Oil and Product Price Dynamics in International Petroleum Markets

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Oil and Product Price Dynamics in

International Petroleum MarketsAlessandro Lanza, Matteo Manera and

Massimo Giovannini

NOTA DI LAVORO 81.2003

SEPTEMBER 2003

IEM International Energy Markets

Alessandro Lanza,Eni S.p.A., Roma, Fondazione Eni Enrico Mattei, Milano

and CRENoS, Cagliari, ItalyMatteo Manera,Department of Statistics, University of Milano-Bicocca, Italy

and Fondazione Eni Enrico Mattei, Milano, ItalyMassimo Giovannini,Fondazione Eni Enrico Mattei, Milano, Italy

This paper can be downloaded without charge at:

The Fondazione Eni Enrico Mattei Note di Lavoro Series Index:

http://www.feem.it/web/activ/_wp.html

Social Science Research Network Electronic Paper Collection:

http://ssrn.com/abstract=464760

The opinions expressed in this paper do not necessarily reflect the position of

Fondazione Eni Enrico Mattei
http://ssrn.com/abstract=464760http://ssrn.com/abstract=464760http://ssrn.com/abstract=464760http://ssrn.com/abstract=464760


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Oil and Product Price Dynamics in International Petroleum Markets

Summary

In this paper we investigate crude oil and products price dynamics. We present a comparison

among ten price series of crude oils and fourteen price series of petroleum products,considering four distinct market areas (Mediterranean, North Western Europe, Latin America

and North America) over the period 1994-2002. We provide first a complete analysis of crude

oil and product price dynamics using cointegration and error correction models. Subsequently

we use the error correction specification to predict crude oil prices over the horizon January

2002-June 2002.The main findings of the paper can be summarized as follows: a) differences

in quality are crucial to understand the behaviour of crudes; b) prices of crude oils whose

physical characteristics are more similar to the marker show the following regularities: b1)

they converge more rapidly to the long-run equilibrium; b2) there is an almost monotonic

relation between Mean Absolute Percentage Error values and crude quality, measured by

API gravity and sulphur concentration; c) the price of the marker is the driving variable of

the crude price also in the short-run, irrespective of the specific geographical area and thequality of the crude under analysis.

Keywords: Oil prices, Product prices, Error correction models, Forecasting

JEL: C22, D40, E32

The authors would like to thank Luigi Buzzacchi, Marzio Galeotti, Margherita Grasso andMicheal McAleer for helpful comments. Roberto Asti and Cristiana Boschi provided excellent

editorial assistance. This study does not necessarily reflect the views of Eni S.p.A..

Address for correspondence:

Alessandro Lanza

Fondazione Eni Enrico Mattei

Corso Magenta, 63

20123 Milano

Italy

Phone: +39-02-52036930

Fax: +39-02-52036946

E-mail: [email protected]


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1

Oil and Product Price Dynamics in International

Petroleum Markets

(Revised: 31 July 2003)

1. Introduction

Over the last 30 years, oil prices have been closely scrutinized by applied economic

literature. Literally hundreds of applied research and policy studies have examined the

role played by oil prices in determining economic growth or inflation rates, both in

developed and developing countries.

Recently, several studies have contributed to this literature by examining the relation

between the price of crude oil and refinery products. If we exclude the specialized

literature, however, much less attention has been given to understanding the price

dynamics for different crudes, even if the quality of crude oils available to refiners (and

consequently their prices) is a critical factor in the strategies employed by refiners

around the world.

Oil is not a homogenous commodity: as a number of experts have pointed out (see,

The International Crude Oil Market Handbook, 2001) there are over 160 different

internationally traded crude oils, all of which vary in terms of characteristics, quality,

and market penetration.

Crude oils are classified by density and sulphur content. Lighter crudes generally

have a higher share of light hydrocarbons i.e. higher value products - that can be

produced by simple distillation. Heavier crude oils give a greater share of lower-valued

products through simple distillation and require additional processing to produce the


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desired range of products. Some crude oils also have a higher sulphur content, an

undesirable characteristic in terms of both processing and product quality.

The quality of the crude oil determines the level of processing and re-processing

necessary to achieve the optimal mix of product output. As a result, price and price

differentials between crude oils also reflect the relative ease of refining. For example, a

premium crude oil like West Texas Intermediate (WTI), the U.S. benchmark, or Brent,

the European benchmark, have a relatively high natural yield of desirable Gasoline. In

contrast, almost half of the simple distillation yield from Urals is a heavy residue that

must be reprocessed or sold at a discount as crude oil.

Refiners are in competition for an optimal mix of crudes for their refineries, in line

with the technology of the particular refinery, the desired output mix and, more

important, the relative price of available crudes. In recent years, refiners have been

faced with two opposing forces: a combination of consumers' desires for lower prices

and government regulations specifying increasingly lighter products of higher quality

(the most difficult to produce) and supplies of crude oil that are increasingly heavier, i.e.

with higher sulphur content (the most difficult to refine).

The importance of identifying the way in which a given crude is linked to a specific

crude benchmark comes directly from market considerations: the pressure of falling

margins in the oil products market, combined with some degree of flexibility in supply

decisions, obliges refiners to seek opportunities in the free market to improve their

profits. Crudes are expected to continue to become heavier with higher sulphur content,

while environmental restrictions are expected to significantly reduce the demand for

high-sulphur content fuels. As a consequence, light sweet crudes will continue to be

available and in even greater demand than today. This is why an understanding of the


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price dynamics, and the role played by different crudes, is crucial for the modern oil

industry.

Because there are so many different varieties and grades of crude oil, buyers and

sellers have found it easier to refer to a limited number of reference, or benchmark,

crude oils. Other varieties are then priced at a discount or premium, according to their

quality. For any given crude oil, the price is considered to be linked to another crude oil

price (usually referred to as the marker). In this very simple scheme, to understand the

behaviour of a given crude oil would be sufficient to explain the behaviour of its

marker. However, the price difference between these two crudes is non-constant over

time. To enrich the relations it is necessary to include variables other than the price

marker to explain the oil price dynamics of the given crude.

In principle, several variables could affect this relation and could be used as

explanatory variables. Considering data availability, the common assumption is that

imbalances in the petroleum product price could reflect most of these missed variables.

For example: if, due to extraordinary seasonal factors, Gasoline demand were higher

than expected, this would be reflected into the relations between crudes according to

various specific characteristics.

This approach has been examined in several different papers. However the specific

economic literature on this issue is not very large. Adrangi, Chatrath, Raffiee and

Ripple (2001) analyze the price dynamics of a specific crude (the Alaska North Slope)

and its relation with US West coast diesel fuel price using a VAR methodology and a

bivariate GARCH model to show the casual relationship between the two prices.

Asche, Gjolberg and Volker (2003) make use of multivariate framework to test whether


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there is a long-term relationship between crude oil and refined product prices in the

North Western Europe market.

Gjlberg and Johnsen (1999) analyze co-movements between the prices of crude oil

and major refined products during the period 1992-98. Specifically, they explore the

existence of long-run equilibrium price relationships, and whether deviations from the

estimated equilibrium can be utilized for predictions of short-term price changes and for

risk management.

In this paper we present a comparison among crudes considering four distinct

market areas (Mediterranean, North Western Europe, Latin America and North

America) on ten prices series of crude oils and on fourteen price series of petroleum

products.

We provide first a complete analysis of crude oil and product price dynamics using

co-integration and error correction models over the period 1994-2002. Subsequently we

use the error correction specification to predict crude oil prices over the horizon January

2002-June 2002.

The main findings of the paper can be summarized as follows.

Differences in quality are crucial to understand the behaviour of crudes.

Prices of crude oils whose physical characteristics are more similar to the marker

show the following regularities:

a) they converge more rapidly to the long-run equilibrium.b) there is an almost monotonic relation between Mean Absolute Percentage Error

values and crude quality, measured by API gravity and sulphur concentration.

This evidence can be motivated by considering the presence of the marker as an


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explanatory variable: the closer the crude to the marker, the higher the

contribution of the latter in explaining and predicting the former.

The price of the marker is the driving variable of the crude price also in the short-

run, irrespective of the specific geographical area and the quality of the crude under

analysis.

This paper is organized as follows. Section 2 provides a description of the analyzed

data. Section 3 discusses the econometric methods and models. In Section 4 the

empirical results are reported and commented. The forecasting performance of the

estimated models is illustrated in Section 5. Concluding remarks close the paper.

2. Data description

Our analysis is based on ten prices series of crude oils and on fourteen price series of

petroleum products. These data cover four distinct market areas: Mediterranean (MED),

North Western Europe (NWE), Latin America (LA) and North America (NA). In the

first two areas the reference price for crude oil (marker) is represented by Brent, while

for the remaining two areas the benchmark crude is WTI. The petroleum products we

are considering belong to three different quality categories: unleaded Gasoline, Gasoil

and Fuel oil. Within the last class we distinguish between high sulphur Fuel oil (HSFO)

and low sulphur Fuel Oil (LSFO). The data frequency is weekly with the exception of

the LA market, where only monthly data are available, while the sample covers the

period 1994-2002. All crude oil prices are expressed in US$ per barrel, whilst product

prices are in US$ per metric ton. More details on the dataset are provided in Table 1.

Table 2 and Table 3 report, for both crude oils and petroleum products, the

coefficients of variation of price levels and the annualized standard deviation of price


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changes. On average, the coefficients of variation for crude prices are the double of the

coefficients of variation of product prices, suggesting that the behaviour of crude prices

is very close to that of financial assets. Moreover, if we look at the two groups

separately, we find an inverse relation between quality (measured by API gravity) and

the coefficient of variation. A possible interpretation is the subsidiary role played by

heavy crudes when light crudes become too expensive, while the lower-quality products

are more volatile since their price is intimately linked to the price of some specific

substitutes (e.g. natural gas).

Table 4 shows the percentage price correlations within crudes and between crudes

and products. Higher correlations occur when crudes and products similar in terms of

API gravity are analyzed. The evidence from Tables 3 and 4 should suggest that prices

characterized by more similar coefficients of variation (i.e. light crudes and heavy

products) are more correlated. However, the coefficient of variation is a measure of

long-run volatility, whereas price change correlation captures short-run movements in

price variations. Moreover, an increase in the demand of light products has the effect of

increasing the supply of both high-quality and low-quality products (see Gjolberg and

Johnsen, 1999). Such considerations justify the presence of higher correlation between

light (heavy) crudes and the top (bottom) of the barrel.

3. Model specification

Crude oil and product prices dynamics can be modelled with an Autoregressive-

Distributed Lag (ADL) specification:

( ) ( ) ( ) ( )1 2y yc m

t t t t t L p L p L p L p u = + + + + (1)


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where L is the lag operator, ( ) 11 ... ,P

P L L L = ( ) 0 1 ... ,Q

Q L L L = + + +

( ) 0 1 ...R

R L L L = + + + and ( ) 0 1 ...

S

S L L L = + + + . Capital letters P, Q, R and S

represent the optimal number of lags of the polynomials (L), (L), (L) and (L),

respectively. With ctp we indicate the price of the selected crude, whereasm

tp is the

price of the marker associated withc

tp , andi

tp , i=1,2, are the prices of two products;

tu is a white noise process. All variables are log-transformed.

Recent developments in time series econometrics suggest that the first step towards

the estimation of model (1) is to check whether or not the different price series are

stationary. Augmented Dickey-Fuller (ADF) tests for unit roots have been used and all

variables have been found to be integrated of order one, or I(1), with intercept but no

trend.1

Though non-stationary, the oil and product price series may form a linear

combination which is stationary, or I(0). If this is the case, the relevant price series are

said to be cointegrated. The basic model used to test for the presence of cointegration is

given by the static regression

1 2

0 1 2 3

y yc m

t t t t t p p p p = + + + + (2)

If the residuals t are I(0), then equation (2) provides the long-run or equilibrium

relationship between the relevant price series. When two or more variables are

1 The complete set of results is reported in Tables A1-A3 of the Appendix.


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cointegrated, we know from the Engle-Granger representation theorem that they admit

an error correction (ECM) formulation of the type:

1 2

11 1 1

0 1 2 3 1

1 0 0 0

Q P R S

y yc c m

t p t p q t q r t r s t s t t

p q r s

p p p p p

= = = =

= + + + + + (3)

where ( )1 20 1 2 3 y yc mt t t t t p p p p = + + + , ( )0 1 1P

p

== ,

( )1 0 1 1Q P

q pq p = == , ( )2 0 1 1R P

r pr p = == , and

( )3 0 1 1S P

s ps p

= == .

The coefficients i in equation (2) can be interpreted as long-run elasticities of the

crude price to the marker price and petroleum products prices. In other terms, each i

measures the percentage variation of crude oil price due to a unit percentage variation of

each explanatory variable.

The choice of explaining oil prices in terms of petroleum product prices relies on the

theory of derived demand, which states that the price of an input should be determined

by its contribution to the market value of the output reflected in its market price (see

Adrangi, Chatrath, Raffiee and Ripple, 2001, for a test of the causal relationship flowing

from product prices to crude oil price).

Equation (3) incorporates short-run and long-run effects, captured by coefficients

ij and , respectively. In particular, is the so-called long-run adjustment coefficient

which measures how fastc

tp converges towards the long-run equilibrium represented

by equation (2).


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4. Empirical results

For each of the eight selected crudes we should estimate, at least in principle, as

many specifications for equation (3) as the number of combinations of products (i.e. six

models for MED and NWE, three models for LA and NA).

Given the large number of resulting models, we use a simple criterion to select the

best specification for each crude. Following Stock and Watson (1993), we estimate an

augmented version of equation (2), formed by adding one lead and one lag to all the

independent variables (DOLS estimation). In this way we obtain corrected t-statistics

for each estimated coefficient, which allow us to select the specifications of the long-run

equation with the largest number of statistically significant parameters. If two or more

long-run specifications have the same number of significant coefficients, we select the

one whose associated ECM yields the largest number of statistically significant

parameters. The final product selection for each crude is reported in the third column of

Table 5.

As it is shown in Table 5, the sum of the estimated coefficients in equation (2)

(ignoring the intercept term) is approximately equal to one. Moreover, the null

hypothesis that this sum is equal to one is not rejected by the data in 5 cases out of 8. 2

These coefficients can be interpreted as the contribution (weight) given by each

independent variable to the determination of crude oil price. The price of the marker

dominates relation (2), while product prices play a sort of compensation role, in order to

preserve the one-to-one relation between the crude and the marker. If we exclude Maya

2A corrected Wald test, based on the DOLS coefficient estimates, rejects the null hypothesis at 1%

significance level for Kern River and Thums, and at 5% for Iranian.


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in the LA area, the coefficients of the corresponding selected pair of product prices

have opposite signs. The contribution of each product to the market value of a particular

crude oil is such that a constant balance between price of the crude and price of the

marker is maintained in the long-run.

Specifically, 1 is always larger than one, and its magnitude increases as heavier

crudes are considered. These features show that when the price of the marker increases

the demand of heavy crude oils increases, which, in turn, forces their price to rise more

than proportionally.

Furthermore, when the MED and NWE areas are considered, the long-run

coefficients 2 and 3

have positive and negative signs, respectively. The converse is

true when we concentrate on NA. A possible interpretation of this empirical evidence is

that, while Europe is characterized by two highly demanded light products (i.e. Gasoline

and Gasoil), only Gasoline has a primary role in North America. As a consequence, an

increase in the demand for Gasoline in Europe is met using very light crudes in the

production process of Gasoline, while medium-quality crudes are employed to produce

Gasoil. On the contrary, the North American refinery system is mainly oriented towards

the production of Gasoline, which explains the positive long-run correlation between

crude and Gasoline prices.

In all areas each crude price is cointegrated with the price of the marker and the

prices of the selected pair of products, according to the ADF tests on the residuals of the

long-run equation (2) reported in Table 6.

The best ECM specification is attained with the product pair LSFO-Gasoline for

seven crudes out of eight (the only exception is HSFO-Gasoline for Urals NWE). The

short-run coefficient of Gasoline in the ECM equation (3) is significant, in all markets


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and for all crudes, with the exception of Forcados. The more volatile product in the

short-run (Gasoline) is responsible of the short-run dynamics of the crude oil price. It is

well known that the refined barrel can be ideally divided in two classes of products:

high-quality (light) and low-quality (heavy) products. Hence, the best explanation of

both short-run and long-run behaviour of a crude oil price is obtained when we include

in the ECM specification the pair formed by the most representative products in each

class, that is LSFO-Gasoline (Table 7).

If we combine the information included in Table 1 with Table 7, it is easy to see that

the magnitude of the estimated long-run adjustment coefficients is sensitive to the

gravity of the specific crude, that is, with the exception of Forcados, a sort of monotonic

relation between speed of adjustment and API emerges. Prices of crude oils whose

physical characteristics are more similar to the marker are likely to converge more

rapidly to the long-run equilibrium.

Furthermore, the price of the marker is the driving variable of the crude price also in

the short-run, irrespective of the specific geographical area and the quality of the crude

under analysis (see Table 5)3.

5. Forecasting crude oil prices

We assess the ability of the ECM specification to predict crude oil prices over the

horizon January 2002-June 2002 by computing three different sets of forecasts: static,

dynamic and simulated. With the exception of LA area, where only monthly data are

available, we split the forecasting horizon (24 weeks) into six windows of four weeks,

with the purpose of partially neutralizing potential contingent factors that could affect

3The estimated short-run coefficients of the ECM are reported in Table A4 of the Appendix.


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the forecasting evaluation (e.g. changes in OPEC policy). Moreover, in order to make

the calculated forecasts comparable, instead of estimating the ECM just once and using

the same estimated parameters to calculate forecast values of the dependent variable for

each of the six windows, we re-estimate the ECM six times with a rolling-sample

technique: in this way, the forecast values in each window depend on updated

coefficients estimates from samples of the same size.

While static and dynamic forecasts are self-explanatory, the procedure we use to

generate the simulated forecasts needs some explanation. The aim of this exercise is to

produce true out-of-sample, multistep-ahead forecasts for the crude oil price, given

the presence of marker and product prices as exogenous variables in model (3). Lets

indicate with Tthe last in-sample observation for each window. Then:

i) For each variable 1 2 , , andy ymt t t t

p p p , we estimated an ARMA(1,1) model of the

type1 1 1 1t t t t

x x u u

= + + , t=2,..,T. Since all estimated ARMA(1,1) models are found

to be statistically adequate to capture the behaviour of these series, for each model we

calculated the residuals t

u .

ii) Each ARMA residual vector t

u , t=2,..,T, is bootstrapped R=1000 times, to obtain

bootstrapped residuals( )

b r

tu , where r=1,..,R=1000 indicates the r-th replication and

superscript b denotes a bootstrapped series.

iii) Each series 1 2 , , andy ymt t t t

p p p is simulated R times out-of-sample

(t=T+1,,T+h) using the estimated ARMA models of stage (i) and the bootstrapped


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residuals of stage (2). That is:( ) ( ) ( ) ( )

11 1 1

t

r r b r b r

t t t x u u

= + + , t=T+1,..,T+h, where the

superscript * denotes a simulated series, and h=4 (h=6 for the crudes of the LA area,

since only monthly data are available).

iv) for each series 1 2 , , andy ymt t t t

p p p , we select, among the R simulated series, that

series whose standard deviation is closest to the standard deviation of the actual series

(this last calculated using in-sample observations).

Formally: ( )( ) ( )( )min . . . .r

t t tr

Std Dev x Std Dev x

= , where tx denotes the selected

simulated series.

v) we re-estimate the ECM specification (3) over the sample t=k,..,T, where

( )max , , ,k P Q R S = , and we calculate the residuals t .

vi) Residualst

are bootstrapped R times, thus obtaining ( )b r

t .

vii) The dependent variable ctp is simulated R times, using the bootstrapped residuals of

the ECM model (stage vi) and the simulated exogenous series (stage iv):

( ) ( ) ( )1 211 1 1

1 0 1 2 3 1 ,

1 0 0 0

r

Q P R S c r c r y yc m b

t t p t p q t q r t r s t s t i t j

p q r s

p p p p p p

+

= = = =

= + + + + + +

t=T+1,..,T+h.

For crudes belonging to the MED, NWE and NA markets, we repeat this procedure for

all the 6 windows using the rolling-sample technique illustrated above.


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After completion of the three forecasting exercises, we obtain, for the MED, NWE,

and NA areas, 24 one-step-ahead (static) forecasts, 24 (dynamic) h-steps-ahead

forecasts (h=1,..,4) and 24 (simulated) forecast distributions, each formed by R=1000

simulated forecasts. All forecasts are collected in six windows of size 4. For the LA area

we produce 6 (static) one-step-ahead forecasts, 6 (dynamic) h-steps-ahead forecasts

(h=1,..,4) and 6 (simulated) forecast distributions.

In order to evaluate the predictive ability of each ECM specifications, we calculate

the mean absolute percentage error (MAPE), the Theils inequality coefficient

(decomposed in bias, variance and covariance proportions) and the SR (success ratio),

which indicates the percentage number of times the forecasted series has the same sign

of the corresponding actual series. Moreover, for the simulated forecasts only, we

calculate a range of dispersion measures associated to each forecast distribution, as

follows. First, we compute the standard deviations of the distribution of forecasts in

each window and in each forecasting period (24 standard deviations). Second, we

calculate the mean of the 24 standard deviations. Third, for each window, we calculate

the mean of the standard deviations relative to the h-th forecasting point, h=1,,4

(mean of 6 standard deviations).

Results from static and dynamic forecast are reported in Table 9. The following

comments apply.

First, due to the different data frequencies, a direct comparison between the LA

market and the remaining areas is not possible, although comments that hold for the

weekly series can be directly extended to the monthly data.


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Second, if we rank the different crudes according to the forecasting performance of

the corresponding ECM specifications using the MAPE, the same ranking holds

irrespective of whether the forecasts are static or dynamic. The only exception is Iranian

heavy, whose dynamic forecasts seem to be relatively better than the static predictions.

Third, there is an almost monotonic relation between MAPE values and crude

quality, measured by API gravity and sulphur concentration. Actually, among the

crudes with similar gravity, crudes with less sulphur are characterized by lower MAPE.

This evidence can be motivated by considering the presence of the marker as an

explanatory variable: the closer the crude to the marker, the higher the contribution of

the latter in explaining and predicting the former.

Fourth, from inspection of the Theils statistic, we experience an increase of the bias

proportion and a correspondent reduction of variance and covariance proportions when

moving from static to dynamic forecasts. Nonetheless, the values of the Theils

coefficient are generally quite small, indicating a good predictive fit.

Fifth, the low value of the variance proportion in the dynamic forecasts is perfectly

consistent with the values of SR.

Results from the simulated forecasts are reported in Table 10. MAPE, Theils

coefficient and SR are calculated on the mean of each forecasted distribution. As

expected, the forecasting performance for each model is slightly worse than in the static

and dynamic cases. Nevertheless, taking into account the crudes from the LA area, we

find that this kind of forecasts performs relatively better for heavier crudes. Actually,

MAPE values are almost five times larger than those obtained from the dynamic

forecasts in NWE, and almost twice than in NA. Conversely, the heaviest crude in LA


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(i.e. Boscan) has MAPE values which are less than twice those of the dynamic forecast,

while Maya, the lightest crude in that area, has a MAPE value which is four times

larger.

The SR, though lower than in both static and dynamic cases, has values which are

higher than 0.50, meaning that the simulated series produce reasonable predictions of

the turning points of crude prices.

The second section of Table 10 reports several dispersion measures of the forecasted

distributions. The mean of all the standard deviations (SD) indicates that lower

predicting variability is associated with higher quality crudes. The overall coherence of

the simulation exercise is guaranteed by the values of each standard deviation, which

increase as the forecasting horizon increases.

6. Conclusions

This paper presents two different exercises that need to be commented in a separate

way even if there are some common interesting features.

The first conclusion is related to the different relation between a given crude, its

area-specific market and the related petroleum products. In this paper we investigate

crude oil and products price dynamics using cointegration and ECM. Empirical

evidence shows that product price are statistically relevant in explaining short- and

long-run adjustment in petroleum markets. The relevant product mix also depend on the

specific market area and on the characteristics of the selected crude. It is also worth to

underline that the long-run adjustment coefficients are sensitive to the gravity of the

specific crude. Prices of crude oils whose physical characteristics are more similar to


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the marker are likely to converge more rapidly to the long-run equilibrium.

Furthermore, the price of the marker is the driving variable of the crude price also in the

short-run, irrespective of the specific geographical area and the quality of the crude

under analysis.

The second conclusion is related to the part of the paper aimed at assessing the

ability of the ECM specification to predict crude oil prices over the horizon January

2002-June 2002. We computed three different sets of forecasts, namely static, dynamic

and simulated, and in general the lower predicting variability is associated with higher

quality crudes. Also in this case there is almost monotonic relation between MAPE

values and crude quality, measured by API gravity and sulphur concentration.

Actually, among the crudes with similar gravity, crudes with less sulphur are

characterized by lower MAPE. This evidence can be motivated by considering the

presence of the marker as an explanatory variable: the closer the crude to the marker,

the higher the contribution of the latter in explaining and predicting the former.


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References

Adrangi, B., A. Chatrath, K. Raffiee and R.D.Ripple (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 T. Vlker (2003), Price relationships in the petroleum

market. An analysis of crude oil and refined product prices,Energy Economics, 25,

289-301.

Engle, R.F., and C.W.J. Granger (1987), Co-integration and error correction:

representation, estimation and testing,Econometrica, 55, 251-276.

Gjolberg, O., and T. Johnsen (1999), Risk management in the oil industry: can

information on long-run equilibrium prices be utilized?, Energy Economics, 21

517-527.

The International Crude Oil Market Handbook (2001), Energy Intelligence Group,

Fourth Edition.

MacKinnon, J.G. (1991), Critical values for co-integration tests, in R.F. Engle and

C.W.J. Granger (eds.), Long-run Economic Relationships, Oxford, Oxford

University Press.

Stock, J. and M. Watson (1993), A simple estimator of cointegrating vectors in higher

order integreted systems,Econometrica, 61, 783-820.


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Table 1. Dataset

Area:

Mediterranean

(MED)

North Western Europe

(NWE)

Latin America

(LA)

N

Marker: Brent (38.3, 0.37%) Brent WTI (39.6, 0.24%)

Crudes:-Urals MED (32, 1.3% )-Iranian heavy (30.2, 1.77%) -Urals NWE (32, 1.3%)-Foracdos (31, 0.19%) -Maya (21.8, 3.33%)-Boscan (10.1, 5.4%) -Kern Riv-Thums (

Products:

-Premium Gasoline-Gasoil-Low Sulphur Fuel Oil(LSFO)

-High Sulphur Fuel Oil(HSFO)

-Premium Gasoline-Gasoil-Low Sulphur Fuel Oil(LSFO)

-High Sulphur Fuel Oil(HSFO)

-Super Unleaded-Gasoil N2-Low Sulphur Fuel Oil(LSFO)

-Super U-Gasoil N-Low Sul(LSFO)

Sample: 10/7/1994-06/28/2002 10/7/1994-06/28/2002 01/1994-06/2002 10/7/1994Frequency: weekly weekly monthly Note to Table 1. Sources Platts and Petroleum Intelligence Weekly (2000); API gravity and sulphur content (%) are reported

LA and NA.


22/40

Table 2. Descriptive statistics: crude oil prices

Coefficient of variation (CV)

Percent price level

Annualized standard deviation (ASD)

Percent price variation

Brent 9.40 33.53

Urals MED 9.62 37.37MED

Iranian 10.47 39.23Urals NWE 9.52 36.50

NWEForcados 9.42 34.70

WTI 8.40 26.82

Maya 11.49 38.28LA

Boscan 12.67 35.48

Kern River 12.96 35.67NA

Thums 11.76 31.87

Note to Table 2.All prices are expressed in logs. ( ) 100 p pCV = where 1T

p ttp T

== and

( ) ( )2

21 1

T

p t pt p T == and ( )100 pASD n= , where n is the number of observations

per year, ( ) ( )2

2

1 1

T

p t ptp T

== and 1

T

p ttp T

== .


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21

Table 3. Descriptive statistics: prices of products

Coefficient of variation (CV) Annualized standard deviation (ASD)

MED NWE LA NA MED NWE LA NA

Gasoline 5.08 4.99 4.48 4.56 30.24 31.18 36.56 35.77

Gasoil 5.58 5.14 4.86 4.90 30.64 26.53 25.10 29.38

LSFO 5.46 5.05 5.80 5.85 29.38 25.33 33.01 31.12

HSFO 6.07 5.66 - - 32.41 33.74 - -

Notes to Table 3. See Table 2


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22

Table 4. Price change correlations

BrentUrals

MEDIranian

Urals

NWEForcad. WTI Maya Boscan

Kern

RiverThums

Brent 1.00

Urals MED 0.96 1.00Iranian 0.96 0.99 1.00

Urals NWE 0.98 - - 1.00

Forcados 0.99 - - 0.97 1.00

WTI 1.00

Maya 0.91 1.00

Boscan 0.76 0.84 1.00

Kern River 0.68 - - 1.00

Thums 0.70 - - 0.96 1.00

Gasoline 0.63 0.59 0.59 0.62 0.620.74m

0.57w

0.70 0.53 0.44 0.45

Gasoil 0.66 0.63 0.63 0.66 0.660.83m

0.65w0.78 0.64 0.48 0.49

LSFO 0.45 0.41 0.42 0.40 0.440.71m

0.43w0.81 0.67 0.44 0.48

HSFO 0.37 0.33 0.33 0.49 0.52 - - - - -

Notes to Table 4. m= monthly; w= weekly.


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23

Table 5. Estimation of the long-run relationship

Crudes Products

(y1, y2)

R21

2 3

Urals MED

LSFO,

Gasoline 0.99

1.04***

(11.69)

0.12*

(1.72)

-0.16**

(-2.58)MED

IranianLSFO,

Gasoline0.99

1.13***

(11.00)

0.18**

(2.34)

-0.24***

(-3.34)

Urals NWEHSFO,Gasoline

0.991.01

***

(11.54)0.11

*

(1.83)-0.13

**

(-2.14)NWE

ForcadosLSFO,Gasoline

0.991.06

***

(16.23)0.01(0.43)

-0.08(-1.51)

MayaLSFO,Gasoline

0.951.85

***

(4.69)-0.52

*

(-1.63)-0.20(-0.58)

LA

BoscanLSFO,

Gasoline

0.912.04***

(3.53)

-0.87*

(-1.85)

0.03

(0.06)Kern River

LSFO,

Gasoline0.94

1.35***

(3.77)

-0.07

(-0.24)

0.04

(0.13)NA

ThumsLSFO,

Gasoline0.95

1.32***

(4.70)

-0.10

(-0.42)

0.03

(0.11)

Notes to Table 5. i i=1,..,3, are the DOLS estimates of the augmented dynamic regression

1 2 1 2

0 1 2 3

r r ry y y yc m m

t t t t i t i i t i i t i t i r i r i r p p p p p p p

= = == + + + + + + + , with r=1

(see Stock and Watson, 1993), in parentheses the rescaled t-statistics; * (**)[***] indicates significance

at 10% (5%) [1%]


26/40

24

Table 6. Cointegration tests

Crudes Products

(y1, y2)

a b p ADF

Urals MED LSFO,

Gasoline

no no 2 -5.98***

MED

Iranian LSFO,

Gasoline

no no 2 -5.98***

Urals NWE HSFO,Gasoline

no no 2 -5.33***

NWE

Forcados LSFO,Gasoline

no no 1 -4.88***

Maya LSFO,Gasoline

no no 0 -3.82(57.96

***)

LA

Boscan LSFO,

Gasoline

no no 0 -3.79

(53.72

***

)Kern River LSFO,

Gasoline

no no 2 -5.09***

NA

Thums LSFO,

Gasolineno no 0 -5.55

***

Notes to Table 6.ADFis the calculated ttest for the null hypothesis of no cointegration (i.e. =0) in the

Augmented Dickey-Fuller regression on ^t: 1 1

p

t t i t i t ia bt v

= = + + + + , where ^t are

the estimated residuals of the DOLS regression; p is the order of the augmentation needed to eliminate

any autocorrelation in the residuals of the ADF regression; * (**)[***] indicates significance at 10%

(5%) [1%] on the basis of the critical values by MacKinnon, (1991); for crudes in the LA area the

Johansens (1991) trace test is reported in parentheses.


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25

Table 7. Selected products and long-run adjustment coefficients

MED NWE LA NA

Selected

products

LSFO-Gasoline

(Urals, Iranian)

HSFO-Gasoline

(Urals)

LSFO-Gasoline

(Forcados)

LSFO-Gasoline

(Maya, Boscan)

LSFO-Gasoline

(Kern River, Thums)

Long-run

products

LSFO-Gasoline

(Urals, Iranian)

HSFO-Gasoline

(Urals)

-

(Forcados)

LSFO

(Maya, Boscan)-

Short-run

products

Gasoline

(Urals, Iranian)

Gasoline

(Forcados)

LSFO-Gasoline

(Maya, Boscan)

LSFO-Gasoline

(Kern River, Thums)

Long-run

adjustment

coefficients

( )

-0.12

(Urals, Iranian)

-0.11

(Urals)

-0.06

(Forcados)

-0.15

(Maya)

-0.09

(Boscan)

-0.07

(Kern River, Thums)

Notes to Table 7. Selected products = pair of products corresponding to the best model specifications (1)and (2); long-run products = products whose coefficients are statistically significant in the long-run

relation (1); short-run products = products whose short-run coefficients are statistically significant inmodel (2); crudes associated with selected products, long-run products, short-run products and long-run

adjustment coefficients (see equation (2)) are reported in parentheses.


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26

Table 8. Static and dynamic forecast evaluation of selected ECM models

MED NWE LA NA

Urals

medIranian

Urals

NWEForcad. Maya Boscan

Kern

RiverThums

MAPE 0.26 0.37 0.24 0.08 0.96 1.95 0.74 0.86

Theil 0.002 0.002 0.002 0.001 0.01 0.01 0.004 0.005BP 0.29 0.06 0.31 0.54 0.29 0.49 0.26 0.28

VP 0.29 0.35 0.29 0.14 0.003 0.03 0.44 0.35Static

Forecasts

CP 0.42 0.59 0.41 0.32 0.71 0.49 0.30 0.37

MAPE 0.55 0.52 0.52 0.19 2.08 5.32 1.48 1.39

Theil 0.003 0.003 0.003 0.001 0.01 0.03 0.01 0.01

BP 0.62 0.63 0.71 0.74 0.82 0.68 0.79 0.65

VP 0.14 0.18 0.21 0.11 0.07 0.27 0.17 0.29

CP 0.25 0.19 0.08 0.15 0.11 0.05 0.04 0.06Dynamic

Forecasts

SR 0.875 0.958 1.00 0.958 1.00 1.00 0.958 0.958

Notes to Table 8. Static forecasts indicate one-step-ahead forecasts, dynamic forecasts indicate 4-step-ahead forecasts (6 steps for LA area); MAPE is the mean absolute percentage error, Theil is the Theils

Inequality Coefficient and BP, VP, CP are the bias, variance, and covariance proportions. SR is the meanof the success ratio calculated as the percentage number of times the sign of the forecasted series is the

same as the sign of the actual series. All the reported values, with the exception of those referring to LA,

are mean values calculated over the 6 forecast windows.


29/40

27

Table 9. Simulated forecast evaluation of selected ECM models

MED NEW LA NA

Urals

medIranian

Urals

NWEForcad. Maya Boscan

Kern

RiverThums

MAPE 2.42 2.26 2.40 2.09 9.83 9.56 3.54 3.22

Theil 0.01 0.01 0.01 0.01 0.06 0.06 0.02 0.02

BP 0.69 0.51 0.61 0.67 0.75 0.67 0.66 0.59

VP 0.29 0.45 0.37 0.30 0.25 0.33 0.19 0.26

CP 0.02 0.04 0.02 0.03 0.004 0.005 0.16 0.14Mean

SR 0.58 0.5 0.71 0.54 0.67 0.66 0.625 0.54

SD 0.50 0.53 0.38 0.16 0.86 1.44 0.91 0.68SD1 0.25 0.27 0.19 0.09 - - 0.55 0.47

SD2 0.45 0.48 0.35 0.15 - - 0.80 0.62

SD3 0.58 0.62 0.45 0.19 - - 1.03 0.73Dispersion

SD4 0.70 0.73 0.51 0.22 - - 1.26 0.88

Notes to Table 9. Simulated forecast stands for true out of sample 4 (6) step-ahead forecast. In order to

calculate the reported measures of dispersion we proceeded as follows: i) we calculated the standard

deviations of the distribution of forecasts in each window and in each forecasting period (24 standard

deviations); ii) in order to obtain SD we calculated the mean of all the standard deviations of point i.

(mean of 24 standard deviations); iii) in order to obtain SDk k=1,..,4we calculated the mean by window

of the standard deviations referring to k-th forecasting point (mean of 6 standard deviations).


30/40

28

Appendix

Table A1.Unit root tests: Crudes

a b P ADF

Brent yes no 1 -2.06

Brent no no 0 -15.94**

Urals med yes no 1 -2.31

Urals med no no 0 -15.81**Iranian yes no 1 -2.24

Iranian no no 0 -15.90**

Urals NWE yes no 1 -2.24

Urals NWE no no 0 -16.07**

Forcados yes no 1 -2.18

Forcados no no 0 -15.60**

WTI yes no 0 -1.69

WTI no no 0 -8.70**

Maya yes no 0 -1.82

Maya no no 0 -8.33**

Boscan yes no 1 -2.24

Boscan no no 0 -6.95**

Kern River yes no 1 -2.26

Kern River no no 0 -15.52**

Thums yes no 1 -2.11

Thums no no 0 -16.00**

Notes to Table A1. ADFis the calculated t test for the null hypothesis of a unit root (i.e. =0) in the series

xt from the Augmented Dickey-Fuller regression: 1 11

p

t t i t t ix a bt x x

= = + + + + ; p is the

order of the augmentation needed to eliminate any autocorrelation in the residuals of the ADF regression;

* (**)[***] indicates significance at 10% (5%) [1%] on the basis of the critical values by MacKinnon,

J.G. (1991) Critical Values for Co-Integration Tests, in R.F. Engle and C.W.J. Granger (eds.), Long-run

Economic Relationships, Oxford, Oxford University Press..


31/40

29

Table A2. Unit root tests: Products, Europe

MED NWE

a b p ADF a b p ADF

Gasoline yes no 1 -2.18 yes no 1 -2.15

Gasoline no no 0 -14.17** no no 0 -15.02**Gasoil yes no 1 -2.03 yes no 1 -1.84

Gasoil no no 0 -14.63** no no 0 -15.12**

LSFO yes no 1 -2.50 yes no 1 -2.16

LSFO no no 0 -12.51** no no 0 -13.45**

HSFO yes no 2 -2.44 yes no 1 -2.19

HSFO no no 1 -11.26** no no 0 -15.59**

Notes to TableA2. see Table A1


32/40

30

Table A3. Unit root tests: Products, America

LA NA

a b p ADF a b p ADF

Gasoline yes no 0 -2.27 yes no 1 -2.73

Gasoline no no 0 -9.51** no no 0 -16.34**Gasoil yes no 1 -1.88 No no 1 -1.73

Gasoil no no 0 -7.68** no no 0 -19.25**

LSFO yes no 0 -1.73 yes no 1 -2.37

LSFO no no 0 -8.92** no no 0 -14.54**

Notes to TableA3. see Table A1


33/40

31

Table A4. ECM model estimates

Urals

MED Iranian

Urals

NWE Forcados Maya Boscan

Kern

River ThumsProducts

(y1, y2)

LSFO,

Gasoline

LSFO,

Gasoline

HSFO,

Gasoline

LSFO,

Gasoline

LSFO,

Gasoline

LSFO,

Gasoline

LSFO,

Gasoline

LSFO,

Gasoline

01 0.51***

(11.15)

0.42***

(8.89)

0.48***

(10.56)

0.30***

(6.04)- -

0.02

(0.37)

-0.05

(-1.13)

02 -0.22

***

(-4.46)-0.15

***

(-2.95)-0.28

***

(-5.88)-0.001(-0.03)

- -0.03(0.57)

-0.02(-0.54)

03 - - - - - -0.13(3.19

***)

0.15(3.53

***)

10 1.11***

(64.9)

1.15***

(59.53)

1.09***

(74.96)

1.04***

(167.06)

1.11***

(13.39)

0.98***

(6.74)

0.73***

(16.51)

0.66***

(17.67)11

-0.57***

(-10.8)

-0.49***

(-8.49)

-0.52***

(-10.2)

-0.29***

(-5.58)- -

0.33***

(5.84)

0.34***

(6.91)

12 0.23

***

(4.27)

0.16***

(2.77)

0.30***

(5.65)

-0.01

(-0.15)- -

0.10*

(1.76)

0.12**

(2.30)

13 - - - - - --0.02(-0.29)

0.002(0.05)

20 -0.02(-0.98)

0.003(0.17)

-0.02(-1.44)

-0.01(-1.19)

0.34***

(6.19)

0.27***

(2.70)

0.07*

(1.70)0.11

***

(2.96)

21 -0.01(-0.29)

-0.02(-0.87)

0.01(0.96)

-0.01(-0.77)

- --0.10

**

(-2.26)-0.08

**

(-2.03)

22 0.01

(0.79)0.02(0.82)

-0.01(-0.36)

0.01(1.24)

- - 0.02(0.36)

0.03(0.70)

23 - - - - - -0.01

(0.18)

0.02

(-0.91)

30 -0.04

**

(-2.01)

-0.04*

(-1.84)

0.01

(0.29)

-0.02**

(-2.51)

-0.08

(-1.47)

-0.17*

(-1.83)

-0.01

(-0.38)

-0.03

(-0.91)

31 0.07

***

(3.26)0.04

*

(1.81)0.001(0.05)

0.01(1.54)

- -0.09

**

(2.29)0.05

*

(1.64)

32 -0.04

**

(-2.04)-0.02(-0.75)

-0.02(-1.45)

0.01**

(2.08)

- -0.01(0.22)

0.03(0.77)

33 - - - - - --0.05(-1.37)

-0.04(-1.31)

-0.12***

(-5.56)

-0.12***

(-5.55)

-0.11***

(-5.45)

-0.06***

(-4.32)

-0.15***

(-3.75)

-0.10**

(-1.96)

-0.07**

(-4.18)

-0.07***

(-3.71)

BG-stat 0.01 0.63 0.71 2.07 0.61 6.21*

0.36 0.94

R2 0.95 0.94 0.97 0.99 0.90 0.64 0.64 0.67

Notes to Table A4. The ECM specification is

1 21 1 1 1

0 1 2 3 11 0 0 0

P Q R S y yc c m

t p t p q t q r t r s t s t t p q r sp p p p p

= = = = = + + + + + , where

P=Q=R=S; BG- stat is the LM version of the Breusch-Godfrey test for absence of first order residual

autocorrelation in the regression; * (**)[***] indicates significance at 10% (5%) [1%]


34/40

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Changes in the U.K.CLIM 68.2002 Barbara K. BUCHNER and Roberto ROSON: Conflicting Perspectives in Trade and Environmental Negotiations

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Affecting the Post-Privatisation Equity in Largest Hungarian FirmsPRIV 79.2002 Theodora WELCH and Rick MOLZ: How Does Trade Sale Privatization Work?

Evidence from the Fixed-Line Telecommunications Sector in Developing EconomiesPRIV 80.2002 Alberto R. PETRUCCI: Government Debt, Agent Heterogeneity and Wealth Displacement in a Small Open

EconomyCLIM 81.2002 Timothy SWANSON and Robin MASON(lvi): The Impact of International Environmental Agreements: The Case

of the Montreal ProtocolPRIV 82.2002 George R.G. CLARKE and Lixin Colin XU: Privatization, Competition and Corruption: How Characteristics of

Bribe Takers and Payers Affect Bribe Payments to Utilities PRIV 83.2002 Massimo FLORIO and Katiuscia MANZONI: The Abnormal Returns of UK Privatisations: From Underpricing

to Outperformance NRM 84.2002 Nelson LOURENO, Carlos RUSSO MACHADO, Maria do ROSRIO JORGE and Lus RODRIGUES: An

Integrated Approach to Understand Territory Dynamics. The Coastal Alentejo (Portugal) CLIM 85.2002 Peter ZAPFEL and Matti VAINIO (lv): Pathways to European Greenhouse Gas Emissions Trading History and

MisconceptionsCLIM 86.2002 Pierre COURTOIS: Influence Processes in Climate Change Negotiations: Modelling the RoundsETA 87.2002 Vito FRAGNELLI and Maria Erminia MARINA (lviii): Environmental Pollution Risk and InsuranceETA 88.2002 Laurent FRANCKX(lviii): Environmental Enforcement with Endogenous Ambient MonitoringETA 89.2002 Timo GOESCHL and Timothy M. SWANSON (lviii): Lost Horizons. The noncooperative management of an

evolutionary biological system.ETA 90.2002 Hans KEIDING (lviii): Environmental Effects of Consumption: An Approach Using DEA and Cost SharingETA 91.2002 Wietze LISE(lviii): A Game Model of Peoples Participation in Forest Management in Northern IndiaCLIM 92.2002 Jens HORBACH: Structural Change and Environmental Kuznets CurvesETA 93.2002 Martin P. GROSSKOPF: Towards a More Appropriate Method for Determining the Optimal Scale of Production

UnitsVOL 94.2002 Scott BARRETT and Robert STAVINS: Increasing Participation and Compliance in International Climate Change

AgreementsCLIM 95.2002 Banu BAYRAMOGLU LISE and Wietze LISE: Climate Change, Environmental NGOs and Public Awareness in

the Netherlands: Perceptions and RealityCLIM 96.2002 Matthieu GLACHANT:The Political Economy of Emission Tax Design in Environmental PolicyKNOW 97.2002 Kenn ARIGA and Giorgio BRUNELLO: Are the More Educated Receiving More Training? Evidence from

ThailandETA 98.2002 Gianfranco FORTE and Matteo MANERA: Forecasting Volatility in European Stock Markets with Non-linear

GARCH ModelsETA 99.2002 Geoffrey HEAL: Bundling BiodiversityETA 100.2002 Geoffrey HEAL, Brian WALKER, Simon LEVIN, Kenneth ARROW, Partha DASGUPTA, Gretchen DAILY, Paul

EHRLICH, Karl-Goran MALER, Nils KAUTSKY, Jane LUBCHENCO, Steve SCHNEIDER and DavidSTARRETT: Genetic Diversity and Interdependent Crop Choices in Agriculture

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Growth Model with Environmental ExternalityETA 104.2002 Anna BOTTASSO and Alessandro SEMBENELLI: Does Ownership Affect Firms Efficiency? Panel Data

Evidence on ItalyPRIV 105.2002 Bernardo BORTOLOTTI, Frank DE JONG, Giovanna NICODANO and Ibolya SCHINDELE: Privatization and

Stock Market LiquidityETA 106.2002 Haruo IMAI and Mayumi HORIE(lviii): Pre-Negotiation for an International Emission Reduction GamePRIV 107.2002 Sudeshna GHOSH BANERJEE and Michael C. MUNGER: Move to Markets? An Empirical Analysis of

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PRIV 113.2002 Yannis KATSOULAKOS and Elissavet LIKOYANNI: Fiscal and Other Macroeconomic Effects of PrivatizationPRIV 114.2002 Guillaume GIRMENS: Privatization, International Asset Trade and Financial MarketsPRIV 115.2002 D. Teja FLOTHO: A Note on Consumption Correlations and European Financial IntegrationPRIV 116.2002 Ibolya SCHINDELE and Enrico C. PEROTTI: Pricing Initial Public Offerings in Premature Capital Markets:

The Case of HungaryPRIV 1.2003 Gabriella CHIESA and Giovanna NICODANO: Privatization and Financial Market Development: Theoretical

IssuesPRIV 2.2003 Ibolya SCHINDELE: Theory of Privatization in Eastern Europe: Literature ReviewPRIV 3.2003 Wietze LISE, Claudia KEMFERT and Richard S.J. TOL: Strategic Action in the Liberalised German Electricity

MarketCLIM 4.2003 Laura MARSILIANI and Thomas I. RENSTRM: Environmental Policy and Capital Movements: The Role of

Government CommitmentKNOW 5.2003 Reyer GERLAGH: Induced Technological Change under Technological CompetitionETA 6.2003 Efrem CASTELNUOVO: Squeezing the Interest Rate Smoothing Weight with a Hybrid Expectations ModelSIEV 7.2003 Anna ALBERINI, Alberto LONGO, Stefania TONIN, Francesco TROMBETTA and Margherita TURVANI: The

Role of Liability, Regulation and Economic Incentives in Brownfield Remediation and Redevelopment:

Evidence from Surveys of Developers NRM 8.2003Elissaios PAPYRAKIS and Reyer GERLAGH: Natural Resources: A Blessing or a Curse?CLIM 9.2003 A. CAPARRS, J.-C. PEREAU and T. TAZDAT: North-South Climate Change Negotiations: a Sequential Game

with Asymmetric InformationKNOW 10.2003 Giorgio BRUNELLO and Daniele CHECCHI: School Quality and Family Background in ItalyCLIM 11.2003 Efrem CASTELNUOVO and Marzio GALEOTTI: Learning By Doing vs Learning By Researching in a Model of

Climate Change Policy AnalysisKNOW 12.2003 Carole MAIGNAN, Gianmarco OTTAVIANO and Dino PINELLI (eds.): Economic Growth, Innovation, Cultural

Diversity: What are we all talking about? A critical survey of the state-of-the-art

KNOW 13.2003 Carole MAIGNAN, Gianmarco OTTAVIANO, Dino PINELLI and Francesco RULLANI (lix): Bio-EcologicalDiversity vs. Socio-Economic Diversity. A Comparison of Existing Measures

KNOW 14.2003 Maddy JANSSENS and Chris STEYAERT(lix): Theories of Diversity within Organisation Studies: Debates andFuture Trajectories

KNOW 15.2003 Tuzin BAYCAN LEVENT, Enno MASUREL and Peter NIJKAMP(lix): Diversity in Entrepreneurship: Ethnic andFemale Roles in Urban Economic Life

KNOW 16.2003 Alexandra BITUSIKOVA (lix): Post-Communist City on its Way from Grey to Colourful: The Case Study fromSlovakia

KNOW 17.2003 Billy E. VAUGHN and Katarina MLEKOV(lix): A Stage Model of Developing an Inclusive CommunityKNOW 18.2003 Selma van LONDEN and Arie de RUIJTER (lix): Managing Diversity in a Glocalizing WorldCoalition

Theory

Network

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PRIV 20.2003 Giacomo CALZOLARI and Alessandro PAVAN(lx): Monopoly with ResalePRIV 21.2003 Claudio MEZZETTI (lx): Auction Design with Interdependent Valuations: The Generalized Revelation

Principle, Efficiency, Full Surplus Extraction and Information AcquisitionPRIV 22.2003 Marco LiCalzi and Alessandro PAVAN(lx): Tilting the Supply Schedule to Enhance Competition in Uniform-

Price AuctionsPRIV 23.2003 David ETTINGER (lx): Bidding among Friends and EnemiesPRIV 24.2003 Hannu VARTIAINEN(lx): Auction Design without CommitmentPRIV 25.2003 Matti KELOHARJU, Kjell G. NYBORG and Kristian RYDQVIST(lx): Strategic Behavior and Underpricing in

Uniform Price Auctions: Evidence from Finnish Treasury Auctions

PRIV 26.2003 Christine A. PARLOUR and Uday RAJAN(lx): Rationing in IPOsPRIV 27.2003 Kjell G. NYBORG and Ilya A. STREBULAEV(lx): Multiple Unit Auctions and Short SqueezesPRIV 28.2003 Anders LUNANDER and Jan-Eric NILSSON(lx): Taking the Lab to the Field: Experimental Tests of Alternative

Mechanisms to Procure Multiple ContractsPRIV 29.2003 TangaMcDANIEL and Karsten NEUHOFF(lx): Use of Long-term Auctions for Network InvestmentPRIV 30.2003 Emiel MAASLAND and Sander ONDERSTAL (lx): Auctions with Financial ExternalitiesETA 31.2003 Michael FINUS and Bianca RUNDSHAGEN: A Non-cooperative Foundation of Core-Stability in Positive

Externality NTU-Coalition GamesKNOW 32.2003 Michele MORETTO: Competition and Irreversible Investments under Uncertainty_PRIV 33.2003 Philippe QUIRION: Relative Quotas: Correct Answer to Uncertainty or Case of Regulatory Capture?

KNOW 34.2003 Giuseppe MEDA, Claudio PIGA and Donald SIEGEL: On the Relationship between R&D and Productivity: ATreatment Effect Analysis

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BiotechnologiesETA 43.2003 Giorgio BUSETTI and Matteo MANERA: STAR-GARCH Models for Stock Market Interactions in the Pacific

Basin Region, Japan and USCLIM 44.2003 Katrin MILLOCK and Cline NAUGES: The French Tax on Air Pollution: Some Preliminary Results on its

EffectivenessPRIV 45.2003 Bernardo BORTOLOTTI and Paolo PINOTTI: The Political Economy of PrivatizationSIEV 46.2003 Elbert DIJKGRAAF and Herman R.J. VOLLEBERGH: Burn or Bury? A Social Cost Comparison of Final Waste

Disposal MethodsETA 47.2003 Jens HORBACH: Employment and Innovations in the Environmental Sector: Determinants and Econometrical

Results for GermanyCLIM 48.2003 Lori SNYDER, Nolan MILLER and Robert STAVINS: The Effects of Environmental Regulation on Technology

Diffusion: The Case of Chlorine ManufacturingCLIM 49.2003 Lori SNYDER, Robert STAVINS and Alexander F. WAGNER:Private Options to Use Public Goods. Exploiting

Revealed Preferences to Estimate Environmental BenefitsCTN 50.2003 Lszl . KCZY and Luc LAUWERS(lxi): The Minimal Dominant Set is a Non-Empty Core-Extension

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Formation GameGG 62.2003 Michael FINUS and Bianca RUNDSHAGEN: How the Rules of Coalition Formation Affect Stability of

International Environmental AgreementsSIEV 63.2003 Alberto PETRUCCI: Taxing Land Rent in an Open EconomyCLIM 64.2003 Joseph E. ALDY, Scott BARRETT and Robert N. STAVINS: Thirteen Plus One: A Comparison of

Global Climate Policy ArchitecturesSIEV 65.2003 Edi DEFRANCESCO: The Beginning of Organic Fish Farming in ItalySIEV 66.2003 Klaus CONRAD: Price Competition and Product Differentiation when Consumers Care for the

EnvironmentSIEV 67.2003 Paulo A.L.D. NUNES, Luca ROSSETTO, Arianne DE BLAEIJ: Monetary Value Assessment of Clam

Fishing Management Practices in the Venice Lagoon: Results from a Stated Choice Exercise

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Multicultural Environment: Saint-Petersburgs CaseKNOW 73.2003 Kristine CRANE(lxii): The City as an Arena for the Expression of Multiple Identities in the Age of

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the DilemmasKNOW 79.2003 Giorgio BELLETTINI, Carlotta BERTI CERONI and Gianmarco I.P.OTTAVIANO: Child Labor and

Resistance to Change

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IEM 81.2003 Alessandro LANZA, Matteo MANERA and Massimo GIOVANNINI: Oil and Product Dynamics inInternational Petroleum Markets

(l) This paper was presented at the Workshop Growth, Environmental Policies and Sustainability

organised by the Fondazione Eni Enrico Mattei, Venice, June 1, 2001

(li) This paper was presented at the Fourth Toulouse Conference on Environment and Resource

Economics on Property Rights, Institutions and Management of Environmental and Natural

Resources, organised by Fondazione Eni Enrico Mattei, IDEI and INRA and sponsored by MATE,Toulouse, May 3-4, 2001

(lii) This paper was presented at the International Conference on Economic Valuation of

Environmental Goods, organised by Fondazione Eni Enrico Mattei in cooperation with CORILA,

Venice, May 11, 2001

(liii) This paper was circulated at the International Conference on Climate Policy Do We Need a

New Approach?, jointly organised by Fondazione Eni Enrico Mattei, Stanford University and

Venice International University, Isola di San Servolo, Venice, September 6-8, 2001

(liv) This paper was presented at the Seventh Meeting of the Coalition Theory Network organised by

the Fondazione Eni Enrico Mattei and the CORE, Universit Catholique de Louvain, Venice, Italy,

January 11-12, 2002

(lv) This paper was presented at the First Workshop of the Concerted Action on Tradable Emission

Permits (CATEP) organised by the Fondazione Eni Enrico Mattei, Venice, Italy, December 3-4, 2001

(lvi) This paper was presented at the ESF EURESCO Conference on Environmental Policy in a

Global Economy The International Dimension of Environmental Policy, organised with thecollaboration of the Fondazione Eni Enrico Mattei , Acquafredda di Maratea, October 6-11, 2001

(lvii) This paper was presented at the First Workshop of CFEWE Carbon Flows between Eastern

and Western Europe, organised by the Fondazione Eni Enrico Mattei and Zentrum fur Europaische

Integrationsforschung (ZEI), Milan, July 5-6, 2001

(lviii) This paper was presented at the Workshop on Game Practice and the Environment, jointly

organised by Universit del Piemonte Orientale and Fondazione Eni Enrico Mattei, Alessandria,

April 12-13, 2002

(lix) This paper was presented at the ENGIME Workshop on Mapping Diversity, Leuven, May 16-

17, 2002

(lx) This paper was presented at the EuroConference on Auctions and Market Design: Theory,

Evidence and Applications, organised by the Fondazione Eni Enrico Mattei, Milan, September 26-

28, 2002

(lxi) This paper was presented at the Eighth Meeting of the Coalition Theory Network organised by

the GREQAM, Aix-en-Provence, France, January 24-25, 2003(lxii) This paper was presented at the ENGIME Workshop on Communication across Cultures in

Multicultural Cities, The Hague, November 7-8, 2002


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2002 SERIES

CLIM Climate Change Modelling and Policy (Editor: Marzio Galeotti )

VOL Voluntary and International Agreements (Editor: Carlo Carraro)

SUST Sustainability Indicators and Environmental Valuation(Editor: Carlo Carraro)

NRM Natural Resources Management (Editor: Carlo Giupponi)

KNOW Knowledge, Technology, Human Capital (Editor: Dino Pinelli)

MGMT Corporate Sustainable Management (Editor: Andrea Marsanich)

PRIV Privatisation, Regulation, Antitrust (Editor: Bernardo Bortolotti)

ETA Economic Theory and Applications (Editor: Carlo Carraro)

2003 SERIES

CLIM Climate Change Modelling and Policy (Editor: Marzio Galeotti )

GG Global Governance (Editor: Carlo Carraro)

SIEV Sustainability Indicators and Environmental Valuation(Editor: Anna Alberini)

NRM Natural Resources Management (Editor: Carlo Giupponi)

KNOW Knowledge, Technology, Human Capital (Editor: Gianmarco Ottaviano)

IEM International Energy Markets (Editor: Anil Markandya)

CSRM

Corporate Social Responsibility and Management (Editor: Sabina Ratti)

PRIV Privatisation, Regulation, Antitrust (Editor: Bernardo Bortolotti)

ETA Economic Theory and Applications (Editor: Carlo Carraro)

CTN Coalition Theory Network

Oil and Product Price Dynamics in International Petroleum Markets

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Transcript of Oil and Product Price Dynamics in International Petroleum Markets