32
WTI-Brent Spread and the value of refining firms. Amir H. Sabet Andrew Caminschi Richard Heaney Business School University of Western Australia
WTI-Brent Spread and the value of refining firms.
Amir H. Sabet
Andrew Caminschi
Richard Heaney
Business School
University of Western Australia
Summary of the paper.
• This paper examines the relation between WTI-Brent spread
and the value of oil refining firm stocks.
• Using S&P 500 refining and marketing index as a proxy for
value of refining firms, our finding is consistent with the view
that discounted WTI relative to Brent prices boosts the value
of refining firms in U.S.
• Using impulse response function simulation techniques, we
found that a positive shock to WTI-Brent spread has a negative
impact on refining firm stock prices.
2
Intuition
3
Intuition
• Both commodities deliver identical consumer benefit, the
economic theory of substitution suggests prices should
converge to some equilibrium, albeit with some bound to
allow for market frictions.
• The central idea being US refiners hold an economic
advantage in terms of crude access, stemming from
logistic/geographic and regulatory advantages.
• These in turn translate to higher earnings power at times of
large WTI discount to Brent, which in turn translates to higher
valuations.
4
WTI-Brent Spread and S&P 500 refining index
5
Input Perspective
• One of the key factors driving this spread is the limited storage in Cushing,
Oklahoma (Büyükşahin et al. 2013) – a key supply basin for US crude.
• While their final product (heating oil, unleaded gasoline & diesel) is
fungible with those of foreign refiners, they have two readily available
inputs to draw on – locally sourced WTI or imported Brent.
• Further, European refiners cannot access U.S. crude oil following the
restrictions passed by U.S. regulators in 1975 which prevents the export of
U.S. crude oil (CRS 2014).
• If equity markets are efficient then when WTI is trading at a material
discount to Brent, US refiners should experience periods of elevated
profitability (high earning), which in turn should be reflected in higher
refiner equity returns.
6
Output Perspective
• Borenstein and Kellogg (2012) show that a decrease in US
benchmark crude oil prices has no effect on the wholesale
gasoline and diesel prices.
• This is consistent with our argument that the price of US
refinery output is fungible with foreign outputs, highlighting
the importance of the WTI discount.
• while U.S. oil refiners’ use discounted WTI as an input for
refinery, they can export their petroleum products including
heating oil and unleaded gasoline to Europe and taking another
advantage.
7
The International Energy Agency (IEA) reported that 16 major
European refineries will be shut down in 2014 as these refiners
are acquiring crude oil based on the price Brent crude oil,
generally the more expensive than WTI.
Source: Bloomberg
8
Literature
• The spread between different crude oil prices have drawn to
the attention of academics over the past few years
– (Lanza et al. 2005, Fattouh 2007, Pirrong 2010, Kao & Wan 2012).
• The relationship between general commodity prices and
stock prices has also been investigated in previous studies
– (El-Sharif et al. 2005, Basher & Sadorsky 2006, Faff & Brailsford
1999, Huang et al. 1996; Jones & Kaul 1996; Sadorsky 1999)
• Our paper contributes to the literature by combining the
previous literature on commodity prices differentials and the
literature on the relationship between commodity prices and
stock prices.
9
Methodology
10
Data
• Our sample consists of weekly time series data collected via
numerous sources from the first week of January 2006 to the
third week of December 2013.
• Oil price data
– Closest to maturity futures contract.
– Bloomberg
• Stock / Index prices
– Thompson Reuters Data Stream.
• Refinery data
– EIA
11
Granger Causality
12
Dependent
Variable
W-B REF CS REFUTILIZ All
W-B 38.898*** 25.258** 42.492*** 129.08***
REF 38.371*** 24.045** 33.296*** 108.89***
CS 78.500*** 27.597*** 58.806*** 204.930***
REFUTILIZ 14.405 11.487 45.008*** 69.675***
Impulse Response function (post 2008)
Response of SPREF to increase in WBS
13
-4
-3
-2
-1
0
1
0 2 4 6 8 10 12 14 16 18 20
ord76, LNWTIBRENTSPREAD, LNSPREFMKT
95% CI impulse-response function (irf)
step
Graphs by irfname, impulse variable, and response variable
Regression Results
14
Full sample
2006-2013
Subsample
(1) (2) 2006-07 2008-2013
Constant 0.001 -0.010* 0.002 -0.001
(0.0028) (0.0057) (0.0048) (0.0034)
RETSP500 -0.043 -0.072 -0.218 -0.039
(0.1396) (0.1360) (0.3253) (0.1485)
WTIBRENTRET 0.237* 0.247* -0.290 0.363**
(0.1365) (0.1324) (0.3097) (0.1792)
RETTBILL4W 0.053 0.005 -0.052 0.005
(0.006) (0.0061) (0.069) (0.0062)
RETMIDWEST 0.240 0.198 -0.085 0.410
(0.170) (0.1696) (0.203) (0.2376)
RETCS 0.024 0.023 -0.012 0.048
(0.0264) (0.0258) (0.0265) (0.038)
RETPERCENT UTIL -0.593*** -0.589*** -0.308 -0.651***
(0.226) (0.2183) (0.3176) (0.2485)
WTI < Brent dummy 0.016***
(0.0063)
R 2 0.0434 0.0597 0.0289 0.0816
N 415 415 104 311
Conclusions
• The persistent supply imbalance and transportation frictions
from mid-west crude have created price differentials that are
economically significant.
• U.S. based refiners are indeed positioned to profit from this
imbalance and this is reflected in refiner equity returns.
• Market participants seeking to profit from future changes in
the WTI-Brent spread could trade on these views via U.S.
refiners, either through direct equity positions or index
tracking ETFs.
15
Impulse Response Function
16
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0
2
4
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0
2
4
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0
2
4
-4
-2
0
2
4
0 2 4 6 8 10 12 14 16 18 20 0 2 4 6 8 10 12 14 16 18 20
0 2 4 6 8 10 12 14 16 18 20 0 2 4 6 8 10 12 14 16 18 20
ord55, LNCRACKSPREAD, LNSPREFMKT ord55, LNPERCUTILZE, LNSPREFMKT
ord55, LNSPREFMKT, LNSPREFMKT ord55, LNWTIBRENTSPREAD, LNSPREFMKT
95% CI impulse-response function (irf)
step
Graphs by irfname, impulse variable, and response variable
17
.
Exogenous: _cons
Endogenous: LNWTIBRENTSPREAD LNSPREFMKT LNCRACKSPREAD LNPERCUTILZE
20 2406.52 28.793* 16 0.025 1.9e-11 -13.3924 -11.8351 -9.49632
19 2392.13 26.922 16 0.042 1.9e-11 -13.4028 -11.9223 -9.69903
18 2378.67 31.079 16 0.013 1.8e-11 -13.4191 -12.0156 -9.90776
17 2363.13 20.838 16 0.185 1.8e-11 -13.422 -12.0954 -10.1031
16 2352.71 27.172 16 0.040 1.7e-11 -13.4579 -12.2082 -10.3314
15 2339.12 19.95 16 0.222 1.7e-11 -13.4735 -12.3006 -10.5393
14 2329.15 26.019 16 0.054 1.6e-11 -13.5122 -12.4163 -10.7705
13 2316.14 37.205 16 0.002 1.6e-11* -13.5314* -12.5124 -10.9821
12 2297.53 34.591 16 0.005 1.6e-11 -13.5147 -12.5726 -11.1578
11 2280.24 34.628 16 0.004 1.6e-11 -13.5064 -12.6412 -11.3419
10 2262.93 40.054 16 0.001 1.6e-11 -13.4979 -12.7096 -11.5258
9 2242.9 30.297 16 0.017 1.7e-11 -13.472 -12.7606 -11.6923
8 2227.75 24.619 16 0.077 1.7e-11 -13.4775 -12.843 -11.8902
7 2215.44 32.96 16 0.007 1.6e-11 -13.5012 -12.9437 -12.1063
6 2198.96 26.967 16 0.042 1.6e-11 -13.4981 -13.0175 -12.2956
5 2185.48 59.524 16 0.000 1.6e-11 -13.5143 -13.1106* -12.5042
4 2155.71 59.58 16 0.000 1.7e-11 -13.4258 -13.099 -12.6081
3 2125.92 53.017 16 0.000 1.9e-11 -13.3371 -13.0872 -12.7118
2 2099.42 113.42 16 0.000 2.0e-11 -13.2696 -13.0965 -12.8367*
1 2042.7 2655.4 16 0.000 2.6e-11 -13.0077 -12.9116 -12.7672
0 715.022 1.2e-07 -4.57249 -4.55326 -4.52439
lag LL LR df p FPE AIC HQIC SBIC
Sample: 1/4/2008 - 12/13/2013 Number of obs = 311
Selection-order criteria
. varsoc LNWTIBRENTSPREAD LNSPREFMKT LNCRACKSPREAD LNPERCUTILZE if year >= 2008, maxlag(20)
var LNWTIBRENTSPREAD LNSPREFMKT LNCRACKSPREAD LNPERCUTILZE if year >=
2008, lags(1/13) exog(L14.LNWTIBRENTSPREAD L14.LNSPREFMKT
L14.LNCRACKSPREAD L14.LNPERCUTILZE)
18
-4
-2
0
2
4
-4
-2
0
2
4
-4
-2
0
2
4
-4
-2
0
2
4
0 2 4 6 8 10 12 14 16 18 20 0 2 4 6 8 10 12 14 16 18 20
0 2 4 6 8 10 12 14 16 18 20 0 2 4 6 8 10 12 14 16 18 20
ord55, LNCRACKSPREAD, LNSPREFMKT ord55, LNPERCUTILZE, LNSPREFMKT
ord55, LNSPREFMKT, LNSPREFMKT ord55, LNWTIBRENTSPREAD, LNSPREFMKT
95% CI impulse-response function (irf)
step
Graphs by irfname, impulse variable, and response variable
19
H0: no autocorrelation at lag order
15 21.2878 16 0.16771
14 8.8589 16 0.91911
13 18.5251 16 0.29406
12 8.8335 16 0.92011
11 16.8153 16 0.39765
10 24.3957 16 0.08121
9 15.1540 16 0.51339
8 15.2375 16 0.50731
7 16.7091 16 0.40466
6 8.8117 16 0.92096
5 43.0536 16 0.00027
4 36.5075 16 0.00246
3 15.3694 16 0.49777
2 20.9424 16 0.18073
1 14.4939 16 0.56197
lag chi2 df Prob > chi2
Lagrange-multiplier test
20
more
Exogenous: _cons
LNMIDWESTSTORAGE
Endogenous: LNSPREFMKT LNWTIBRENTSPREAD LNCRACKSPREAD LNPERCUTILZE
15 3331.07 39.302* 25 0.034 4.1e-15 -18.978 -17.1514 -14.4084
14 3311.42 36.053 25 0.071 4.0e-15 -19.0123 -17.306 -14.7435
13 3293.39 46.058 25 0.006 3.8e-15 -19.0572 -17.471 -15.0889
12 3270.36 61.474 25 0.000 3.7e-15 -19.0699 -17.6039 -15.4022
11 3239.63 47.478 25 0.004 3.8e-15 -19.033 -17.6871 -15.666
10 3215.89 51.753 25 0.001 3.8e-15 -19.0411 -17.8154 -15.9747
9 3190.01 33.253 25 0.125 3.8e-15 -19.0354 -17.9299 -16.2697
8 3173.39 34.717 25 0.093 3.6e-15 -19.0893 -18.1039 -16.6242
7 3156.03 37.116 25 0.056 3.4e-15 -19.1384 -18.2733 -16.9739
6 3137.47 30.47 25 0.207 3.2e-15 -19.1799 -18.4348 -17.316
5 3122.23 80.354 25 0.000 3.0e-15* -19.2427* -18.6178 -17.6794
4 3082.06 91.168 25 0.000 3.3e-15 -19.1451 -18.6404 -17.8824
3 3036.47 71.24 25 0.000 3.8e-15 -19.0127 -18.6282 -18.0507
2 3000.85 146.36 25 0.000 4.1e-15 -18.9444 -18.68* -18.283*
1 2927.67 4030.9 25 0.000 5.6e-15 -18.6346 -18.4904 -18.2738
0 912.234 2.0e-09 -5.8343 -5.81027 -5.77417
lag LL LR df p FPE AIC HQIC SBIC
Sample: 1/4/2008 - 12/13/2013 Number of obs = 311
Selection-order criteria
> axlag(15)
. varsoc LNSPREFMKT LNWTIBRENTSPREAD LNCRACKSPREAD LNPERCUTILZE LNMIDWESTSTORAGE if year >= 2008, m
var LNWTIBRENTSPREAD LNSPREFMKT LNCRACKSPREAD LNPERCUTILZE LNMIDWESTSTORAGE
if year >= 2008, lags(1/5) exog(L6.LNWTIBRENTSPREAD L6.LNSPREFMKT L6.LNCRACKSPREAD
L6.LNPERCUTILZE L6.LNMIDWESTSTORAGE )
21
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-2
0
2
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-4
-2
0
2
-6
-4
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0
2
-6
-4
-2
0
2
0 2 4 6 8 10 12 14 16 18 20 0 2 4 6 8 10 12 14 16 18 20
0 2 4 6 8 10 12 14 16 18 20 0 2 4 6 8 10 12 14 16 18 20
ord56, LNCRACKSPREAD, LNSPREFMKT ord56, LNPERCUTILZE, LNSPREFMKT
ord56, LNSPREFMKT, LNSPREFMKT ord56, LNWTIBRENTSPREAD, LNSPREFMKT
95% CI impulse-response function (irf)
step
Graphs by irfname, impulse variable, and response variable
22
.
LNPERCUTILZE ALL 69.675 39 0.002
LNPERCUTILZE LNCRACKSPREAD 45.008 13 0.000
LNPERCUTILZE LNSPREFMKT 11.487 13 0.570
LNPERCUTILZE LNWTIBRENTSPREAD 14.405 13 0.346
LNCRACKSPREAD ALL 204.93 39 0.000
LNCRACKSPREAD LNPERCUTILZE 58.806 13 0.000
LNCRACKSPREAD LNSPREFMKT 27.597 13 0.010
LNCRACKSPREAD LNWTIBRENTSPREAD 78.5 13 0.000
LNSPREFMKT ALL 108.89 39 0.000
LNSPREFMKT LNPERCUTILZE 33.296 13 0.002
LNSPREFMKT LNCRACKSPREAD 24.045 13 0.031
LNSPREFMKT LNWTIBRENTSPREAD 38.371 13 0.000
LNWTIBRENTSPREAD ALL 129.08 39 0.000
LNWTIBRENTSPREAD LNPERCUTILZE 42.492 13 0.000
LNWTIBRENTSPREAD LNCRACKSPREAD 25.258 13 0.021
LNWTIBRENTSPREAD LNSPREFMKT 38.898 13 0.000
Equation Excluded chi2 df Prob > chi2
Granger causality Wald tests
. vargranger
23
H0: no autocorrelation at lag order
12 26.2657 25 0.39353
11 44.5683 25 0.00936
10 26.7047 25 0.37080
9 52.8692 25 0.00093
8 33.4461 25 0.12029
7 23.0981 25 0.57183
6 34.0604 25 0.10662
5 30.4001 25 0.20966
4 20.0163 25 0.74597
3 40.9199 25 0.02341
2 25.7369 25 0.42174
1 27.3908 25 0.33665
lag chi2 df Prob > chi2
Lagrange-multiplier test
var LNWTIBRENTSPREAD LNSPREFMKT LNCRACKSPREAD LNPERCUTILZE
LNMIDWESTSTORAGE if year >= 2008, lags(1/5)
exog(L6.LNWTIBRENTSPREAD L6.LNSPREFMKT L6.LNCRACKSPREAD
L6.LNPERCUTILZE L6.LNMIDWESTSTORAGE )
24
-6
-4
-2
0
2
0 2 4 6 8 10 12 14 16 18 20
ord64, LNWTIBRENTSPREAD, LNSPREFMKT
95% CI impulse-response function (irf)
step
Graphs by irfname, impulse variable, and response variable
25
Exogenous: _cons
Endogenous: LNWTIBRENTSPREAD LNSPREFMKT LNCRACKSPREAD LNPERCUTILZE
12 2297.53 34.591* 16 0.005 1.6e-11 -13.5147* -12.5726 -11.1578
11 2280.24 34.628 16 0.004 1.6e-11 -13.5064 -12.6412 -11.3419
10 2262.93 40.054 16 0.001 1.6e-11 -13.4979 -12.7096 -11.5258
9 2242.9 30.297 16 0.017 1.7e-11 -13.472 -12.7606 -11.6923
8 2227.75 24.619 16 0.077 1.7e-11 -13.4775 -12.843 -11.8902
7 2215.44 32.96 16 0.007 1.6e-11 -13.5012 -12.9437 -12.1063
6 2198.96 26.967 16 0.042 1.6e-11 -13.4981 -13.0175 -12.2956
5 2185.48 59.524 16 0.000 1.6e-11* -13.5143 -13.1106* -12.5042
4 2155.71 59.58 16 0.000 1.7e-11 -13.4258 -13.099 -12.6081
3 2125.92 53.017 16 0.000 1.9e-11 -13.3371 -13.0872 -12.7118
2 2099.42 113.42 16 0.000 2.0e-11 -13.2696 -13.0965 -12.8367*
1 2042.7 2655.4 16 0.000 2.6e-11 -13.0077 -12.9116 -12.7672
0 715.022 1.2e-07 -4.57249 -4.55326 -4.52439
lag LL LR df p FPE AIC HQIC SBIC
Sample: 1/4/2008 - 12/13/2013 Number of obs = 311
Selection-order criteria
. varsoc LNWTIBRENTSPREAD LNSPREFMKT LNCRACKSPREAD LNPERCUTILZE if year >= 2008, maxlag(12)
26
H0: no autocorrelation at lag order
12 26.5965 16 0.04619
11 29.2164 16 0.02252
10 21.5024 16 0.16000
9 42.4121 16 0.00034
8 32.4165 16 0.00882
7 13.9000 16 0.60617
6 25.5668 16 0.06044
5 27.8213 16 0.03321
4 16.3840 16 0.42650
3 40.2786 16 0.00071
2 17.4063 16 0.35979
1 28.2405 16 0.02959
lag chi2 df Prob > chi2
Lagrange-multiplier test
. varlmar, mlag(12)
27
-6
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0
2
0 2 4 6 8 10 12 14 16 18 20
ord68, LNWTIBRENTSPREAD, LNSPREFMKT
95% CI impulse-response function (irf)
step
Graphs by irfname, impulse variable, and response variable
28
Exogenous: _cons
LNTBILL4Wraw
Endogenous: LNWTIBRENTSPREAD LNSPREFMKT LNCRACKSPREAD LNPERCUTILZE
12 2041.18 51.825* 25 0.001 1.0e-11 -11.1651 -9.69912 -7.49749
11 2015.26 49.255 25 0.003 1.0e-11 -11.1593 -9.81342 -7.79224
10 1990.64 55.501 25 0.000 9.9e-12 -11.1617 -9.93598 -8.09526
9 1962.89 37.124 25 0.056 1.0e-11 -11.144 -10.0385 -8.3782
8 1944.32 34.174 25 0.104 9.6e-12 -11.1854 -10.2 -8.72023
7 1927.24 53.221 25 0.001 9.1e-12 -11.2363 -10.3711 -9.07174
6 1900.63 34.319 25 0.101 9.2e-12 -11.2259 -10.4809 -9.36201
5 1883.47 74.658 25 0.000 8.7e-12* -11.2763* -10.6515 -9.71306
4 1846.14 81.31 25 0.000 9.4e-12 -11.197 -10.6923 -9.9344
3 1805.48 71.821 25 0.000 1.0e-11 -11.0964 -10.7118 -10.1344
2 1769.57 132.32 25 0.000 1.1e-11 -11.0262 -10.7618* -10.3648
1 1703.41 2982.5 25 0.000 1.5e-11 -10.7615 -10.6173 -10.4007*
0 212.186 1.8e-07 -1.33239 -1.30835 -1.27226
lag LL LR df p FPE AIC HQIC SBIC
Sample: 1/4/2008 - 12/13/2013 Number of obs = 311
Selection-order criteria
> lag(12)
. varsoc LNWTIBRENTSPREAD LNSPREFMKT LNCRACKSPREAD LNPERCUTILZE LNTBILL4Wraw if year >= 2008, max
29
-4
-2
0
2
0 2 4 6 8 10 12 14 16 18 20
ord69, LNWTIBRENTSPREAD, LNSPREFMKT
95% CI impulse-response function (irf)
step
Graphs by irfname, impulse variable, and response variable
30
H0: no autocorrelation at lag order
12 40.9746 25 0.02310
11 38.5408 25 0.04093
10 36.8234 25 0.06000
9 48.8739 25 0.00293
8 33.7709 25 0.11290
7 29.9612 25 0.22575
6 41.9948 25 0.01800
5 38.3693 25 0.04256
4 30.1108 25 0.22017
3 44.2030 25 0.01029
2 38.0334 25 0.04592
1 43.2078 25 0.01328
lag chi2 df Prob > chi2
Lagrange-multiplier test
. varlmar, mlag(12)
Other reasons for shutdown in
European refineries
• Ageing
• Saudi refineries
31
U.S. Refineries and pipelines
32
