1. The Relationship between US Rig Count and BRENT WTI Spread
Presented in the 40th IAEE Conference
Huei-Chu Liao &Shu-Chuan Lin

2. Contents Introduction Literature Review Method Data & Results
Conclusions

3. 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  

4. 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)  

5. Weekly WTI/BRENT Price
2000/1/1~2017/6/8
Data Source:

6. Weekly WTI/BRENT Price
2014/1/1~2017/6/8
Data Source:

7. 資料來源: ICE

8. 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)

9. The Important Factors Influencing Oil Price
資料來源:

10. The Trend of Oil Demand of China
(kb/d)

11. 資料來源: IEA

12. The Distribution Map of Global Oil Production
資料來源: American Petroleum Institute

13. Method: Regression Dependent Variable: BRENT WTI Spread
Independent Variables
US rig count
oil stock
US dollar index
Chinese Oil Demand

14. 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 sample data is shrunk to 307

15. Brent-WTI WTI BRENT STOCK USD index Oil Rig Gas Rig Mean 9.12 78.61
87.73
85.75
407.97
Median
7.08
90.94
105.40
81.92
354.00
Maximum
28.33
112.30
126.62
103.11
936.00
Minimum
-1.52
28.14
27.76
73.48
316.00
81.00
Std. Dev.
7.65
24.88
30.09
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
Observations
308

18. Model 1
Model 2

19. 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
(-5.33) *** -
BRENT
0.194 (5.84) ***
STOCK
0.001 (0.81)
0.001 (2.13) **
USI
(-7.04) ***
0.192 (1.52)
Constant
(4.65) ***
(-2.81) ***
Observations
307
R-squared
65.3%
65.9%
NOTE：() is t value, ***、**and *means1%、5% and 10% significant level.

20. 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

21. 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.

22. The Recent Development of Oil Production in the US

23. Thank You for Your Attention!