07350015%2E2012%2E672290

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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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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 ofJBES, 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 ofJBESconsists of several papers from this conference.

Price Transmission in the EU Wholesale

Petroleum Markets

Szymon W

LAZLOWSKI

Economics and Strategy Group, Aston University, Aston Triangle, Birmingham B4 7ET, UK ([email protected])

Monica G

IULIETTI

Nottingham University Business School, University of Nottingham, Wollaton Road, Nottingham NG8 1BB, UK (Monica.Giulietti.nottingham.ac.uk)

Jane B

INNER

Accounting and Financial Management Division, Management School, University of Sheffield, Sheffield S1 4DT, UK ([email protected])

Costas M

ILAS

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 sig-nificant 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 transmis-sion 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 fric-tions as opposed to supply adjustment costs cause prices to adjust in disequilibrium. As these alternative views have dif-ferent 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 trans-mission, 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.

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166 Journal of Business & Economic Statistics, April 2012

This article analyzes the impact of data frequency on non-linear models of price transmission based on smooth transi-tion 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, irre-spective of the distance from the equilibrium, with one where the speed of adjustment depends on the distance from equi-librium. 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, Dra-nove, Shanley, and Schaefer (2009)], especially in the absence of a full range of future markets which can help suppliers miti-gate 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, Cramon-Taubadel and Meyer (2001) argued that if the data is too aggre-gated 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 con-sidered. The Monte Carlo experiments reported by Cramon-Taubadel and Meyer (2001) indicate that the size of nonlinear-ity 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 im-pact 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 ag-gregated data, through the lag structure changes, and that the implied speed of adjustment to shocks declines the more ag-gregated 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 devel-oped tests for the presence of nonlinearities in the price

trans-mission process between the crude oil and wholesale markets, and to compare the outcome of the testing procedure under dif-ferent temporal aggregation of the time series for the relevant prices, therefore identifying both the correct econometric frame-work and data frequency required to investigate nonlinearities in price transmission.

As far as the econometric specification is concerned, we em-ploy the nonlinear apparatus developed by Ter¨asvirta (1994) and Escribano and Jord´a (2001). Since these nonlinear models depend on the contemporaneous markets, rather than the long price history, they should not be affected by the omitted dy-namics 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, how-ever, 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 inter-national 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 Eu-ropean markets less integrated and efficient than the U.S. ones. The major contribution to knowledge from this work is the find-ing 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 na-ture of the nonlinearities found in this work provides evidence in support of the existence of menu costs, or more generally, fric-tions 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 Hagstr¨omer, 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 re-maining 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 con-vention in the industry, our preliminary analysis also involved an additional kind of fuel—liquefied petroleum gas (LPG). The re-sults, 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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other than crude oil. Worldwide, about 40% of the LPG is pro-duced in crude oil refining and 60% is propro-duced 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 earli-est 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 wholesale-to-retail transmission in Europe can be found in, for example, Wlazlowski, Binner, Giulietti, and Milas (2009).

3. APPLIED ANALYSIS

3.1. 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 mar-ket where the trading volume is concentrated—for example, Adrangi, Chatrath, Raffiee, and Ripple (2001). The relation-ship 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):

Table 1. Midstream response to upstream changes—half-life and 90% decay times (in weeks)

Weekly data

Periods

Half-life 90% decay

Unleaded 0.73 (0.52) 4.58 (1.95)

Diesel 0.89 (0.51) 2.80 (1.33)

Heating 0.81 (0.53) 6.67 (2.29)

L/S refined 1.04 (0.37) 4.84 (2.12) H/S refined 0.75 (0.29) 2.67 (1.23)

Leaded 0.75 (0.35) 4.67 (2.44)

Daily data

Periods

Half-life 90% decay

Unleaded 0.81 (0.43) 1.98 (0.89)

Diesel 0.75 (0.36) 1.66 (0.55)

Heating 0.89 (0.45) 2.06 (1.01)

L/S refined 0.62 (0.29) 1.84 (0.88) H/S refined 0.78 (0.34) 1.92 (0.55)

Leaded 0.85 (0.32) 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, ˆut are the residuals from the level equations (yt =δ0+δ1xt+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 1presents the results (half-lives and 90% decays to-gether 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 nonlinear-ities 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(( ˆut−d, ζ, c) bounded between

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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 setd 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, Ter¨asvirta, and Franses (2002) and Lundbergh, Terasvirta, and Van Dijk (2003), we consider two most common functions, that is:

• exponential functionG( ˆut−d, ζ, c)=1−e−ζ( ˆut−d−c) 2

;

• logistic functionG( ˆut−d, ζ, c)= ₁₊_e−ζ1( ˆut−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 logis-tic function has a sudden switch and the exponential func-tion becomes linear. The centering parametercdetermines the position of the transition function relative to 0. The presence of a smooth transition between two regimes is the defining fea-ture 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⇔uˆt−d > c. Figure 1presents the different adjustments for ESTAR, LSTAR, and SETAR models. For simplicity, it is assumed that the thresh-old 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 whenG(·)=0 the adjustment is equal

toδL

0, while whenG(·)=1 the adjustment isδ0H. The otherδH andδL _{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 ex-pansion of the transition function aroundcand the assumption ofd =1 (so that (2) resembles a cointegrating Dickey–Fuller equation):

Tn(f(x))=

∞

n=0

f(n)₍_a₎

n! (x−a) n_,

Tn(G(·))|a =G(a)+G′(a)(x−a)+

G′′₍_a₎

2 (x−a) 2

+G

(3)₍_a₎

3! (x−a) 3

+ · · · ·

We rewrite (2), so that it becomes:

uˆt =δL0uˆt−1+ m

i=1

δ_iLuˆt−i+G( ˆut−d, ζ)

δH₀ −δ₀L

ˆ

ut−1

+

m

i=1

δ_iH−δL_iuˆt−i

+νt (3)

and replace the transition functions with their fourth order Taylor expansions arounda =0. Following the procedure suggested by

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Table 2. Comparison of results for weekly and daily data

Product H0 H0L H0E End result

Weekly data

ULP 1.35 (0.214) – – Linear

Diesel 2.73 (0.0058) 4.58 (0.0011) 3.83 (0.0043) ESTAR

Heating oil 1.58 (0.1262) – – Linear

L/S refined 0.60 (0.7735) – – Linear

H/S refined 0.99 (0.4379) – – Linear

Leaded petrol 0.50 (0.8532) – – Linear

Daily data

ULP 8.87 (1.1×10−21₎ _{5.58 (4.9}_×₁₀−7₎ _{2.12 (0.0306)} _LSTAR

Diesel 5.28 (3.9×10−11 _{6.90 (4.9}_×₁₀−9₎ _{4.45 (2.3}_×₁₀−5₎ _LSTAR Heating oil 5.37 (2.3×10−20₎ _{3.06 (3.8}_×₁₀−5₎ _{3.35 (6.9}_×₁₀−6₎ _ESTAR L/S refined 3.88 (1.3×10−8₎ _{2.75 (0.0021)} _{5.29 (9.0}_×₁₀−8₎ _ESTAR H/S refined 4.56 (2.3×10−7₎ _{0.37 (0.8977)} _{8.80 (1.6}_×₁₀−9₎ _ESTAR Leaded petrol 13.89 (1.9×10−28₎ _{6.28 (1.4}_×₁₀−6₎ _{6.78 (3.8}_×₁₀−7₎ _ESTAR

The table reports tests for the hypothesesH0:ζ5=ζ4=ζ3=ζ2=0,H0L:ζ5=ζ3=0 andH0E:ζ4=ζ2=0, as discussed in the main text. Escribano and Jord´a (2001), we then estimate:

uˆt =ζ0+ζ1′∗Xt+ζ₂′∗(Xt∗uˆt−d)+ζ3′∗

_X

t∗uˆ2_t₋_d

+ζ₄′∗_X

t∗uˆ3_t₋_d+ζ₅′∗Xt∗uˆ4_t₋_d+νt, (4) whereXt =( ˆut−1, uˆt−1, . . . , uˆt−m), and perform the

follow-ing step-by-step testfollow-ing algorithm:

1. testH0:ζ5=ζ4=ζ3=ζ2=0—if rejected proceed, if not then conclude that no nonlinearities were found;

2. testH0L:ζ5=ζ3=0 with the help of anF-test denotedFL; 3. testH0E:ζ4 =ζ2=0 with the help of anF-test denotedFE; 4. if the minimump-value corresponds toFL 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 inTable 2indicate 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 minimump-value delivered for theH0L:ζ5=ζ3=0 hy-pothesis. 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 thep-values associated with theH0L:ζ5=ζ3=0 andH0E: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, lin-earity 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 up-stream (as modeled in the LSTAR framework) is not supported by our results. Instead, we find a more natural distinction be-tween responses to small (positive and negative) disequilibria as opposed to the bigger ones (as modeled in the ESTAR frame-work). 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 cov-erage and thus avoid problems related to stability of the pricing relationship and their impact on testing for the presence of non-linearities, 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 inTable 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 re-gression standard errors and adjustedR2_{’s. These statistical tests} (available on request) confirm the superiority of ESTAR models in terms of lower standard errors and higher adjustedR2_’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 non-linear, 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 inTable A.1. The values of the coefficients on the lagged dise-quilibria indicate that for LSTAR models the prices adjust both

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Table 3. Adjustment in linear models

Daily data

Product Half-life 90% Decay

Unleaded petrol 2.29 (2.36) 13.37 (3.97)

Diesel 1.29 (1.89) 8.81 (2.64)

Heating oil 0.57 (5.91) 12.15 (4.74) L/S refined 4.85 (2.30) 19.83 (2.80) R/S refined 3.44 (1.78) 16.62 (2.85) Leaded petrol 2.79 (2.12) 14.94 (3.67)

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 pa-rameter 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 care expressed as a percentile of disequilibria centered around their respective medians.

The estimates from the nonlinear models were used to cal-culate 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 Tables3 and4, together with scaled standard deviations obtained from bootstrapping the residuals from nonlinear estimation in 200 repetitions. Our models, which include long and variable lags ofuˆt, are free of autocorrelation based on the Breusch–Godfrey LM test (see, e.g., Table A.1, where we consider autocorrelation up to or-der 20). For this reason, autocorrelation is not consior-dered 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 in-dicated 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 stan-dard deviations shocks within L and H regimes of ESTAR mod-els based on bootstrapping techniques and 1,000 draws with replacement. We find that the adjustment is faster for large dis-equilibria and slower for small disdis-equilibria. More specifically, for leaded petrol and high-sulfur oil, we find a statistically sig-nificant 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 disequilib-ria of up to 0.13 percentage points which persists beyond 150 days. For high-sulfur oil and diesel, the difference, in the oppo-site 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 mar-kets 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 trans-mission speed summarized inTable 1indicates that the tradi-tional analysis based on weekly data might not be appropriate for the upstream-to-midstream transmission. We revisit this trans-mission link with daily data to find that the nonlinearities are not detected in a low-frequency sample. The use of flexible nonlin-ear 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 previ-ous 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 Grif-fin (2003) and Bettendorf et al. (2003)], and they add credence

Table 4. Adjustment in nonlinear models

Daily data

Positive Negative

Half-life 90% decay Half-life 90% decay

Unleaded petrol 2.9 (2.59) 12.18 (5.46) 3.30 (0.84) 7.35 (4.59)

Diesel 1.44 (1.17) 6.64 (2.08) 3.37 (1.15) 7.66 (6.89)

Heating oil 0.57 (5.24) 11.92 (4.86) 5.11 (147.28) NA

L/S refined 3.99 (3.23) 19.78 (4.02) 6.14 (1.83) 17.06 (3.35)

H/S refined 3.46 (1.86) 10.18 (5.03) 5.01 (0.05) 8.41 (0.32)

Leaded petrol 2.78 (2.63) 15.93 (4.67) 4.91 (0.12) 9.17 (0.48)

NA not available, denotes when in-regime adjustment does not reach 10% threshold.

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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 trans-action costs and frictions in price transmission, rather than to outcomes of collusive behavior in a different context, as ar-gued 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 impor-tantly, 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 addi-tional 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 fre-quency or represent an inherent feature of upstream petroleum markets. This could shed more light on the issue of nonlin-earities in transmission and contribute toward a more rigorous explanation of this phenomenon.

APPENDIX

Table A.1. Nonlinear ECM (daily data)

Estimate Std. error

(9)

NOTE: Entries marked with — refer to a situation when the values could not be calculated— see, for example, Franses and van Dijk (2000).LM20figures refer to Breusch–Godfrey serial correlationF-test statistics (of order 20) together with correspondingp-values.

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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