07350015%2E2012%2E672290
Journal of Business & Economic Statistics
ISSN: 0735-0015 (Print) 1537-2707 (Online) Journal homepage: http://www.tandfonline.com/loi/ubes20
Price Transmission in the EU Wholesale Petroleum
Markets
Szymon Wlazlowski , Monica Giulietti , Jane Binner & Costas Milas
To cite this article: Szymon Wlazlowski , Monica Giulietti , Jane Binner & Costas Milas (2012)
Price Transmission in the EU Wholesale Petroleum Markets, Journal of Business & Economic
Statistics, 30:2, 165-172, DOI: 10.1080/07350015.2012.672290
To link to this article: http://dx.doi.org/10.1080/07350015.2012.672290
Published online: 24 May 2012.
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Date: 11 January 2016, At: 22:35
Special Section on Measurement Error: On July 9–11, 2007, a conference on the topic of “Measurement Error: Theory and
Practice” was held at Aston Business School in Birmingham, U.K., organized as a joint venture between Aston University and
Lund University in Sweden. Arnold Zellner, the founding editor of JBES, was heavily involved in the organization of this
conference, and was one of three keynote speakers there (the others were William Barnett and Dennis Fixler). This special
section of JBES consists of several papers from this conference.
Price Transmission in the EU Wholesale
Petroleum Markets
Szymon WLAZLOWSKI
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Economics and Strategy Group, Aston University, Aston Triangle, Birmingham B4 7ET, UK
([email protected])
Monica GIULIETTI
Nottingham University Business School, University of Nottingham, Wollaton Road, Nottingham NG8 1BB, UK
(Monica.Giulietti.nottingham.ac.uk)
Jane BINNER
Accounting and Financial Management Division, Management School, University of Sheffield, Sheffield S1 4DT,
UK ([email protected])
Costas MILAS
Management School, University of Liverpool, L69 7ZH, UK and Rimini Centre for Economic Analysis, Rimini, Italy
([email protected])
This article employs nonlinear smooth transition models to analyze the relationship between upstream
and midstream prices of petroleum products. We test for the presence of nonlinearities in price linkages
using both weekly series constructed using official EU procedures and also daily industry series applied
for the first time. Our results show that the estimated shape of the transition function and equilibrium
reversion path depend on the frequency of the price dataset. Our analysis of the crude oil to wholesale
price transmission provides evidence of nonlinearities when prices are observed with daily frequency. The
nature of the nonlinearities provides evidence in support of the existence of menu costs or, more generally,
frictions in the markets rather than supply adjustment costs. This result differs from that found for the U.S.
petroleum markets.
KEY WORDS: European oil markets; Measurement error; Nonlinear models.
1.
INTRODUCTION
The pricing of petroleum products continues to receive significant attention in the applied literature, mainly because public
opinion is concerned with the impact of price spikes in crude oil
markets on downstream markets. High commodity prices, global
financial crisis combined with increasing energy demand from
developing countries, suggest that the issues of price transmission in oil markets will continue to attract significant attention
both from the public and the academic community.
This article investigates the process of price transmission in
the EU oil markets in order to provide evidence about the sign
and speed of price adjustments in vertically related markets
when prices have moved away from their long-run equilibrium
relationship. More precisely, our empirical analysis provides
support for one of the alternative views about the economic
drivers of price adjustments, namely the view that market frictions as opposed to supply adjustment costs cause prices to
adjust in disequilibrium. As these alternative views have different policy implications in terms of potential anticompetitive
behavior, our analysis offers important insights into the pricing
strategies of oil companies.
Applied researchers have developed a number of econometric
methods to test for the presence of nonlinearities in price transmission, initially based on the error correction models (ECM)
by Engle and Granger (1987) and Stock and Watson (1993)
augmented by splitting the short-run variables according to the
direction of price changes. More recently, the differences in price
responses to cost increases and decreases have been modeled
using threshold autoregressive models (TAR) by Tong (1978)
and Tong and Lim (1980) which makes it possible to estimate
two or more separate pricing regimes under the assumption that
the regime switch is instantaneous–see Wlazlowski (2008) for
a summary of the key studies in the area.
165
© 2012 American Statistical Association
Journal of Business & Economic Statistics
April 2012, Vol. 30, No. 2
DOI: 10.1080/07350015.2012.672290
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166
This article analyzes the impact of data frequency on nonlinear models of price transmission based on smooth transition autoregressive models (STAR), using a unique commercial
high-frequency dataset.
The general specification of the price transmission process in
our article allows us to investigate both the sign and the speed
of the adjustment between regimes. This makes it feasible to
contrast the possibility of a uniform speed of adjustment, irrespective of the distance from the equilibrium, with one where
the speed of adjustment depends on the distance from equilibrium. While the former is usually associated with markets
characterized by supply/inventory adjustment costs, the latter is
typical of markets where menu/transaction costs are associated
with changing the terms of supply contracts [see Besanko, Dranove, Shanley, and Schaefer (2009)], especially in the absence
of a full range of future markets which can help suppliers mitigate the impact of cost shocks—as discussed by Borenstein and
Shepard (2002) and Alm, Sennoga, and Skidmore (2005).
The price transmission between the crude oil and wholesale
markets was investigated by Shin (1992) using the cointegration
framework and a high-frequency dataset. He found that both
the estimation framework and the data frequency affect the
results on nonlinearities in price transmission. More recently,
Bachmeier and Griffin (2003) analyzed the price transmission
between the crude oil and wholesale markets in the U.S.
and between wholesale and retail market using daily and
weekly data. They found some evidence for rejecting the null
hypothesis of linear transmission in the daily dataset, but not
for the weekly one.
Bettendorf, der Geest, and Varkevisser (2003) analyzed the
tax incidence on the transmission between daily spot wholesale
and retail prices over the period January 1996 to December
2001 in the Netherlands, using an error correction model. The
results indicate that, although nonlinearities are negligible, the
characteristics of price transmission vary across datasets with
different frequency.
While most researchers agree that with lower frequency the
null hypothesis of linear transmission is underrejected, CramonTaubadel and Meyer (2001) argued that if the data is too aggregated over time, in order to use nonlinear estimation techniques,
the researchers have to widen the time coverage of the research.
This, however, increases the probability of structural changes
occurring in the pricing relationship over the time period considered. The Monte Carlo experiments reported by CramonTaubadel and Meyer (2001) indicate that the size of nonlinearity tests surpasses the traditional levels, leading to overrejection
of the null hypothesis of symmetric price response. Paya and
Peel (2006) used a similar approach to ours to assess the impact of temporal aggregation/averaging on unit root testing but
in a different context, with an application to purchasing power
parity (PPP). Their main findings are that “(exponential) STAR
non-linearities are generally preserved in the temporally aggregated data, through the lag structure changes, and that the
implied speed of adjustment to shocks declines the more aggregated the data”(p. 666). We analyze similar issues but in the
context of price transmission and using a framework that allows
for nonlinearities of a more general nature (i.e., exponential or
logistic).
The main contribution of our article is to use recently developed tests for the presence of nonlinearities in the price trans-
Journal of Business & Economic Statistics, April 2012
mission process between the crude oil and wholesale markets,
and to compare the outcome of the testing procedure under different temporal aggregation of the time series for the relevant
prices, therefore identifying both the correct econometric framework and data frequency required to investigate nonlinearities
in price transmission.
As far as the econometric specification is concerned, we employ the nonlinear apparatus developed by Teräsvirta (1994)
and Escribano and Jordá (2001). Since these nonlinear models
depend on the contemporaneous markets, rather than the long
price history, they should not be affected by the omitted dynamics and nonlinearities in long-run adjustment—see Geweke
(2004) for a discussion of this issue.
The evidence for the U.S. wholesale markets seems to support
the view that supply adjustment costs explain the observed price
rigidities. The European markets investigated in our work, however, are more likely to be subject to market frictions which make
it costly to adjust prices when close to the equilibrium level but
necessary to adjust quickly when the prices move away from
equilibrium. This type of behavior is often observed in international markets where incomplete exchange rate passthrough
takes place. Although the fuels investigated in this work are all
quoted in U.S. dollars, the European suppliers in these markets
operate with different local currencies, which can increase the
market frictions and opportunities for arbitrage, making the European markets less integrated and efficient than the U.S. ones.
The major contribution to knowledge from this work is the finding that in the majority of cases analyzed price adjustment to
exogenous shocks is characterized by slow responses to small
disequilibria and faster responses to large disequilibria. The nature of the nonlinearities found in this work provides evidence in
support of the existence of menu costs, or more generally, frictions in the markets rather than supply adjustment costs. These
results are different from the results found for the U.S. petroleum
markets, for example, in Borenstein and Shepard (2002).
2.
DATA
The data used for our empirical analysis involve two sets of
net-of-taxes price series (ordered according to the place in the
supply chain):
• USD prices of Brent crude oil (denoted upstream prices)
which was found to be the price leading crude oil for the
European Union area—see Hagströmer, Wlazlowski, and
Giulietti (2010);
• USD wholesale prices from the Amsterdam–Rotterdam–
Antwerp (ARA) area which represent the spot prices in the
EU and are denoted midstream.
The price series cover the period June 1994 to November
2006 (diesel oil) and January 1994 to November 2006 (all remaining products). Different data coverage is due to the change
in methodology of gathering prices for diesel fuel related to strict
environmental policies introduced in 1994. Following the convention in the industry, our preliminary analysis also involved an
additional kind of fuel—liquefied petroleum gas (LPG). The results, however, are not reported here as LPG differs significantly
from other products analyzed in this article. Most importantly
this is the only product that might be obtained from sources
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Wlazlowski et al.: Petroleum Markets
167
other than crude oil. Worldwide, about 40% of the LPG is produced in crude oil refining and 60% is produced during crude oil
and natural gas extraction—see Hekkert, Hendriks, Faaij, and
Neelis (2005). Those two sources differ significantly in terms
of production technology—the associated gas does not have
to be processed unlike crude oil—and economic properties—
transporting LPG from the extraction site to the consumer is less
efficient than crude oil transportation to the refiner. Therefore,
although crude oil remains the main source of LPG, its pricing
mechanism is unique in some aspects. Our results confirmed
this with estimates for LPG differing from those for remaining
products. Results for LPG are available upon request.
All series are expressed in logarithms in order to avoid
problems with nonlinear trends in the data. Also, we represent
the pricing mechanism as a Cobb–Douglas function which
allows us to incorporate the effects of the exchange rate for
cases when the transmission process involves tiers denominated
in different currencies.
Furthermore, we have market data provided by the
practitioners—the midstream prices were provided by Platt’s,
a leading industry consultancy and price data provider. Both
upstream and midstream prices are heavily disaggregated in
terms of product and geographical coverage and therefore are
typically implemented in the so-called price-formulas used
in the over-the-counter transactions worldwide—see Claessens
and Varangis (1995) and Bacon and Kojima (2006). As such,
both series provide a reliable indicator of the spot market in
Europe. To analyze all time series, we pair upstream prices with
the appropriate midstream prices quoted on the same (or earliest available) date. This is the common market approach, as the
lead–lag times cannot be specified in advance.
Since for the upstream–midstream transmission, both original
daily data and the constructed weekly data are available, we
apply the nonlinear framework to both samples and compare
the results. We focus on the comparison of results obtained
from both datasets—a detailed STAR analysis of wholesaleto-retail transmission in Europe can be found in, for example,
Wlazlowski, Binner, Giulietti, and Milas (2009).
3.
3.1.
APPLIED ANALYSIS
Analysis of Frequency of Adjustments
Using the standard Augmented Dickey-Fuller (ADF) tests,
all series are found to be integrated of order one. It is assumed
that the price shocks emanate from the larger, more liquid market where the trading volume is concentrated—for example,
Adrangi, Chatrath, Raffiee, and Ripple (2001). The relationship between the prices was verified using the ADF-type test
proposed by Phillips and Ouliaris (1990). In most cases that
we analyzed, the null hypothesis of a spurious relationship was
rejected and the midstream prices were found to revert to the
equilibrium set by upstream prices.
As the next step, for every product–country–transmission tier,
we analyze the dynamics of the adjustments in the symmetric
case using the following model (for simplicity, the exchange
rate is not presented):
p
yt = α + ωxt +
l=1
ψl zt−l + δ0 ût−1 + νt ,
(1)
Table 1. Midstream response to upstream changes—half-life and
90% decay times (in weeks)
Weekly data
Periods
Unleaded
Diesel
Heating
L/S refined
H/S refined
Leaded
Half-life
0.73 (0.52)
0.89 (0.51)
0.81 (0.53)
1.04 (0.37)
0.75 (0.29)
0.75 (0.35)
90% decay
4.58 (1.95)
2.80 (1.33)
6.67 (2.29)
4.84 (2.12)
2.67 (1.23)
4.67 (2.44)
Daily data
Periods
Unleaded
Diesel
Heating
L/S refined
H/S refined
Leaded
Half-life
0.81 (0.43)
0.75 (0.36)
0.89 (0.45)
0.62 (0.29)
0.78 (0.34)
0.85 (0.32)
90% decay
1.98 (0.89)
1.66 (0.55)
2.06 (1.01)
1.84 (0.88)
1.92 (0.55)
2.03 (1.03)
NOTE: Numbers in parentheses are standard errors.
where yt are the midstream prices, xt are the corresponding
upstream prices, zt = (yt , xt ) are the price dynamics, ût
are the residuals from the level equations (yt = δ0 + δ1 xt + ut ),
the sign of which determines whether the sellers’ margins are
squeezed (negative disequilibria) or rather artificially inflated
(positive ones). Using the estimates, we calculate the half-life
and 90% decay of upstream shocks, which proxy the adjustment
speeds. The results for daily data are divided by 5 (the number
of pricing days a week) to achieve comparability with weekly
data.
Table 1 presents the results (half-lives and 90% decays together with standard errors obtained from bootstrapping the
residuals of the estimated models with 200 repetitions). The
most important finding from this analysis is that the estimates of
the time required for the adjustment in upstream-to-midstream
transmission are lower than the frequency unit (one week). On
the basis of this result, we undertake the analysis of nonlinearities in price transmission based on the daily data on upstream
and midstream prices.
3.2.
Testing for the Presence of Nonlinearities
The analysis of the symmetric ECM indicates that the price
transmission between upstream and midstream tiers is best
captured with the use of daily data. Nonlinearities in the price
transmission can be conveniently captured by the following
simplification of Equation (1) which allows for change in
the adjustment speed, the value of which is governed by the
smooth transition function G((ût−d , ζ, c) bounded between
0 and 1:
m
ût = [1 − G(ût−d , ζ, c)] δ0L ût−1 +
δiL ût−i
i=1
+ G(ût−d , ζ, c) δ0H ût−1 +
m
i=1
δiH ût−i + νt , (2)
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168
Journal of Business & Economic Statistics, April 2012
Figure 1. Comparison of nonlinear models.
where the d is the delay parameter, which determines how
responsive the adjustment is to lagged disequilibria and margin
changes. In our models, we set d based on the grid search, so
as to minimize the residual sum of squares from the model, in
line with the approach of Hansen (1996, 1997). Depending on
the specification of the transition function, different patterns
of adjustment could be analyzed. Following, for example, van
Dijk, Teräsvirta, and Franses (2002) and Lundbergh, Terasvirta,
and Van Dijk (2003), we consider two most common functions,
that is:
2
• exponential function G(ût−d , ζ, c) = 1 − e−ζ (ût−d −c) ;
• logistic function G(ût−d , ζ, c) = 1+e−ζ 1(ût−d −c) ,
where ζ is the smoothness parameter, which determines the
smoothness in the switch from one adjustment regime to the
other—the closer it is to zero the smoother the transition is.
When the value of the parameter approaches ∞, the logistic function has a sudden switch and the exponential function becomes linear. The centering parameter c determines the
position of the transition function relative to 0. The presence
of a smooth transition between two regimes is the defining feature of STAR models, while the previously used SETAR models
(which also comprise two regimes) assume a sudden and full
switch between L(ow) and H(igh) regimes, G = 1 ⇔ ût−d > c.
Figure 1 presents the different adjustments for ESTAR, LSTAR,
and SETAR models. For simplicity, it is assumed that the threshold parameters (c’s) are the same across models and all equal
zero, so that the SETAR/LSTAR regimes are symmetric around
zero and the ESTAR adjustment is symmetric with respect to
the distance from zero.
The logistic function gives rise to a model which has different
adjustment speeds for negative and positive residuals, while the
exponential function involves the same adjustments for extreme
positive or negative residuals, but different adjustment for
small and extreme values (left panel). These two regimes are
denoted H and L since when G(·) = 0 the adjustment is equal
to δ0L , while when G(·) = 1 the adjustment is δ0H . The other δ H
and δ L parameters describe short-run dynamics affecting the
adjustment process in both models.
We attempted to estimate an even more general model in
which adjustment depends on both the size (small or large) and
sign (positive or negative) of the residual. We were unable to
obtain convergent estimates of this model (even imposing a
common threshold and smoothness parameter). Thus, we only
focus our attention on estimates of Equation (1) using either
the exponential or logistic function.
Because of the extra parameters in the transition function,
the direct tests for the nonlinear model given by (2) against the
one specified by (1) are not possible—see the discussion on
the identification problem in Davies (1987). However, one can
modify (2) and estimate a simplified model with the Taylor expansion of the transition function around c and the assumption
of d = 1 (so that (2) resembles a cointegrating Dickey–Fuller
equation):
Tn (f (x)) =
∞
f (n) (a)
n=0
n!
(x − a)n ,
Tn (G(·))|a = G(a) + G′ (a)(x − a) +
+
G′′ (a)
(x − a)2
2
G(3) (a)
(x − a)3 + · · · ·
3!
We rewrite (2), so that it becomes:
ût = δ0L ût−1 +
m
i=1
+
m
i=1
δiL ût−i + G(ût−d , ζ ) δ0H − δ0L ût−1
H
δi − δiL ût−i + νt
(3)
and replace the transition functions with their fourth order Taylor
expansions around a = 0. Following the procedure suggested by
Wlazlowski et al.: Petroleum Markets
169
Table 2. Comparison of results for weekly and daily data
Product
ULP
Diesel
Heating oil
L/S refined
H/S refined
Leaded petrol
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ULP
Diesel
Heating oil
L/S refined
H/S refined
Leaded petrol
H0
1.35 (0.214)
2.73 (0.0058)
1.58 (0.1262)
0.60 (0.7735)
0.99 (0.4379)
0.50 (0.8532)
8.87 (1.1 × 10−21)
5.28 (3.9 × 10−11
5.37 (2.3 × 10−20 )
3.88 (1.3 × 10−8 )
4.56 (2.3 × 10−7 )
13.89 (1.9 × 10−28 )
H0E
End result
H0L
Weekly data
–
4.58 (0.0011)
–
–
–
–
–
3.83 (0.0043)
–
–
–
–
Linear
ESTAR
Linear
Linear
Linear
Linear
Daily data
5.58 (4.9 × 10−7 )
6.90 (4.9 × 10−9 )
3.06 (3.8 × 10−5 )
2.75 (0.0021)
0.37 (0.8977)
6.28 (1.4 × 10−6 )
2.12 (0.0306)
4.45 (2.3 × 10−5 )
3.35 (6.9 × 10−6 )
5.29 (9.0 × 10−8 )
8.80 (1.6 × 10−9 )
6.78 (3.8 × 10−7 )
LSTAR
LSTAR
ESTAR
ESTAR
ESTAR
ESTAR
The table reports tests for the hypotheses H0 : ζ5 = ζ4 = ζ3 = ζ2 = 0, H0L : ζ5 = ζ3 = 0 and H0E : ζ4 = ζ2 = 0, as discussed in the main text.
Escribano and Jordá (2001), we then estimate:
ût = ζ0 + ζ1′ ∗ Xt + ζ2′ ∗ (Xt ∗ ût−d ) + ζ3′ ∗ Xt ∗ û2t−d
+ ζ4′ ∗ Xt ∗ û3t−d + ζ5′ ∗ Xt ∗ û4t−d + νt,
(4)
where Xt = (ût−1 , ût−1 , . . . , ût−m ), and perform the following step-by-step testing algorithm:
1. test H0 : ζ5 = ζ4 = ζ3 = ζ2 = 0—if rejected proceed, if not
then conclude that no nonlinearities were found;
2. test H0L : ζ5 = ζ3 = 0 with the help of an F-test denoted FL ;
3. test H0E : ζ4 = ζ2 = 0 with the help of an F-test denoted FE ;
4. if the minimum p-value corresponds to FL select LSTAR,
otherwise select ESTAR.
The first step comprises standard tests for the null hypothesis
of linearity, while the remaining ones test for the shape of the
transition function.
For weekly data, the results reported in Table 2 indicate the
presence of nonlinearities only for diesel oil. Indeed, the null
hypothesis of linearity (H0 : ζ5 = ζ4 = ζ3 = ζ2 = 0) is rejected
only in this case, where the ESTAR model is chosen based on
the minimum p-value delivered for the H0L : ζ5 = ζ3 = 0 hypothesis. For daily data, instead, nonlinearities are present in all
transmissions as the null hypothesis of linearity indicates that
the presence of nonlinearities (H0 : ζ5 = ζ4 = ζ3 = ζ2 = 0) is
rejected in all cases. A comparison of the p-values associated
with the H0L : ζ5 = ζ3 = 0 and H0E : ζ4 = ζ2 = 0 hypotheses
suggests an LSTAR model for unleaded petrol and diesel and an
ESTAR model for all other transmissions. For H/S Refined, linearity is strongly rejected in favor of an ESTAR specification; in
this case, there is very strong evidence against an LSTAR model.
Our results indicate that the size of the price change (absolute
size of the disequilibria that affect the exponential transmission
function) is as important as the sign of the price change in the
price transmission process. In other words, the possibility of a
slow downward price adjustment following price decreases upstream (as modeled in the LSTAR framework) is not supported
by our results. Instead, we find a more natural distinction between responses to small (positive and negative) disequilibria
as opposed to the bigger ones (as modeled in the ESTAR framework). With respect to the differences between daily and weekly
datasets, our results indicate that when using weekly data, the
null of linear price transmission is rejected only in two cases out
of seven, while for daily data the same hypothesis is rejected
in all cases. By using higher frequency data (daily and weekly
as opposed to weekly and monthly), we shorten the time coverage and thus avoid problems related to stability of the pricing
relationship and their impact on testing for the presence of nonlinearities, as discussed by Cramon-Taubadel and Meyer (2001).
For both exponential and logistic nonlinearities, the smooth
transition between pricing regimes indicates that the changes
in the pricing process are gradual rather than sudden and full,
as assumed in the SETAR models usually employed for that
purpose. This is illustrated by the results presented in Table A.1
in the Appendix, where the estimated values of the smoothness
parameters (ζ ) vary between approximately 1.9 and 20. Further
to nonlinear tests which favor ESTAR over LSTAR models, we
have attempted a direct comparison of both models based on regression standard errors and adjusted R 2 ’s. These statistical tests
(available on request) confirm the superiority of ESTAR models
in terms of lower standard errors and higher adjusted R 2 ’s.
There are a number of possible explanations for the visible
pattern of underrejection of the null of symmetry for the weekly
data. Apart from the arguments suggested by Geweke (1978) and
Blank and Schmiesing (1990), a reasonable explanation is that
the power of the test increases with the use of a larger sample
size in the form of daily as opposed to weekly observations.
Therefore, the explanation suggesting that excessive temporal
aggregation might obscure the actual price pairs looks more
plausible. Although solving this puzzle is beyond the scope of
this article, possible research into this issue might involve Monte
Carlo studies similar to those used by Paya and Peel (2006) for
the ESTAR case.
3.3.
Extent of Nonlinearities
Using the daily dataset for the transmissions identified as nonlinear, we estimated the STAR ECM models given by Equation
(3). The lag structure was chosen so as to maximize the AIC
criteria in the linear case.
The results for the nonlinear estimation are presented in detail
in Table A.1. The values of the coefficients on the lagged disequilibria indicate that for LSTAR models the prices adjust both
170
Journal of Business & Economic Statistics, April 2012
Table 3. Adjustment in linear models
Daily data
Product
Unleaded petrol
Diesel
Heating oil
L/S refined
R/S refined
Leaded petrol
Half-life
2.29 (2.36)
1.29 (1.89)
0.57 (5.91)
4.85 (2.30)
3.44 (1.78)
2.79 (2.12)
90% Decay
13.37 (3.97)
8.81 (2.64)
12.15 (4.74)
19.83 (2.80)
16.62 (2.85)
14.94 (3.67)
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NOTE: Numbers in parentheses are standard errors.
to cost increases and decreases while for the ESTAR models the
prices adjust to significant cost changes and exhibit sluggishness
following small cost changes. The values of the smoothing parameter are significantly higher for LSTAR models (more than
10 standard deviations of the disequilibria) compared to ESTAR
models (less than 5 standard deviations). This suggests that the
switch in LSTAR models is more abrupt, while the transition
between regimes in ESTAR models is smooth. The parameters
c are expressed as a percentile of disequilibria centered around
their respective medians.
The estimates from the nonlinear models were used to calculate the way in which disequilibria are eliminated in the
daily data. The values of decays of upstream shocks (half-lives
and 90% decays) are summarized in Tables 3 and 4, together
with scaled standard deviations obtained from bootstrapping
the residuals from nonlinear estimation in 200 repetitions. Our
models, which include long and variable lags of ût , are free
of autocorrelation based on the Breusch–Godfrey LM test (see,
e.g., Table A.1, where we consider autocorrelation up to order 20). For this reason, autocorrelation is not considered when
bootstrapping the errors of the models. In addition, residuals
are not pooled across products since we examine the path of
adjustment for each product separately.
The presence of ESTAR-type nonlinearities indicates that
when analyzing price transmission using higher frequency data,
the picture of nonlinearities in price responses reveals faster
changes than identified in the previous literature. Instead of
a two-regime, welfare decreasing pricing behavior, the results
of our analysis suggest a more intricate pricing behavior, with
sluggish responses to small cost changes (as indicated by the
presence of ESTAR-type nonlinearities), rich dynamics (as indicated by the lag structure), and 50% adjustment lasting less
than a month.
Another key feature of our ESTAR models based on daily
data is the difference in adjustment speeds for small and large
disequilibria. We consider simulated adjustments to two standard deviations shocks within L and H regimes of ESTAR models based on bootstrapping techniques and 1,000 draws with
replacement. We find that the adjustment is faster for large disequilibria and slower for small disequilibria. More specifically,
for leaded petrol and high-sulfur oil, we find a statistically significant difference in adjustment rates between large and small
disequilibria which peaks at just under 0.10 percentage points of
margin after 50 days before converging to 0.02 percentage points
after 150 days. For heating oil, we find a statistically significant
difference in adjustment between large and small disequilibria of up to 0.13 percentage points which persists beyond 150
days. For high-sulfur oil and diesel, the difference, in the opposite direction to the other fuels, peaks at about 0.03 percentage
points after 10 days but is completely eliminated after 50 days.
In summary, we find that the adjustment is faster for large and
slower for small disequilibria. This is evidence to suggest that,
as discussed earlier, the fuels investigated in this work are all
quoted in U.S. dollars, but the European suppliers in these markets operate with different local currencies which can increase
the market frictions and opportunities for arbitrage. This will
make the European markets less integrated and efficient than the
U.S. ones.
4.
CONCLUSIONS
In this article, we analyze nonlinearities in the transmission
of petroleum product prices in Europe. The analysis of the transmission speed summarized in Table 1 indicates that the traditional analysis based on weekly data might not be appropriate for
the upstream-to-midstream transmission. We revisit this transmission link with daily data to find that the nonlinearities are not
detected in a low-frequency sample. The use of flexible nonlinear models and high-frequency data, on the other hand, allows
us to find evidence of significant nonlinearities in the crude oil
to wholesale price transmission. This result differs from previous studies which use lower frequency observations and simpler
models to test for nonlinearities [e.g., Hosken, McMillan, and
Taylor (2008) for U.S. using weekly data and Rao and Rao
(2005) also for U.S. but with monthly data].
Our results build on those obtained by other researchers who
compared weekly and monthly data [e.g., Bachmeier and Griffin (2003) and Bettendorf et al. (2003)], and they add credence
Table 4. Adjustment in nonlinear models
Daily data
Positive
Unleaded petrol
Diesel
Heating oil
L/S refined
H/S refined
Leaded petrol
Half-life
2.9 (2.59)
1.44 (1.17)
0.57 (5.24)
3.99 (3.23)
3.46 (1.86)
2.78 (2.63)
NOTE: Numbers in parentheses are standard errors.
NA not available, denotes when in-regime adjustment does not reach 10% threshold.
Negative
90% decay
12.18 (5.46)
6.64 (2.08)
11.92 (4.86)
19.78 (4.02)
10.18 (5.03)
15.93 (4.67)
Half-life
3.30 (0.84)
3.37 (1.15)
5.11 (147.28)
6.14 (1.83)
5.01 (0.05)
4.91 (0.12)
90% decay
7.35 (4.59)
7.66 (6.89)
NA
17.06 (3.35)
8.41 (0.32)
9.17 (0.48)
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Wlazlowski et al.: Petroleum Markets
171
to the view that higher frequency of the data is necessary to
understand the mechanics of the price transmission and weekly
data might limit the ability to identify nonlinearities in price
transmission at the upstream level of the oil supply chain. More
importantly, our results contribute to the existing literature by
providing evidence of the widespread presence of ESTAR-type
nonlinearities which could be attributed to the presence of transaction costs and frictions in price transmission, rather than to
outcomes of collusive behavior in a different context, as argued by Borenstein, Cameron, and Gilbert (1997) and Peltzman
(2000). This result is important because it highlights the effects
of market frictions on prices which have not previously been
identified in studies of U.S. markets.
Our work can be extended in several directions. Most importantly, the impact of intertemporal data aggregation should be
verified using Monte Carlo simulations. Similarly, the results of
the simulation of price responses should be combined with additional information on buyer–seller interaction (such as volume
and frequency of transactions) to assess whether the identified
nonlinearities should be attributed to the use of higher data frequency or represent an inherent feature of upstream petroleum
markets. This could shed more light on the issue of nonlinearities in transmission and contribute toward a more rigorous
explanation of this phenomenon.
APPENDIX
Table A.1. Nonlinear ECM (daily data)
Estimate
Std. error
ûLt−1
ûLt−1
ûLt−2
ûLt−3
ûLt−16
ûH
t−1
ûH
t−1
ûH
t−2
ûH
t−3
ûH
t−16
ζ
c
LM20
Unleaded petrol
−0.052
−0.325
−0.146
−0.027
0.030
−0.035
−0.185
−0.090
−0.065
0.017
13.000
0.249
16.817
0.010
0.024
0.025
0.022
0.022
0.009
0.032
0.035
0.034
0.031
–
0.024
0.665
ûLt−1
ûLt−1
ûLt−2
ûLt−3
ûLt−8
ûLt−19
ûH
t−1
ûH
t−1
ûH
t−2
ûH
t−3
ûH
t−8
ûH
t−19
ζ
c
LM20
Diesel
−0.046
−0.439
−0.223
−0.109
0.069
−0.022
−0.081
−0.219
−0.113
0.025
0.001
−0.047
19.999
0.009
17.284
0.012
0.029
0.032
0.030
0.024
0.027
0.011
0.027
0.026
0.025
0.025
0.023
–
0.017
0.634
Table A.1. (Continued)
Estimate
Std. error
ûLt−1
ûLt−1
ûLt−2
ûLt−3
ûLt−4
ûLt−5
ûLt−6
ûLt−7
ûLt−19
ûH
t−1
ûH
t−1
ûH
t−2
ûH
t−3
ûH
t−4
ûH
t−5
ûH
t−6
ûH
t−7
ûH
t−19
ζ
c
LM20
Heating oil
0.007
−0.386
−0.092
−0.178
0.097
−0.076
−0.011
−0.022
−0.061
−0.053
−0.351
−0.224
−0.099
−0.084
−0.044
−0.047
−0.031
−0.020
5.012
−0.153
13.648
0.104
0.101
0.134
0.088
0.126
0.188
0.185
0.103
0.078
0.009
0.021
0.022
0.023
0.027
0.025
0.022
0.020
0.018
6.095
0.037
0.848
ûLt−1
ûLt−1
ûLt−2
ûLt−3
ûLt−13
ûLt−14
ûLt−19
ûH
t−1
ûH
t−1
ûH
t−2
ûH
t−3
ûH
t−13
ûH
t−14
ûH
t−19
ζ
c
LM20
Refined-oil-low sulphur
−0.024
−0.131
−0.059
0.114
0.001
0.034
−0.087
−0.024
−0.249
−0.073
0.002
0.037
0.025
−0.003
1.899
0.148
20.581
0.020
0.039
0.038
0.036
0.037
0.040
0.038
0.005
0.021
0.023
0.024
0.024
0.023
0.022
–
0.044
0.422
ûLt−1
ûLt−1
ûLt−2
ûLt−4
ûLt−19
ûH
t−1
ûH
t−1
ûH
t−2
ûH
t−4
ûH
t−19
ζ
c
LM20
Refined-oil-high sulphur
−0.183
−0.300
−0.069
−0.047
−0.089
−0.033
−0.173
–0.085
–0.036
–0.038
4.501
–0.031
16.130
0.151
0.066
0.067
0.061
0.058
0.005
0.021
0.021
0.020
0.020
1.240
0.037
0.709
ûLt−1
ûLt−1
ûLt−2
ûLt−16
ûH
t−1
Leaded petrol
–0.138
–0.039
0.043
0.075
–0.033
0.156
0.052
0.050
0.045
0.006
172
Journal of Business & Economic Statistics, April 2012
Table A.1. (Continued)
Estimate
ûH
t−1
ûH
t−2
ûH
t−16
ζ
c
LM20
Leaded petrol
–0.313
–0.145
0.020
7.128
–0.026
14.902
Std. error
0.020
0.021
0.020
–
0.021
0.782
NOTE: Entries marked with — refer to a situation when the values could not be calculated—
see, for example, Franses and van Dijk (2000). LM20 figures refer to Breusch–Godfrey serial
correlation F-test statistics (of order 20) together with corresponding p-values.
Downloaded by [Universitas Maritim Raja Ali Haji] at 22:35 11 January 2016
ACKNOWLEDGMENTS
We want to thank the journal’s editor and associate editor in
addition to the two anonymous reviewers for their most useful
comments and suggestions on an earlier version of the article.
The authors also wish to acknowledge the help of Jeremy Smith
of the Department of Economics, University of Warwick, UK
in commenting on an early draft of this article.
[Received November 2007. Revised January 2011.]
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ISSN: 0735-0015 (Print) 1537-2707 (Online) Journal homepage: http://www.tandfonline.com/loi/ubes20
Price Transmission in the EU Wholesale Petroleum
Markets
Szymon Wlazlowski , Monica Giulietti , Jane Binner & Costas Milas
To cite this article: Szymon Wlazlowski , Monica Giulietti , Jane Binner & Costas Milas (2012)
Price Transmission in the EU Wholesale Petroleum Markets, Journal of Business & Economic
Statistics, 30:2, 165-172, DOI: 10.1080/07350015.2012.672290
To link to this article: http://dx.doi.org/10.1080/07350015.2012.672290
Published online: 24 May 2012.
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Download by: [Universitas Maritim Raja Ali Haji]
Date: 11 January 2016, At: 22:35
Special Section on Measurement Error: On July 9–11, 2007, a conference on the topic of “Measurement Error: Theory and
Practice” was held at Aston Business School in Birmingham, U.K., organized as a joint venture between Aston University and
Lund University in Sweden. Arnold Zellner, the founding editor of JBES, was heavily involved in the organization of this
conference, and was one of three keynote speakers there (the others were William Barnett and Dennis Fixler). This special
section of JBES consists of several papers from this conference.
Price Transmission in the EU Wholesale
Petroleum Markets
Szymon WLAZLOWSKI
Downloaded by [Universitas Maritim Raja Ali Haji] at 22:35 11 January 2016
Economics and Strategy Group, Aston University, Aston Triangle, Birmingham B4 7ET, UK
([email protected])
Monica GIULIETTI
Nottingham University Business School, University of Nottingham, Wollaton Road, Nottingham NG8 1BB, UK
(Monica.Giulietti.nottingham.ac.uk)
Jane BINNER
Accounting and Financial Management Division, Management School, University of Sheffield, Sheffield S1 4DT,
UK ([email protected])
Costas MILAS
Management School, University of Liverpool, L69 7ZH, UK and Rimini Centre for Economic Analysis, Rimini, Italy
([email protected])
This article employs nonlinear smooth transition models to analyze the relationship between upstream
and midstream prices of petroleum products. We test for the presence of nonlinearities in price linkages
using both weekly series constructed using official EU procedures and also daily industry series applied
for the first time. Our results show that the estimated shape of the transition function and equilibrium
reversion path depend on the frequency of the price dataset. Our analysis of the crude oil to wholesale
price transmission provides evidence of nonlinearities when prices are observed with daily frequency. The
nature of the nonlinearities provides evidence in support of the existence of menu costs or, more generally,
frictions in the markets rather than supply adjustment costs. This result differs from that found for the U.S.
petroleum markets.
KEY WORDS: European oil markets; Measurement error; Nonlinear models.
1.
INTRODUCTION
The pricing of petroleum products continues to receive significant attention in the applied literature, mainly because public
opinion is concerned with the impact of price spikes in crude oil
markets on downstream markets. High commodity prices, global
financial crisis combined with increasing energy demand from
developing countries, suggest that the issues of price transmission in oil markets will continue to attract significant attention
both from the public and the academic community.
This article investigates the process of price transmission in
the EU oil markets in order to provide evidence about the sign
and speed of price adjustments in vertically related markets
when prices have moved away from their long-run equilibrium
relationship. More precisely, our empirical analysis provides
support for one of the alternative views about the economic
drivers of price adjustments, namely the view that market frictions as opposed to supply adjustment costs cause prices to
adjust in disequilibrium. As these alternative views have different policy implications in terms of potential anticompetitive
behavior, our analysis offers important insights into the pricing
strategies of oil companies.
Applied researchers have developed a number of econometric
methods to test for the presence of nonlinearities in price transmission, initially based on the error correction models (ECM)
by Engle and Granger (1987) and Stock and Watson (1993)
augmented by splitting the short-run variables according to the
direction of price changes. More recently, the differences in price
responses to cost increases and decreases have been modeled
using threshold autoregressive models (TAR) by Tong (1978)
and Tong and Lim (1980) which makes it possible to estimate
two or more separate pricing regimes under the assumption that
the regime switch is instantaneous–see Wlazlowski (2008) for
a summary of the key studies in the area.
165
© 2012 American Statistical Association
Journal of Business & Economic Statistics
April 2012, Vol. 30, No. 2
DOI: 10.1080/07350015.2012.672290
Downloaded by [Universitas Maritim Raja Ali Haji] at 22:35 11 January 2016
166
This article analyzes the impact of data frequency on nonlinear models of price transmission based on smooth transition autoregressive models (STAR), using a unique commercial
high-frequency dataset.
The general specification of the price transmission process in
our article allows us to investigate both the sign and the speed
of the adjustment between regimes. This makes it feasible to
contrast the possibility of a uniform speed of adjustment, irrespective of the distance from the equilibrium, with one where
the speed of adjustment depends on the distance from equilibrium. While the former is usually associated with markets
characterized by supply/inventory adjustment costs, the latter is
typical of markets where menu/transaction costs are associated
with changing the terms of supply contracts [see Besanko, Dranove, Shanley, and Schaefer (2009)], especially in the absence
of a full range of future markets which can help suppliers mitigate the impact of cost shocks—as discussed by Borenstein and
Shepard (2002) and Alm, Sennoga, and Skidmore (2005).
The price transmission between the crude oil and wholesale
markets was investigated by Shin (1992) using the cointegration
framework and a high-frequency dataset. He found that both
the estimation framework and the data frequency affect the
results on nonlinearities in price transmission. More recently,
Bachmeier and Griffin (2003) analyzed the price transmission
between the crude oil and wholesale markets in the U.S.
and between wholesale and retail market using daily and
weekly data. They found some evidence for rejecting the null
hypothesis of linear transmission in the daily dataset, but not
for the weekly one.
Bettendorf, der Geest, and Varkevisser (2003) analyzed the
tax incidence on the transmission between daily spot wholesale
and retail prices over the period January 1996 to December
2001 in the Netherlands, using an error correction model. The
results indicate that, although nonlinearities are negligible, the
characteristics of price transmission vary across datasets with
different frequency.
While most researchers agree that with lower frequency the
null hypothesis of linear transmission is underrejected, CramonTaubadel and Meyer (2001) argued that if the data is too aggregated over time, in order to use nonlinear estimation techniques,
the researchers have to widen the time coverage of the research.
This, however, increases the probability of structural changes
occurring in the pricing relationship over the time period considered. The Monte Carlo experiments reported by CramonTaubadel and Meyer (2001) indicate that the size of nonlinearity tests surpasses the traditional levels, leading to overrejection
of the null hypothesis of symmetric price response. Paya and
Peel (2006) used a similar approach to ours to assess the impact of temporal aggregation/averaging on unit root testing but
in a different context, with an application to purchasing power
parity (PPP). Their main findings are that “(exponential) STAR
non-linearities are generally preserved in the temporally aggregated data, through the lag structure changes, and that the
implied speed of adjustment to shocks declines the more aggregated the data”(p. 666). We analyze similar issues but in the
context of price transmission and using a framework that allows
for nonlinearities of a more general nature (i.e., exponential or
logistic).
The main contribution of our article is to use recently developed tests for the presence of nonlinearities in the price trans-
Journal of Business & Economic Statistics, April 2012
mission process between the crude oil and wholesale markets,
and to compare the outcome of the testing procedure under different temporal aggregation of the time series for the relevant
prices, therefore identifying both the correct econometric framework and data frequency required to investigate nonlinearities
in price transmission.
As far as the econometric specification is concerned, we employ the nonlinear apparatus developed by Teräsvirta (1994)
and Escribano and Jordá (2001). Since these nonlinear models
depend on the contemporaneous markets, rather than the long
price history, they should not be affected by the omitted dynamics and nonlinearities in long-run adjustment—see Geweke
(2004) for a discussion of this issue.
The evidence for the U.S. wholesale markets seems to support
the view that supply adjustment costs explain the observed price
rigidities. The European markets investigated in our work, however, are more likely to be subject to market frictions which make
it costly to adjust prices when close to the equilibrium level but
necessary to adjust quickly when the prices move away from
equilibrium. This type of behavior is often observed in international markets where incomplete exchange rate passthrough
takes place. Although the fuels investigated in this work are all
quoted in U.S. dollars, the European suppliers in these markets
operate with different local currencies, which can increase the
market frictions and opportunities for arbitrage, making the European markets less integrated and efficient than the U.S. ones.
The major contribution to knowledge from this work is the finding that in the majority of cases analyzed price adjustment to
exogenous shocks is characterized by slow responses to small
disequilibria and faster responses to large disequilibria. The nature of the nonlinearities found in this work provides evidence in
support of the existence of menu costs, or more generally, frictions in the markets rather than supply adjustment costs. These
results are different from the results found for the U.S. petroleum
markets, for example, in Borenstein and Shepard (2002).
2.
DATA
The data used for our empirical analysis involve two sets of
net-of-taxes price series (ordered according to the place in the
supply chain):
• USD prices of Brent crude oil (denoted upstream prices)
which was found to be the price leading crude oil for the
European Union area—see Hagströmer, Wlazlowski, and
Giulietti (2010);
• USD wholesale prices from the Amsterdam–Rotterdam–
Antwerp (ARA) area which represent the spot prices in the
EU and are denoted midstream.
The price series cover the period June 1994 to November
2006 (diesel oil) and January 1994 to November 2006 (all remaining products). Different data coverage is due to the change
in methodology of gathering prices for diesel fuel related to strict
environmental policies introduced in 1994. Following the convention in the industry, our preliminary analysis also involved an
additional kind of fuel—liquefied petroleum gas (LPG). The results, however, are not reported here as LPG differs significantly
from other products analyzed in this article. Most importantly
this is the only product that might be obtained from sources
Downloaded by [Universitas Maritim Raja Ali Haji] at 22:35 11 January 2016
Wlazlowski et al.: Petroleum Markets
167
other than crude oil. Worldwide, about 40% of the LPG is produced in crude oil refining and 60% is produced during crude oil
and natural gas extraction—see Hekkert, Hendriks, Faaij, and
Neelis (2005). Those two sources differ significantly in terms
of production technology—the associated gas does not have
to be processed unlike crude oil—and economic properties—
transporting LPG from the extraction site to the consumer is less
efficient than crude oil transportation to the refiner. Therefore,
although crude oil remains the main source of LPG, its pricing
mechanism is unique in some aspects. Our results confirmed
this with estimates for LPG differing from those for remaining
products. Results for LPG are available upon request.
All series are expressed in logarithms in order to avoid
problems with nonlinear trends in the data. Also, we represent
the pricing mechanism as a Cobb–Douglas function which
allows us to incorporate the effects of the exchange rate for
cases when the transmission process involves tiers denominated
in different currencies.
Furthermore, we have market data provided by the
practitioners—the midstream prices were provided by Platt’s,
a leading industry consultancy and price data provider. Both
upstream and midstream prices are heavily disaggregated in
terms of product and geographical coverage and therefore are
typically implemented in the so-called price-formulas used
in the over-the-counter transactions worldwide—see Claessens
and Varangis (1995) and Bacon and Kojima (2006). As such,
both series provide a reliable indicator of the spot market in
Europe. To analyze all time series, we pair upstream prices with
the appropriate midstream prices quoted on the same (or earliest available) date. This is the common market approach, as the
lead–lag times cannot be specified in advance.
Since for the upstream–midstream transmission, both original
daily data and the constructed weekly data are available, we
apply the nonlinear framework to both samples and compare
the results. We focus on the comparison of results obtained
from both datasets—a detailed STAR analysis of wholesaleto-retail transmission in Europe can be found in, for example,
Wlazlowski, Binner, Giulietti, and Milas (2009).
3.
3.1.
APPLIED ANALYSIS
Analysis of Frequency of Adjustments
Using the standard Augmented Dickey-Fuller (ADF) tests,
all series are found to be integrated of order one. It is assumed
that the price shocks emanate from the larger, more liquid market where the trading volume is concentrated—for example,
Adrangi, Chatrath, Raffiee, and Ripple (2001). The relationship between the prices was verified using the ADF-type test
proposed by Phillips and Ouliaris (1990). In most cases that
we analyzed, the null hypothesis of a spurious relationship was
rejected and the midstream prices were found to revert to the
equilibrium set by upstream prices.
As the next step, for every product–country–transmission tier,
we analyze the dynamics of the adjustments in the symmetric
case using the following model (for simplicity, the exchange
rate is not presented):
p
yt = α + ωxt +
l=1
ψl zt−l + δ0 ût−1 + νt ,
(1)
Table 1. Midstream response to upstream changes—half-life and
90% decay times (in weeks)
Weekly data
Periods
Unleaded
Diesel
Heating
L/S refined
H/S refined
Leaded
Half-life
0.73 (0.52)
0.89 (0.51)
0.81 (0.53)
1.04 (0.37)
0.75 (0.29)
0.75 (0.35)
90% decay
4.58 (1.95)
2.80 (1.33)
6.67 (2.29)
4.84 (2.12)
2.67 (1.23)
4.67 (2.44)
Daily data
Periods
Unleaded
Diesel
Heating
L/S refined
H/S refined
Leaded
Half-life
0.81 (0.43)
0.75 (0.36)
0.89 (0.45)
0.62 (0.29)
0.78 (0.34)
0.85 (0.32)
90% decay
1.98 (0.89)
1.66 (0.55)
2.06 (1.01)
1.84 (0.88)
1.92 (0.55)
2.03 (1.03)
NOTE: Numbers in parentheses are standard errors.
where yt are the midstream prices, xt are the corresponding
upstream prices, zt = (yt , xt ) are the price dynamics, ût
are the residuals from the level equations (yt = δ0 + δ1 xt + ut ),
the sign of which determines whether the sellers’ margins are
squeezed (negative disequilibria) or rather artificially inflated
(positive ones). Using the estimates, we calculate the half-life
and 90% decay of upstream shocks, which proxy the adjustment
speeds. The results for daily data are divided by 5 (the number
of pricing days a week) to achieve comparability with weekly
data.
Table 1 presents the results (half-lives and 90% decays together with standard errors obtained from bootstrapping the
residuals of the estimated models with 200 repetitions). The
most important finding from this analysis is that the estimates of
the time required for the adjustment in upstream-to-midstream
transmission are lower than the frequency unit (one week). On
the basis of this result, we undertake the analysis of nonlinearities in price transmission based on the daily data on upstream
and midstream prices.
3.2.
Testing for the Presence of Nonlinearities
The analysis of the symmetric ECM indicates that the price
transmission between upstream and midstream tiers is best
captured with the use of daily data. Nonlinearities in the price
transmission can be conveniently captured by the following
simplification of Equation (1) which allows for change in
the adjustment speed, the value of which is governed by the
smooth transition function G((ût−d , ζ, c) bounded between
0 and 1:
m
ût = [1 − G(ût−d , ζ, c)] δ0L ût−1 +
δiL ût−i
i=1
+ G(ût−d , ζ, c) δ0H ût−1 +
m
i=1
δiH ût−i + νt , (2)
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168
Journal of Business & Economic Statistics, April 2012
Figure 1. Comparison of nonlinear models.
where the d is the delay parameter, which determines how
responsive the adjustment is to lagged disequilibria and margin
changes. In our models, we set d based on the grid search, so
as to minimize the residual sum of squares from the model, in
line with the approach of Hansen (1996, 1997). Depending on
the specification of the transition function, different patterns
of adjustment could be analyzed. Following, for example, van
Dijk, Teräsvirta, and Franses (2002) and Lundbergh, Terasvirta,
and Van Dijk (2003), we consider two most common functions,
that is:
2
• exponential function G(ût−d , ζ, c) = 1 − e−ζ (ût−d −c) ;
• logistic function G(ût−d , ζ, c) = 1+e−ζ 1(ût−d −c) ,
where ζ is the smoothness parameter, which determines the
smoothness in the switch from one adjustment regime to the
other—the closer it is to zero the smoother the transition is.
When the value of the parameter approaches ∞, the logistic function has a sudden switch and the exponential function becomes linear. The centering parameter c determines the
position of the transition function relative to 0. The presence
of a smooth transition between two regimes is the defining feature of STAR models, while the previously used SETAR models
(which also comprise two regimes) assume a sudden and full
switch between L(ow) and H(igh) regimes, G = 1 ⇔ ût−d > c.
Figure 1 presents the different adjustments for ESTAR, LSTAR,
and SETAR models. For simplicity, it is assumed that the threshold parameters (c’s) are the same across models and all equal
zero, so that the SETAR/LSTAR regimes are symmetric around
zero and the ESTAR adjustment is symmetric with respect to
the distance from zero.
The logistic function gives rise to a model which has different
adjustment speeds for negative and positive residuals, while the
exponential function involves the same adjustments for extreme
positive or negative residuals, but different adjustment for
small and extreme values (left panel). These two regimes are
denoted H and L since when G(·) = 0 the adjustment is equal
to δ0L , while when G(·) = 1 the adjustment is δ0H . The other δ H
and δ L parameters describe short-run dynamics affecting the
adjustment process in both models.
We attempted to estimate an even more general model in
which adjustment depends on both the size (small or large) and
sign (positive or negative) of the residual. We were unable to
obtain convergent estimates of this model (even imposing a
common threshold and smoothness parameter). Thus, we only
focus our attention on estimates of Equation (1) using either
the exponential or logistic function.
Because of the extra parameters in the transition function,
the direct tests for the nonlinear model given by (2) against the
one specified by (1) are not possible—see the discussion on
the identification problem in Davies (1987). However, one can
modify (2) and estimate a simplified model with the Taylor expansion of the transition function around c and the assumption
of d = 1 (so that (2) resembles a cointegrating Dickey–Fuller
equation):
Tn (f (x)) =
∞
f (n) (a)
n=0
n!
(x − a)n ,
Tn (G(·))|a = G(a) + G′ (a)(x − a) +
+
G′′ (a)
(x − a)2
2
G(3) (a)
(x − a)3 + · · · ·
3!
We rewrite (2), so that it becomes:
ût = δ0L ût−1 +
m
i=1
+
m
i=1
δiL ût−i + G(ût−d , ζ ) δ0H − δ0L ût−1
H
δi − δiL ût−i + νt
(3)
and replace the transition functions with their fourth order Taylor
expansions around a = 0. Following the procedure suggested by
Wlazlowski et al.: Petroleum Markets
169
Table 2. Comparison of results for weekly and daily data
Product
ULP
Diesel
Heating oil
L/S refined
H/S refined
Leaded petrol
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ULP
Diesel
Heating oil
L/S refined
H/S refined
Leaded petrol
H0
1.35 (0.214)
2.73 (0.0058)
1.58 (0.1262)
0.60 (0.7735)
0.99 (0.4379)
0.50 (0.8532)
8.87 (1.1 × 10−21)
5.28 (3.9 × 10−11
5.37 (2.3 × 10−20 )
3.88 (1.3 × 10−8 )
4.56 (2.3 × 10−7 )
13.89 (1.9 × 10−28 )
H0E
End result
H0L
Weekly data
–
4.58 (0.0011)
–
–
–
–
–
3.83 (0.0043)
–
–
–
–
Linear
ESTAR
Linear
Linear
Linear
Linear
Daily data
5.58 (4.9 × 10−7 )
6.90 (4.9 × 10−9 )
3.06 (3.8 × 10−5 )
2.75 (0.0021)
0.37 (0.8977)
6.28 (1.4 × 10−6 )
2.12 (0.0306)
4.45 (2.3 × 10−5 )
3.35 (6.9 × 10−6 )
5.29 (9.0 × 10−8 )
8.80 (1.6 × 10−9 )
6.78 (3.8 × 10−7 )
LSTAR
LSTAR
ESTAR
ESTAR
ESTAR
ESTAR
The table reports tests for the hypotheses H0 : ζ5 = ζ4 = ζ3 = ζ2 = 0, H0L : ζ5 = ζ3 = 0 and H0E : ζ4 = ζ2 = 0, as discussed in the main text.
Escribano and Jordá (2001), we then estimate:
ût = ζ0 + ζ1′ ∗ Xt + ζ2′ ∗ (Xt ∗ ût−d ) + ζ3′ ∗ Xt ∗ û2t−d
+ ζ4′ ∗ Xt ∗ û3t−d + ζ5′ ∗ Xt ∗ û4t−d + νt,
(4)
where Xt = (ût−1 , ût−1 , . . . , ût−m ), and perform the following step-by-step testing algorithm:
1. test H0 : ζ5 = ζ4 = ζ3 = ζ2 = 0—if rejected proceed, if not
then conclude that no nonlinearities were found;
2. test H0L : ζ5 = ζ3 = 0 with the help of an F-test denoted FL ;
3. test H0E : ζ4 = ζ2 = 0 with the help of an F-test denoted FE ;
4. if the minimum p-value corresponds to FL select LSTAR,
otherwise select ESTAR.
The first step comprises standard tests for the null hypothesis
of linearity, while the remaining ones test for the shape of the
transition function.
For weekly data, the results reported in Table 2 indicate the
presence of nonlinearities only for diesel oil. Indeed, the null
hypothesis of linearity (H0 : ζ5 = ζ4 = ζ3 = ζ2 = 0) is rejected
only in this case, where the ESTAR model is chosen based on
the minimum p-value delivered for the H0L : ζ5 = ζ3 = 0 hypothesis. For daily data, instead, nonlinearities are present in all
transmissions as the null hypothesis of linearity indicates that
the presence of nonlinearities (H0 : ζ5 = ζ4 = ζ3 = ζ2 = 0) is
rejected in all cases. A comparison of the p-values associated
with the H0L : ζ5 = ζ3 = 0 and H0E : ζ4 = ζ2 = 0 hypotheses
suggests an LSTAR model for unleaded petrol and diesel and an
ESTAR model for all other transmissions. For H/S Refined, linearity is strongly rejected in favor of an ESTAR specification; in
this case, there is very strong evidence against an LSTAR model.
Our results indicate that the size of the price change (absolute
size of the disequilibria that affect the exponential transmission
function) is as important as the sign of the price change in the
price transmission process. In other words, the possibility of a
slow downward price adjustment following price decreases upstream (as modeled in the LSTAR framework) is not supported
by our results. Instead, we find a more natural distinction between responses to small (positive and negative) disequilibria
as opposed to the bigger ones (as modeled in the ESTAR framework). With respect to the differences between daily and weekly
datasets, our results indicate that when using weekly data, the
null of linear price transmission is rejected only in two cases out
of seven, while for daily data the same hypothesis is rejected
in all cases. By using higher frequency data (daily and weekly
as opposed to weekly and monthly), we shorten the time coverage and thus avoid problems related to stability of the pricing
relationship and their impact on testing for the presence of nonlinearities, as discussed by Cramon-Taubadel and Meyer (2001).
For both exponential and logistic nonlinearities, the smooth
transition between pricing regimes indicates that the changes
in the pricing process are gradual rather than sudden and full,
as assumed in the SETAR models usually employed for that
purpose. This is illustrated by the results presented in Table A.1
in the Appendix, where the estimated values of the smoothness
parameters (ζ ) vary between approximately 1.9 and 20. Further
to nonlinear tests which favor ESTAR over LSTAR models, we
have attempted a direct comparison of both models based on regression standard errors and adjusted R 2 ’s. These statistical tests
(available on request) confirm the superiority of ESTAR models
in terms of lower standard errors and higher adjusted R 2 ’s.
There are a number of possible explanations for the visible
pattern of underrejection of the null of symmetry for the weekly
data. Apart from the arguments suggested by Geweke (1978) and
Blank and Schmiesing (1990), a reasonable explanation is that
the power of the test increases with the use of a larger sample
size in the form of daily as opposed to weekly observations.
Therefore, the explanation suggesting that excessive temporal
aggregation might obscure the actual price pairs looks more
plausible. Although solving this puzzle is beyond the scope of
this article, possible research into this issue might involve Monte
Carlo studies similar to those used by Paya and Peel (2006) for
the ESTAR case.
3.3.
Extent of Nonlinearities
Using the daily dataset for the transmissions identified as nonlinear, we estimated the STAR ECM models given by Equation
(3). The lag structure was chosen so as to maximize the AIC
criteria in the linear case.
The results for the nonlinear estimation are presented in detail
in Table A.1. The values of the coefficients on the lagged disequilibria indicate that for LSTAR models the prices adjust both
170
Journal of Business & Economic Statistics, April 2012
Table 3. Adjustment in linear models
Daily data
Product
Unleaded petrol
Diesel
Heating oil
L/S refined
R/S refined
Leaded petrol
Half-life
2.29 (2.36)
1.29 (1.89)
0.57 (5.91)
4.85 (2.30)
3.44 (1.78)
2.79 (2.12)
90% Decay
13.37 (3.97)
8.81 (2.64)
12.15 (4.74)
19.83 (2.80)
16.62 (2.85)
14.94 (3.67)
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NOTE: Numbers in parentheses are standard errors.
to cost increases and decreases while for the ESTAR models the
prices adjust to significant cost changes and exhibit sluggishness
following small cost changes. The values of the smoothing parameter are significantly higher for LSTAR models (more than
10 standard deviations of the disequilibria) compared to ESTAR
models (less than 5 standard deviations). This suggests that the
switch in LSTAR models is more abrupt, while the transition
between regimes in ESTAR models is smooth. The parameters
c are expressed as a percentile of disequilibria centered around
their respective medians.
The estimates from the nonlinear models were used to calculate the way in which disequilibria are eliminated in the
daily data. The values of decays of upstream shocks (half-lives
and 90% decays) are summarized in Tables 3 and 4, together
with scaled standard deviations obtained from bootstrapping
the residuals from nonlinear estimation in 200 repetitions. Our
models, which include long and variable lags of ût , are free
of autocorrelation based on the Breusch–Godfrey LM test (see,
e.g., Table A.1, where we consider autocorrelation up to order 20). For this reason, autocorrelation is not considered when
bootstrapping the errors of the models. In addition, residuals
are not pooled across products since we examine the path of
adjustment for each product separately.
The presence of ESTAR-type nonlinearities indicates that
when analyzing price transmission using higher frequency data,
the picture of nonlinearities in price responses reveals faster
changes than identified in the previous literature. Instead of
a two-regime, welfare decreasing pricing behavior, the results
of our analysis suggest a more intricate pricing behavior, with
sluggish responses to small cost changes (as indicated by the
presence of ESTAR-type nonlinearities), rich dynamics (as indicated by the lag structure), and 50% adjustment lasting less
than a month.
Another key feature of our ESTAR models based on daily
data is the difference in adjustment speeds for small and large
disequilibria. We consider simulated adjustments to two standard deviations shocks within L and H regimes of ESTAR models based on bootstrapping techniques and 1,000 draws with
replacement. We find that the adjustment is faster for large disequilibria and slower for small disequilibria. More specifically,
for leaded petrol and high-sulfur oil, we find a statistically significant difference in adjustment rates between large and small
disequilibria which peaks at just under 0.10 percentage points of
margin after 50 days before converging to 0.02 percentage points
after 150 days. For heating oil, we find a statistically significant
difference in adjustment between large and small disequilibria of up to 0.13 percentage points which persists beyond 150
days. For high-sulfur oil and diesel, the difference, in the opposite direction to the other fuels, peaks at about 0.03 percentage
points after 10 days but is completely eliminated after 50 days.
In summary, we find that the adjustment is faster for large and
slower for small disequilibria. This is evidence to suggest that,
as discussed earlier, the fuels investigated in this work are all
quoted in U.S. dollars, but the European suppliers in these markets operate with different local currencies which can increase
the market frictions and opportunities for arbitrage. This will
make the European markets less integrated and efficient than the
U.S. ones.
4.
CONCLUSIONS
In this article, we analyze nonlinearities in the transmission
of petroleum product prices in Europe. The analysis of the transmission speed summarized in Table 1 indicates that the traditional analysis based on weekly data might not be appropriate for
the upstream-to-midstream transmission. We revisit this transmission link with daily data to find that the nonlinearities are not
detected in a low-frequency sample. The use of flexible nonlinear models and high-frequency data, on the other hand, allows
us to find evidence of significant nonlinearities in the crude oil
to wholesale price transmission. This result differs from previous studies which use lower frequency observations and simpler
models to test for nonlinearities [e.g., Hosken, McMillan, and
Taylor (2008) for U.S. using weekly data and Rao and Rao
(2005) also for U.S. but with monthly data].
Our results build on those obtained by other researchers who
compared weekly and monthly data [e.g., Bachmeier and Griffin (2003) and Bettendorf et al. (2003)], and they add credence
Table 4. Adjustment in nonlinear models
Daily data
Positive
Unleaded petrol
Diesel
Heating oil
L/S refined
H/S refined
Leaded petrol
Half-life
2.9 (2.59)
1.44 (1.17)
0.57 (5.24)
3.99 (3.23)
3.46 (1.86)
2.78 (2.63)
NOTE: Numbers in parentheses are standard errors.
NA not available, denotes when in-regime adjustment does not reach 10% threshold.
Negative
90% decay
12.18 (5.46)
6.64 (2.08)
11.92 (4.86)
19.78 (4.02)
10.18 (5.03)
15.93 (4.67)
Half-life
3.30 (0.84)
3.37 (1.15)
5.11 (147.28)
6.14 (1.83)
5.01 (0.05)
4.91 (0.12)
90% decay
7.35 (4.59)
7.66 (6.89)
NA
17.06 (3.35)
8.41 (0.32)
9.17 (0.48)
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Wlazlowski et al.: Petroleum Markets
171
to the view that higher frequency of the data is necessary to
understand the mechanics of the price transmission and weekly
data might limit the ability to identify nonlinearities in price
transmission at the upstream level of the oil supply chain. More
importantly, our results contribute to the existing literature by
providing evidence of the widespread presence of ESTAR-type
nonlinearities which could be attributed to the presence of transaction costs and frictions in price transmission, rather than to
outcomes of collusive behavior in a different context, as argued by Borenstein, Cameron, and Gilbert (1997) and Peltzman
(2000). This result is important because it highlights the effects
of market frictions on prices which have not previously been
identified in studies of U.S. markets.
Our work can be extended in several directions. Most importantly, the impact of intertemporal data aggregation should be
verified using Monte Carlo simulations. Similarly, the results of
the simulation of price responses should be combined with additional information on buyer–seller interaction (such as volume
and frequency of transactions) to assess whether the identified
nonlinearities should be attributed to the use of higher data frequency or represent an inherent feature of upstream petroleum
markets. This could shed more light on the issue of nonlinearities in transmission and contribute toward a more rigorous
explanation of this phenomenon.
APPENDIX
Table A.1. Nonlinear ECM (daily data)
Estimate
Std. error
ûLt−1
ûLt−1
ûLt−2
ûLt−3
ûLt−16
ûH
t−1
ûH
t−1
ûH
t−2
ûH
t−3
ûH
t−16
ζ
c
LM20
Unleaded petrol
−0.052
−0.325
−0.146
−0.027
0.030
−0.035
−0.185
−0.090
−0.065
0.017
13.000
0.249
16.817
0.010
0.024
0.025
0.022
0.022
0.009
0.032
0.035
0.034
0.031
–
0.024
0.665
ûLt−1
ûLt−1
ûLt−2
ûLt−3
ûLt−8
ûLt−19
ûH
t−1
ûH
t−1
ûH
t−2
ûH
t−3
ûH
t−8
ûH
t−19
ζ
c
LM20
Diesel
−0.046
−0.439
−0.223
−0.109
0.069
−0.022
−0.081
−0.219
−0.113
0.025
0.001
−0.047
19.999
0.009
17.284
0.012
0.029
0.032
0.030
0.024
0.027
0.011
0.027
0.026
0.025
0.025
0.023
–
0.017
0.634
Table A.1. (Continued)
Estimate
Std. error
ûLt−1
ûLt−1
ûLt−2
ûLt−3
ûLt−4
ûLt−5
ûLt−6
ûLt−7
ûLt−19
ûH
t−1
ûH
t−1
ûH
t−2
ûH
t−3
ûH
t−4
ûH
t−5
ûH
t−6
ûH
t−7
ûH
t−19
ζ
c
LM20
Heating oil
0.007
−0.386
−0.092
−0.178
0.097
−0.076
−0.011
−0.022
−0.061
−0.053
−0.351
−0.224
−0.099
−0.084
−0.044
−0.047
−0.031
−0.020
5.012
−0.153
13.648
0.104
0.101
0.134
0.088
0.126
0.188
0.185
0.103
0.078
0.009
0.021
0.022
0.023
0.027
0.025
0.022
0.020
0.018
6.095
0.037
0.848
ûLt−1
ûLt−1
ûLt−2
ûLt−3
ûLt−13
ûLt−14
ûLt−19
ûH
t−1
ûH
t−1
ûH
t−2
ûH
t−3
ûH
t−13
ûH
t−14
ûH
t−19
ζ
c
LM20
Refined-oil-low sulphur
−0.024
−0.131
−0.059
0.114
0.001
0.034
−0.087
−0.024
−0.249
−0.073
0.002
0.037
0.025
−0.003
1.899
0.148
20.581
0.020
0.039
0.038
0.036
0.037
0.040
0.038
0.005
0.021
0.023
0.024
0.024
0.023
0.022
–
0.044
0.422
ûLt−1
ûLt−1
ûLt−2
ûLt−4
ûLt−19
ûH
t−1
ûH
t−1
ûH
t−2
ûH
t−4
ûH
t−19
ζ
c
LM20
Refined-oil-high sulphur
−0.183
−0.300
−0.069
−0.047
−0.089
−0.033
−0.173
–0.085
–0.036
–0.038
4.501
–0.031
16.130
0.151
0.066
0.067
0.061
0.058
0.005
0.021
0.021
0.020
0.020
1.240
0.037
0.709
ûLt−1
ûLt−1
ûLt−2
ûLt−16
ûH
t−1
Leaded petrol
–0.138
–0.039
0.043
0.075
–0.033
0.156
0.052
0.050
0.045
0.006
172
Journal of Business & Economic Statistics, April 2012
Table A.1. (Continued)
Estimate
ûH
t−1
ûH
t−2
ûH
t−16
ζ
c
LM20
Leaded petrol
–0.313
–0.145
0.020
7.128
–0.026
14.902
Std. error
0.020
0.021
0.020
–
0.021
0.782
NOTE: Entries marked with — refer to a situation when the values could not be calculated—
see, for example, Franses and van Dijk (2000). LM20 figures refer to Breusch–Godfrey serial
correlation F-test statistics (of order 20) together with corresponding p-values.
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ACKNOWLEDGMENTS
We want to thank the journal’s editor and associate editor in
addition to the two anonymous reviewers for their most useful
comments and suggestions on an earlier version of the article.
The authors also wish to acknowledge the help of Jeremy Smith
of the Department of Economics, University of Warwick, UK
in commenting on an early draft of this article.
[Received November 2007. Revised January 2011.]
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