daily, missing observations forced the conversion from daily to

weekly data through arithmetic average of available data. The time interval spans from 7th

of October, 1994 to the 28th of June, 2002 and, as usual in the oil market, all prices are

expressed in US Dollars.

For the Mediterranean basin, the crude oils used were Iranian Heavy (FOB6 Sidi

Kerir) and Urals MED (CIF Augusta, Sicily). The products taken into consideration for the

same area were “Low Sulphur Fuel Oil FOB cargoes” (“LSFO”, Sulphur content 1%) and

“Gas Oil FOB cargoes (sulphur content 0.2%)”.

6 FOB: “Free-On-Board”, means the price of a good which is ready to be shipped from a certain location. CIF: “Cost-

Insurance-Freight”, means the price of a good including shipping and delivering costs to a certain location.


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For North West Europe the crude oils used were Ekofisk (origin Norway, FOB) and

the Urals NWE (origin Russia, CIF Rotterdam). The products were “Unleaded Gasoline

FOB barges (Premium Gasoline unleaded)”, “Gas Oil FOB cargoes (Sulphur 0.2%)7”and

“Low Sulphur Fuel Oil FOB barges (Sulphur 1%)”.

Finally, for American Gulf Coast, the crude was West Texas Sour (WTS origin

U.S.A., FOB) and the product Gas Oil (sulphur 0.2%, FOB). In addition, Gasoline Future

prices were also considered. This is not a Platt’s assessment but a financial Future which is

traded at the New York Mercantile Exchange (Nymex). In this case, weekly data was

obtained by calculating arithmetic averages of daily settlement prices for the front month

future contract. .

6. Empirical Results and Conclusions

The market features previously described and the available dataset has dictated the

choice of the price pairs used as inputs in order to estimate the models introduced in section

4. We illustrate only those cases indicating a strong direction in the information flow

between two markets, both at price and volatility level.

In order to avoid the full presentation of statistical results, we report only the

Granger Causality and Exogenity test outcomes for VECM models. Regarding MGARCH

models, as already said, model selection is based on the Akaike info criterion (AIC) and

Schwarz criterion (SIC) and we report the estimation for the selected models. In the

following, we assume that asset “1” is the crude oil market and asset “2” is the product

market. Thus, crude oil price is equal to “S” in equation (1) and its variance is h11 in the

volatility models (4-7). Product price is equal to “F” in equation (2) and its variance is h22 in

the volatility models (4-7).

7 Please note that the benchmarks chosen in this analysis reflect the regulatory and market standards of the 1990s and

early 2000s. For example, effective January 1 2008, the maximum sulphur content of European Gasoil was lowered

from 0.2% to 0.1% which has resulted in all deliveries under this contract needing to meet the lower sulphur

specification.


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The investigation of the crude-product relationship for the Mediterranean confirms

indications found studying market features of this region. We propose the estimates for the

crude-product pair Iranian crude oil and LSFO. Granger Causality and Exogenity results

are presented in Table 1. In conclusion, past values of LSFO are significant in explaining

Crude oil current variation but the opposite is not true. Estimates suggest that in the short

run there is an information flow from product market to crude oil market. Conversely crude

oil may be considered as a weakly exogenous variable with respect to the product. The two

results at first appear contradictory but they are consistent. If we consider exogenity in a

VECM framework as a long run indication, we can state that in the short run there may be

an influence of product market on crude oil market but in the long run the crude oil price

drives the product price.

Turning to volatility, model selection criterions led us to choose model (7), and results are

presented in Table 2. Volatility interaction seems to confirm conclusions drawn at price

level. Product market shocks are significant in explaining crude oil volatility but the

opposite is not true. Moreover the value of the coefficient measuring the impact is very

high. BEKK overall covariance (matrix) structure is not simple to understand. Model (7) is

used to consider whether the same news affects the two markets simultaneously. The

multiplication of oil and product shocks is significant in the refined product volatility and

covariance equations. Moreover in both cases it has a negative sign. This means that when

one shock is positive and the other is negative, the product volatility and covariance will

tend to increase in the next period and vice versa. One interpretation of this result might be

that the arrival of unexpected news, which moves one market sharply in a different

direction, will impact the other market in the next period. Hence, an increase in covariance

is justified when shocks have opposite sign. Looking at the value of the coefficient, we are

able to say that the most important volatility information flow in the short run goes from

product market to crude market. But we cannot definitely state that there is no feedback

mechanism from the crude oil market to the product market in this time horizon. Moreover,

asymmetric effects are significant in all volatility equations.

The second considered crude/product pair in the Mediterranean basin consists of

Urals crude oil and Gas Oil. In the qualitative analysis of this market we have already


17

remarked that Gas Oil plays a critical role in this region. We report Granger Causality and

Exogenity results in Table 3. The hypothesis that Gasoil causes in the sense of Granger

Urals crude oil may be accepted only at 10% confidence level. However, the crude oil may

be still considered weakly exogenous. Therefore Granger Causality is less clear than in the

previous case.

Model (7) is chosen for volatility, and the corresponding estimate is reported in Table 4.

Gasoil shocks are significant in the Crude oil volatility equation whilst the converse is not

true. Shock interaction is significant for Gas Oil volatility at 10% level. The sum of the

results implie a relevant short run dynamic of information flow from product market to

crude oil market. Nevertheless we cannot exclude some small feedback mechanism from

crude oil market to Gas Oil in the short run. In the long run is the crude oil again which

leads product price as confirmed by the exogenity test.

In the North West Europe region, fuel oil should have a less important role than in

the Mediterranean area and we would expect price dynamics and volatility to reflect this

situation. We present the same analysis already used for the previous crude-product pairs. In

this case we study the relation between Ekofisk crude oil and LSFO. Crude oil causes in the

sense of Granger LSFO (at 10% confidence level) and it is weakly exogenous with respect

to the refined product (see Table 5). The point to note here is that in the short term the crude

oil also leads the product market. For volatility, we chose model (5) (see Table 6). Fuel Oil

shocks are not significant in any equation while crude oil shocks are always significant.

Consequently at the volatility level the conclusion seems clear: the relevant information

flow is from crude oil market to product market. There is no surprise here because, as

mentioned earlier, in this area the role of fuel oil is much less important than in the

Mediterranean area.

The US Gulf Coast district utilizes relatively low quality specification crude oils but

through technologically advanced refineries produces an elevated percentage of middle

distillate in the overall refined output. Moreover, this area is an exporter of refined products

and so future value of the refined mix should be an important consideration in Gulf Coast

refiner decisions. In our study we consider one of the highest specification, crude in this

area WTS, and an important middle-distillate, Gas Oil. In Table 7 we show that Gas Oil


18

causes in the sense of Granger WTS, that is to say that in the short run there is an important

information flow from product market to crude oil market. However, crude oil remains

weakly exogeneus, so we again see a recurring theme: in the short run product market news

may lead the crude oil market, but in the long run this is not the case.

Model (6) is chosen for volatility (see Table 8). Gas Oil market shocks are significant in

explaining crude oil volatility, but the opposite is not true. Results allow us to conclude that

there is a relevant information flow from product market to crude oil market in the short

run.

Furthermore we studied the relationship between crude price differentials and

product price differentials. We refer to North West Europe region. We considered a high-

quality crude and one of medium quality: Ekofisk and Urals. This area is producer and

exporter of gasoline while local supply struggles to satisfy increasing Gas Oil demand.

In this case, asset “1” is the crude differential and asset “2” is the product differential. As

we consider differentials, which are stationary series8, we cannot apply the VECM model

but instead use a simple VAR model9. In Table 9 we present the results of the Granger

Causality Test. Past values of the product differential are significant to explain current crude

oil differential at 5% confidence level. The opposite is true at 10% confidence level.

Therefore, at 10% confidence level, we cannot identify a unique direction in information

flow between crude oil and product markets. However, past values of the product

differential are significant to explain current crude differential. So we can say that news

which impacts product markets also impact crude oil markets.

Model (7) is chosen for volatility (see Table 10). Product differential is significant in the

crude oil volatility equation. Shocks that affect product markets also seem to affect the

crude oil markets. The value of coefficients may be surprising but we should comment that

in the previous analysis we were really considering “returns”, while the differential between

two prices (even in the form of a logarithm) is not the return of the position.

8 The spreads resulted stationary time-series in the period covered by this analysis. However, depending on the period

taken into consideration and timeframe, spreads may result not stationary.

9 In our framework, a standard bivariate VAR model can be described by Equation (1) and (2) where the ki coefficients

are equal to zero. Thus, in a standard VAR model there are no error correction terms. As the Weakly Exogenity test is

based on the ki coefficients, it is not possible to perform it within a standard VAR model.


19

Statistical results suggest that product markets play an important role in explaining short run

dynamics of crude oil markets in North West Europe area.

Table 11 presents a review of the established relationships between crude oil and refined

product. The only case in which crude oil completely determines product dynamics, in the

long and short run, is for the crude-product pair Ekofisk – Fuel Oil in North West Europe.

This suggests a general rule: where a product is not critical, the relevant information flow,

both at price level and at volatility level, moves from crude market to product market.

Where some refined products have particular significance, shocks to these markets may

affect crude oil markets. This phenomenon vanishes in the long run where crude oil price

leads in general the market.

Previously we noted the role of the Nymex Gasoline future contract in determining

European gasoline prices. With the tools already used we studied the relationship between

North West Europe Unleaded gasoline spot price and Nymex Gasoline future price (New

York). American future price – asset “1” – causes in the sense of Granger European spot

price – asset “2” – and it is weakly exogenous (see Table 12). So the Nymex future price

leads European spot price both in short and long run. Model (5) is chosen for volatility (see

Table 15). Nymex future shocks are significant in explaining European spot price volatility

but the opposite is not true. So also at volatility level the American future price seems to

anticipate European spot price. The results are conclusive: Nymex Future Gasoline prices

lead European spot prices both in the short and long run.

Acknowledgments

The authors would like to thank Professor Matteo Manera for the essential contribute to this

project, David Hale and Eugenio Monge for their comments and suggestions to the English

version.


20

References

Adrangi, B., Chatrath, A., Raffiee, K. and Ripple, R.D. (2001), “Alaska North Slope crude

oil price and the behaviour of diesel prices in California”, Energy Economics, 23, 29-

42

Asche, F., O.Gjolberg and Volker (2001), “Price Relationship in the Petroleum Market. An

analysis of crude oil and refined product prices”, Discussion Paper, Agricultural

University of Norway.

Bessler, D.A., D.J. Leatham and Yang (2001), “Asset Storability and Price Discovery in

Commodity Futures Markets: A new Look”, The Journal of Futures Markets, 21, No

3, 279-300.

Chan, K., Chan, K.C., Karolyi, G.A. (1991), “Intraday volatility in the stock index and stock

index futures markets”, Rev. Financ. Stud., 4, 657–684.

Chou, R.Y., Lin, J., Wu, C.S. (1999) “Modelling the Taiwan stock market and international

linkages”, Pac. Econ. Rev., 4, 305–320.

Compte, F. e O. Lieberman (2001), “Asymptotic theory for multivariate garch process”,

http://www.math-info.univ-paris5.fr/~comf/rev3gar.pdf

Donald, L. e T.H. Root (1999), “Convergence to the long-run equilibrium: the case of

natural gas markets”, Energy Economics, 21, 95-110

Duffie, D., S. Gray and P. Hoang (1999), “Volatility in Energy Prices”, in “Managing

Energy Price Risk”, Risk Publications.

Emery G.W. (2002), “An Analysis of the Relationship Between Electricity and Natural Gas

Futures Prices”, The Journal of Futures Markets, 2002, 22, No 2, 95-122.


21

Engle, R. and K.F. Kroner (1995), “Multivariate simultaneous generalized arch” University

of California, San Diego, Discussion Paper 89-57R .

Engle, R. F. and C. W. J. Granger (1987), “Co-integration and Error Correction:

Representation, Estimation and Testing”, Econometrica, 55, 251-276.

Engle, R., Ito, T., Lin, W. (1990), “Meteor showers or heat waves? Heteroscedasticity intra-

daily volatility in the foreign exchange markets”, Econometrica, 58, 525–542.

Ewing T.B., F. Malik and O. Ozfidan (2002), “Volatility transmission in the oil and natural

gas markets”, Energy Economics, 24, 525-538

Galeotti, M., A. Lanza and M. Manera (2001), “Rockets and Feathers Revisited: An

International Comparison on Gasoline Markets”, Working Paper Bocconi University.

Gallant, A.R., P. Rossi and G. Tauchen (1992), “Stock prices and volume”, Review of

Financial Study, 5, 199-242.

Girma P.B. and A.S. Paulson (1999), “Risk Arbitrage Opportunities in Petroleum Futures

Spreads”, The Journal of Futures Markets, 19, No 8, 931-955.

Gjølberg, O., Johnsen, T. (1999), “ Risk Management in the Oil Industry: Can Information

on Long – Run Equilibrium Prices be Utilized?”, Energy Economics, 21, 517-527.

Horsnell, P. (2000), “The Mediterranean Basin in the world petroleum Market”, Oxford

Institute for Energy studies.

Serletis, A., Herbert J. (1999), “The message in North America energy prices”, Energy

Economics, 21, 471-483.


22

Indjehagopian J.P., F. Lantz and V. Simon (2000), “Dynamics of heating oil market prices

in Europe”, Energy Economics, 22, 225-252.

Johansen, S. (1988) “Statistical Analysis of Cointegration Vectors,” Journal of Economic

Dynamics and Control, 12, 231-254.

Johansen, S. (1991), “Estimation and Hypothesis Testing of Cointegration Vectors in

Gaussian Vector Autoregressive Models”, Econometrica, 59, 1551-1580.

Johansen, S. and K. Juselius (1994), “Maximum Likelihood Estimation and Inference on

Cointegration - with Applications to the Demand for Money,” Oxford Bulletin of

Economics and Statistics, 52, 169-210.

Kearney, C., Patton, A.J. (2000), “ Multivariate GARCH modeling of exchange rate

volatility transmission in the European monetary system”. Financ. Rev., 41, 29–48.

Kellard, N., P.Newbold, T. Rayner and C. Ennew (1999), “The Relative Efficiency of

Commodity Futures Markets”, The Journal of Futures Markets, 1999, 19, No 4, 413-

432.

Killen, P.J., K.G. Spletter, N.K. Earnest and B.L. Stults (2001), “Refinery-Profitability

Statistics Begin in this Issue”, Oil & Gas Journal 99 pp.46-50 (January 15th, 2001)

Kroner K.F, and V.K Ng (1998), “Modelling Asymmetric Comovements of Asset

Returns”, Review of Financial Studies, 11, 817-844.

Lin, W. (1992), “Alternative estimators for factor Garch models – A Monte Carlo

comparison, Journal of Applied Econometrics, 7, 259-279

Low, A.H.W, J. Muthuswamy and R.I. Webb (1999), “Arbitrage, Cointegration, and the

Joint Dynamics of Prices Across Discrete Commodity Futures Auctions”, The

Journal of Futures Markets, 19, No 7, 799-815.


23

Mauro, A., (1999), “Price Risk Management in the Energy Industry: The Value at RiskApproach”. Freely available at http://ssrn.com/abstract=1020917

Ng, V. and S.C. Pirrong (1994), “Fundamentals and Volatility: Storage, Spreads, and the

Dynamics of Metal Prices”, Journal of Business, 67, 203-230.

Ng V. and S.C. Pirrong (1996), “Price dynamics in refined petroleum spot and futures

markets”, Journal of Empirical Finance, 2 ,359-388.

Pagan, A and G.W. Schwert (1990), “Alternative models for conditional stock volatility”,

Journal of Econometrics, 45, 267-290.

Partha D., Pravin K.T. and P. Varangis (1996), “The Excess Co-Movement of Commodity

Prices Reconsidered”, Journal of Applied Econometrics, 11, No 3, 275-291.

Serlitis, A. (1994), "A cointegration analysis of petroleum futures prices", in Energy

Economics, 16, 93-97.

Serletis A. and J. Herbert (1999), “The message in North America energy prices”, Energy

Economics, 21, 471-483.


24

APPENDIX

Table A

Regions US Gulf Coast US East Coast US Midwest Northwest

Europe (ARA)

Process % capacity % capacity % capacity % capacity

Crude distillation 100.00 100.00 100.00 100.00

Vacuum distillation 48.00 40.00 42.70 30.60Fluid catalytic cracking 36.70 37.90 35.50 26.80Hydrocraking 8.90 2.60 5.20 5.70

Coking 13.90 5.00 10.20 2.60

Crude Quality

Gravity, API 30.70 33.20 32.60 37.30

Sulphur, wt % 1.48 0.94 1.35 0.77

Source: Oil &Gas Journal, January 15 2001 page 47


25

Table 1 Granger Causality and Exogenity for VECM (equations 1 - 3)

N° lags: 10

Granger Causality

Hypothesis: Lsfo doesn't cause Iranian Hypothesis: Iranian doesn't cause Lsfo

Chi-square df Prob Chi-square df Prob

19.08711 10 0.0392 9.227736 10 0.5106

Weakly Exogenity

Value -0.0350 0.0770

Standard error 0.0381 0.0253

Note: boldface type means parameter significant at 5% level, italic type at 10% level

1 2

Table 2 Estimates for Garch Model 7

Dependent Variable

Parameter 0.00138 0.03480 0.62174 -0.29420 0.23327 0.11380

Wald-test (param.=0) 0.00005 0.39479 0.00738 0.14729 0.01764 0.45964

Parameter 0.00081 0.02503 0.15725 -0.12548 0.37872 0.00781

Wald-test (param.=0) 0.00000 0.17488 0.04085 0.04166 0.00174 0.91796

Parameter 0.00038 0.02951 0.31268 -0.19873 0.29723 0.02981Wald-test (param.=0) 0.00426 0.14534 0.00991 0.01542 0.00030 0.84634

Note: boldtype type and italic type as in table 1

Explanatory Variables

211c

1211cc

th ,11

th ,22

th ,12

21,1 t

21,1 t

21,1 t

21,2 t

21,2 t

21,2 t

21,1 t

21,2 t

1,21,1 tt

1,11 th

1,22 th

1,12 th

1,21,1 tt

1,21,1 tt

1,21,1 tt

222

212

cc


26


N°lags:10

Granger Causality

Hypothesis: gas oil doesn't cause Urals Crude Oil Hypothesis: Urals Crude Oil doesn't cause gas oil

Chi-square df Prob. Chi-square df Prob.

29.29036 10 0.0011 16.47119 10 0.0869

Weakly Exogenity

Value 0.0172 0.1133

Standard errors 0.0435 0.0354


1 2


Dependent Variable

Parameter 0.00004 0.00198 0.09822 -0.02789 0.08414 0.88883

Wald-test (param.=0) 0.29742 0.75122 0.01761 0.56383 0.00154 0.00000





211

c

1211cc

th ,11

th

,22

th ,12

21,1 t

21,1 t

21,1 t

21,2 t

21,2 t

21,2 t

21,1 t

21,2 t

1,21,1 tt

1,11 th

1,22 th

1,12 th

1,21,1 tt

1,21,1 tt

1,21,1 tt

222

212

cc


27


N° lags:6

Granger Causality

Hipothesis: Lsfo doesn't cause Ekofisk Hipothesis: Ekofisk doesn't cause Lsfo


6.358014 6 0.3843 10.64858 6 0.0999

Weakly Exogenity

Value -0.0030 0.0706

Standard Error 0.0287 0.0184


1 2


Dependent Variable

Parameter 0.00106 0.27167 0.00977 -0.10306 0.27663

Wald-test (param.=0) 0.00068 0.00133 0.58571 0.31042 0.09402

Parameter 0.00065 0.05592 0.06648 0.12194 0.00003Wald-test (param.=0) 0.00000 0.02023 0.13731 0.00087 0.98886

Parameter 0.00041 0.12325 -0.02549 0.11101 -0.00283Wald-test (param.=0) 0.00021 0.00216 0.23578 0.04526 0.97766


Explanatory Variable

211c

1211cc

th ,11

th

,22

th ,12

21,1 t

21,1 t

21,1 t

21,2 t

21,2 t

21,2 t

1,11 th

1,22 th

1,12 th

1,21,1 tt

1,21,1 tt

1,21,1 tt

222

212

cc


28


N° lags:7

Granger Causality

Hipothesis: Gasoil doesn't cause WTS Hipothesis: WTS doesn't cause Gasoil

Chi-square df Prob. Chi-square df Probabilità

56.85119 7 0.0000 6.552531 7 0.4769

Weakly Exogenity

Value 0.0388 0.1379



1 2


Dependent Variables

Parameter 0.0001 0.0300 0.1066 0.8230 0.0454

Wald-test (param.=0) 0.1425 0.2689 0.0003 0.0000 0.2330

Parameter 0.0001 0.0260 0.0627 0.7991 0.0238

Wald-test (param.=0) 0.1188 0.3089 0.0463 0.0000 0.2760

Parameter 0.0001 0.0434 0.8110 -0.0329Wald-test (param.=0) 0.0637 0.0077 0.0000 0.0149



21 1c

21,1 t 2

1,2 t 21,1 t1,11 th

21,1 t 2

1,2 t 21,2 t

1,22 th

1,21,1 tt 1,12 th1,21,1 tt 1211

cc

th ,1 1

th ,22

th ,12

222

212

cc


29


Dependent Variable

Parameter 0.00001 0.10378 0.00555 -0.04800 0.01127 0.73519

Wald-test (param.=0) 0.01066 0.00014 0.01305 0.00094 0.74621 0.00000


Parameter 0.00000 -0.16572 -0.00918 0.07802 0.02010 -0.79808Wald-test (param.=0) 0.91844 0.01471 0.05844 0.00424 0.51530 0.00000



211c

1211cc

th ,11

th ,22

th ,12

21,1 t

21,1 t

21,1 t

21,2 t

21,2 t

21,2 t

21,1 t

21,2 t

1,21,1 tt

1,11 th

1,22 th

1,12 th

1,21,1 tt

1,21,1 tt

1,21,1 tt

222

212 cc

Table 9 Causality for VAR(7)

N° lags:7

Granger Causality

Hypothesis: product diff. doesn't cause crude diff. Hypothesis: crude diff. doesn't cause product diff.


29.76292 7 0.0001 12.61616 7 0.082


30

Table 11 Crude oil - refined product relationships: a summary

Area Relationship Granger Causality Exogenity Volatility Transmission

Med Iranian - Lsfo From product to oil* Oil From product to oil*

Med Urals - Gasoil From product to oil** Oil From product to oil*

Nwe Ekofisk - Lsfo From oil to product** Oil From oil to product**

Gc Wts - Gasoil From product to oil* Oil From product to oil*

Nwe Δ(Ekofisk - Urals) -

Δ(Benzina - Gasoil) #

From crudes to products* - From crudes to products*

' Med=Mediterranean, Gc= Gulf Coast and Nwe=North West Europe# As differentials are stationary series, we estimated a VAR model instead of a VECM.

* Significant at 5%** Significant at 10%


31


Dependent Variables

Parameter 0.00002 0.01581 0.00150 0.00973 0.96861

Wald-test (param.=0) 0.16596 0.13033 0.74140 0.44354 0.00000

Parameter 0.00002 0.11318 0.00110 -0.02229 0.88102Wald-test (param.=0) 0.28264 0.00002 0.77146 0.57616 0.00000

Parameter 0.00002 0.04230 -0.00128 0.00885 0.92378Wald-test (param.=0) 0.31858 0.01402 0.47052 0.72630 0.00000



211c

1211cc

th ,11

th ,22

th ,12

21,1 t

21,1 t

21,1 t

21,2 t

21,2 t

21,2 t

1,11 th

1,22 th

1,12 th

1,21,1 tt

1,21,1 tt

1,21,1 tt

222

212 cc


N° lags:3

Granger Causality

Hypothesis: European Spot doesn't cause Us Future Hypothesis: Us Future doesn't cause European Spot


2.578376 3 0.4613 14.30501 3 0.0025

Esogenità debole

Value -0.0016 0.1562



1 2

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