The Relationship between US Rig Count and BRENT WTI Spread
Huei-Chu Liao
Department of Economics, TamKang University, [email protected]
Shu-Chuan Lin
Chinese Petroleum Corporation
Presented in 4oth IAEE Conference, Singapore
JEL classification: Q41
Keywords: WTI, Brent, Spread, Rig Count
1. Introduction
West Taxes Intermediate Crude Oil price (WTI) has been a famous benchmark price in the
world. Its largest trade volume in the paper trade market help it to be the leading benchmark
price in the world oil market. Many commercial traders use WTI to hedge the oil price risk.
However, WTI lost its world leading price role beginning in 2012 due to the apparent price
deviation from the other two main benchmark price, i.e. BRENT and DUBAI. Although
BRENT replaced WTI as the new leading benchmark price since 2012, many market players
still prefer to hedge by trading the WTI futures. This kind of trade may incur some risk. First
of all, the hedgers may not avoid their risk if the gap between BRENT and WTI is more volatile.
Secondly, hedgers may even lose more money in some worse cases. Thus it is worthy to
clarify the impact factors and their impact on the gap volatility between the BRENT and WTI.
More recently, rig count in North America is found to be an important factor to influence the gap
volatility.
The magnitude of rig count is used to judge the activities of upstream oil companies.
Usually, the rotary rig count increases if oil price is moving up, and then adds up the oil
production. Thus, some literature use oil price and rig count as two important independent
variables to explain the oil production and find significant impacts(Apergis et al., 2016; Khalifa
et al., 2016). However, these authors would like to argue that the linkage (oil price↑→rig count
↑→ oil production↑) above may be suitable in explaining the past history but not current
world due to the rapid information transition and the fast trade mechanism in the financial
market today. In fact, rather than the positive linkage above (oil price↑→rig count↑), these
authors find more complicated linkage in recent data. Opposite to the past analysis, the
variable of rig count today turns out to be an important variable to explain the movement of oil
price (rig count↑→ oil production↑→ oil price↓). Especially the quick response of shale oil
and gas production in North America. These authors believe that more financial market players
would expect a lower WTI price as they found the number of rig count is increasing in North
America. While the shale production is barely no impact on the BRENT since current shale
production is still concentrated in the North America. Consequently, we can observe the
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extended gap BRENT and WTI (i.e. the BRENT WTI spread).
The purpose of this paper is to examine the relationship between US rig count and BRENT
WTI spread. The methodology is explained in Section 2. Data and empirical results will be
illustrated in the Section 3. Conclusions and Remarks are in the Section 4.
2. Methodology
After reviewing many related articles, these authors found no formal articles investigate
the relationship between US rig count and BRENT WTI spread. Thus, we tackle this issue
step by step as below:
1)Draw some trend figures for WTI & Brent prices and BRENT WTI Spread.
2)Implement some simple statistical analysis.
3)Draw some trend figures for US rig count.
4)Implement some simple statistical analysis for US rig count.
5)Implement some regression analysis for US rig count, WTI, Brent, BRENT WTI Spread and
some related variables such as the oil stock, US dollar index.
6)Evaluate the relationship among US rig count, WTI, Brent, and BRENT WTI Spread.
7)Conclude and make some suggestions.
3. Data and Empirical Results
3.1 Data Collection and Some Findings
We collect the weekly data series of BRENT, WTI oil price, US oil stock and the US dollar
index from Energy Information Administration (EIA). While the weekly data series are
collected from BAKER HUGHES. Due to the data limitation of rig count, our data are only
collected from February 4, 2011 to December 23, 2016 since rig count for oil and gas is
separated only after February 4, 2011. Totally we have 308 samples. Table 1 is our data
characteristics. However, in order to implement some differential analysis (i.e. Pt-Pt-1), our
sample data is shrunk to 307 when we implement our econometric analysis.
Table 1: Data Characteristics
Brent-WTI
WTI
BRENT
STOCK
USD index
Mean
9.12
78.61
87.73
379804.90
85.75
1071.10 407.97
Median
7.08
90.94 105.40 351466.50
81.92
1264.00 354.00
Maximum
28.33
112.30 126.62 512095.00
103.11
1609.00 936.00
Minimum
-1.52
28.14
27.76
301123.00
73.48
316.00
81.00
Std. Dev.
7.65
24.88
30.09
61186.25
8.18
413.23
254.77
Skewness
0.55
-0.55
-0.58
0.85
0.48
-0.51
0.77
Kurtosis
2.17
1.66
1.62
2.21
1.69
1.75
2.52
Jarque-Bera
24.37
38.49
41.79
45.13
33.79
33.15
33.15
Observations
Oil Rig
Gas Rig
308
Except the implementation of sample data analysis above, these authors also tried to
draw many figures to clarifying the relationship between rig count and BRENT WTI spread.
Two Figures are found to be more interesting, which are listed below as Figure 1 and Figure 2.
Figure 1 shows that the variable of rig count is very related to the WTI but seems to be no
relationship with the BRENT WTI spread. However, we find very different results when we
alter the right vertical axis from the WTI price to the BRENT WTI spread as we had drawn in
Figure 2. We find close relationship between rig count and BRENT WTI spread after 2015.
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This finding is consistent to the recent market trade situation.
that WTI is much quicker to response to the rig count news.
More recent information shows
2500
160
140
2000
120
100
1500
80
60
1000
40
20
500
0
0
7/24/1998
4/19/2001
1/14/2004 10/10/2006 7/6/2009
rig
4/1/2012
B-W
12/27/2014 9/22/2017
-20
6/18/2020
WTI
Figure 1: US Rig Count, WTI and BRENT WTI Spread
Figure 2: Weekly Relationship between US Rig Count and BRENT WTI Spread (2009~2016)
3.2 Empirical Results
Basic on the above simple data and figures analysis, we implement further for some
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econometric analysis by considering other impact factors. Finally, we select two models
show below as the Model 1 and Model 2. The empirical results are listed in Table 2
Model 1
B  W    1 OILRAG   2 GASRIG  3 WTI   4 STOCK  3 USDX  
Model 2
B  W    1 OILRAG   2 GASRIG  3 BRENT   4 STOCK  3 USDX  
Table 2 Empirical Results for BRENT-WTI Spread (Dependent Variable )
VARIABLES
Model 1
Model 2
OILRIG
0.004 (1.97) **
0.001 (0.64)
GASRIG
0.013 (4.17) ***
0.017 (5.80) ***
WTI
-0.180 (-5.33) ***
BRENT
-
0.194 (5.84) ***
STOCK
0.001 (0.81)
0.001 (2.13) **
USI
-0.839 (-7.04) ***
0.192 (1.52)
Constant
79.536 (4.65) ***
-48.627 (-2.81) ***
Observations
307
307
R-squared
65.3%
65.9%
NOTE：() is t value, ***、**and *means1%、5% and 10% significant level.
The results of Model 1 in Table 2 indicate that both the oil rig count and gas rig count have
positive and significant impact on the BRENT-WTI Spread. This represents that the increase
of rig count would expand the gap between BRENT and WTI. Unfortunately, only the gas rig
count has significant positive impact on the BRENT-WTI Spread in Model 2. This results may
imply that we need to do more works for discover more evidence.
4. Conclusion and Remarks
WTI and Brent are two important benchmark prices for both the oil spot market, futures
market and the derivative market. In order to reduce the holding risk, many people use
different kinds of financial tools to hedge. In recent years, the option of BRENT WTI Spread
is also very popular especially in the period from 2011 to 2013 due to the shale production
boom in US. It is believed by these authors that the shale production boom is very likely to
occur again because of the claim of the new US president Trump. This paper examines all
related articles and use some econometric techniques to find the relationship between US rig
count and the BRENT WTI spread. Current results find the time series data of rig count has
positive and significant relationship with BRENT WTI spread from 2011 to 2016. Hope these
findings can shed some light for explaining the changing differential between WTI and Brent.
Current shortage of this paper is the failure for capturing the impact of pipeline
construction in North American. Since the large gap between WTI and BRENT is mainly due
to the transportation congestion of Midland North America. Without capturing the pipeline
construction impact, the regression coefficients may be biased estimated. Although we still
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need to do more research for robust results, most of our empirical findings reveal that rig count
has positive and significant impact on the BRENT WTI Spread, which should be notified by the
market players.
Major References
Apergis, N., Ewing, B. T., Payne, J. E., A time series analysis of oil production, rig count and
crude oil price: Evidence from six U.S. oil producing regions, Energy, 97, pp. 339-349,
2016
EIA, “What drives Crude Oil Prices? Financial Markets” June 6, 2014 retrieved from
http://www.eia.gov/finance/markets/financial_markets.cfm
Guthrie Jonathan, Opec decision threatens US shale industry investment, The Financial
Times, Nov 27, 2014
Khalifa, A.,Caporin, M., Hammoudeh, S., The relationship between oil prices and the rig
counts: A one-way street with, 39th IAEE Conference, Noway, Bergen, 2016.
Raval Anjli and Hume Neil, Oil plunges as OPEC tests the mettle of US shale industry, The
Financial Times, Nov 28, 2014
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