The Relationship between US Rig
Count and BRENT WTI Spread
Presented in the 40th IAEE
Conference
Huei-Chu Liao &Shu-Chuan Lin
Contents
Introduction
Literature Review
Method
Data & Results
Conclusions
2
Introduction (1)
WTI has been the leading benchmark price in
the world oil market.
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.
More recently, rig count in North America is
found to be an important factor to influence
the gap volatility.
3
Introduction (2)
Many market players still prefer to hedge by
trading the WTI futures.
This kind of trade may incur some risk.
1) the hedgers may not avoid risk if the gap
between BRENT and WTI is more volatile.
2) hedgers may even lose more money in some
worse cases.
 →clarify the impact on the gap volatility
between the BRENT and WTI. (rig count)
4
Weekly WTI/BRENT Price
2000/1/1~2017/6/8
Data Source: http://web3.moeaboe.gov.tw/oil102/
Weekly WTI/BRENT Price
2014/1/1~2017/6/8
Data Source: http://web3.moeaboe.gov.tw/oil102/
資料來源: ICE
Literature Review
 Impact Factors (stock , US exchange rate, China oil
demand, …)on Oil Price
(EIA, 2014; Raval Anjli and Hume Neil, 2014;
Guthrie Jonathan, 2014, …)
 The magnitude of rig count is used to judge the
activities of upstream oil companies.
oil price↑→rig count↑→ oil production↑
(Apergis et al., 2016; Khalifa et al., 2016)
8
The Important Factors Influencing Oil Price
資料來源:https://www.eia.gov/finance/markets/crudeoil/financial_markets.php
Jan2004
Aug2004
Mar2005
Oct2005
May2006
Dec2006
Jul2007
Feb2008
Sep2008
Apr2009
Nov2009
Jun2010
Jan2011
Aug2011
Mar2012
Oct2012
May2013
Dec2013
Jul2014
Feb2015
Sep2015
Apr2016
Nov2016
The Trend of Oil Demand of China
(kb/d)
14000
12000
10000
8000
6000
4000
需求
總進口
2000
0
資料來源: IEA
The Distribution Map of Global Oil
Production
資料來源: American Petroleum Institute
Method: Regression
Dependent Variable:
BRENT WTI Spread
Independent Variables
•
•
•
•
US rig count
oil stock
US dollar index
Chinese Oil Demand
Data
weekly data series of BRENT, WTI oil price, US
oil stock and the US dollar index from Energy
Information Administration (EIA).
weekly data series are collected from BAKER
HUGHES.
02.04, 2011 to 12.23, 2016 since rig count for
oil and gas is separated only after February 4,
2011.
Totally we have 308 samples.
some differential analysis (i.e. Pt-Pt-1), our
14
sample data is shrunk to 307
BrentWTI
BREN
WTI
STOCK
T
USD
Oil Rig Gas Rig
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
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
JarqueBera
24.37
38.49 41.79
45.13
33.79
33.15
33.15
Observations
308
413.23 254.77
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
B-W
-20
4/1/2012 12/27/2014 9/22/2017 6/18/2020
WTI
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) ***
WTI
-0.180 (-5.33) ***
BRENT
-
0.017 (5.80) ***
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.
Conclusions (1)
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.
the shale production boom is very likely to
occur again because of the claim of the new
US president Trump.
rig count has positive and significant
relationship with BRENT WTI spread from
2011 to 2016
20
Conclusions (2)
Current shortage of this paper
failure for capturing the impact of pipeline
construction in North American.
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.
that rig count has positive and significant
impact on the BRENT WTI Spread, which
should be notified by the market players.
21
The Recent Development of Oil
Production in the US
Thank You for Your
Attention!