Spring-Summer Research Paper

Dynamics of North American Crude Oil Price Differential

Jing Wen Zhang

August 14, 2015

Introduction

The crude oil price differential is defined as per barrel price differences between two types of

crude oil. There are hundreds types of crude oil produced in Canada, and form price differentials

with most commonly watched price benchmarks: West Texas Intermediate (WTI)1 in North America.

Due to data availability and quality issues, studying every single price differential is an inefficient and

difficult task. It is better to classify these oils into light and heavy crude with one key criteria: crude

oil density2. This classification not only solves the data availability problem, but also reflects the real

Canadian crude oil industry. Unlike many countries who focuses on produce certain type of crude oil,

both light and heavy crude oil represent large shares of Canadian crude oil productions. The price dif-

ferences between Canadian crude oils and WTI have major impacts on the domestic energy industry.

Large price differentials usually indicate Canadian oil producers receive discounted prices compared

to American counterparts; on the other hand, it benefits Canadian refiners as they have lower cost

of production. Fluctuations in the price differentials also affect the oil industry. Identify clear trend

in movements of price differentials can help industry participants predict the profitability of oilfield

projects, better informed when scheduling production levels and oil transportations, hedge against

potential hazardous price movements. This paper studies relationship between Canadian crude oil

1WTI is high quality sweet light crude oil.2Light crude has lower density, flows freely at room temperature, contains higher level of energy molecules. It’s

usually more expensive than heavy crude oil.

prices and WTI price, investigate whether Canadian crude oil prices following the movement of WTI.

There are many literatures studying the relationship between crude oil prices. Adelman (1984)

first suggested the crude oil globalization hypothesis that the world oil market is “one great pool”. It

means different types of oil price should follow each other’s movement and there are certain market

acceptable price gaps exist between different types of crude oils. Later studies (Sauer, 1994; Gulen,

1997 & 1999) investigate global major oil benchmark prices with econometric tests confirmed Adel-

man’s globalization hypothesis is true, and long-run relationships exist in the global oil market. The

estimated long-run price differential levels represent market-accepted price gaps3 between different

types of crude oils. This paper referring these market-accepted price differential levels as equilibrium

levels, as they are representing the long-run relationship between crude oil prices. It is important to

note empirical evidences from existing studies do not imply that we will observe a fixed price gap

between the two types of crude oil over time. Price differentials fluctuate for many reasons: market

supply and demand may differ each day, transportation cost varies over time4, fluctuation in crude

oil qualities5, and geopolitical factors also have a major impact on oil prices, etc. Whenever market

observes a price differential grew excessively large/small or experienced a swiftly change, an adjust-

ment process may occur: market tries to push the price differential toward equilibrium level in the

following periods. This paper focuses on testing the exist of long-run relationship between Canadian

oil prices and WTI, examines who does most of the adjustment, and when will adjustment process

occur to revert price gaps toward equilibrium levels.

There are several studies investigate the adjustment process in the global oil market. Their conclu-

sions offer limited help to predict features of crude oil price differentials in North America. Ghoshray

and Trifonova (2014) identifies the qualities of crude oils are not a good predictor on adjustment pro-

cesses on the international stage. Hammoudeh et al. (2008, 2010) found more recognized leader does

3The link between the two types of crude oil price can be represented as P1 = T +Q+P2, as T is transportation costand Q denotes quality differences between two types of oils. The price differential is T +Q in this simplified case.

4Transportation cost is determined by transportation fuel costs; availability of pipelines, oil tankers, and railroad tanks.5Oil quality varies each day, even from the same oil-well, which will affect the price of crude oil.

most of the adjustment to revert the price gap toward equilibrium levels. These finds are contrary to

our expectations of the North American market: heavy crude should follow the price movement of

light crude oils, and Canadian oils should follow WTI’s price movement. We have reason to suspect

existing studies’ results are not applicable to the North American energy market due to its unique-

ness: relatively low crude oil transportation cost between two nations; limited geopolitical restriction

as Canada is exempted from U.S. oil export ban legislation; high-level of integration in the regional

crude oil industries6. Therefore, it is necessary to study North American crude oil market indepen-

dently.

Adjustment processes may differ under different market conditions. This study uses two groups

of tests to investigate market responses under different scenarios. The first group of tests examines

different levels of responses from crude oil prices when the price differential exceeds or below a cer-

tain threshold. It uses error-correction models to estimate how do oil prices response to last period

price gaps. The existences of threshold effect in major international crude oil price differentials are

identified by Fattouh (2010). The test result shows Canadian crude oil prices do most of the adjust-

ment, Canadian heavy crude only actively reduces the price gap with WTI when the it exceeds certain

levels. The second group of tests focuses on widening and narrowing in price differentials, which are

closely watched by commodity markets. The test analyses how does the market response to observed

widening and narrowing in price differentials. The results confirm the leadership of WTI as Canadian

crude oils adjust more actively. Test result also reveals there is no universal responses in price dif-

ferential movements: Canadian Light crude does most of the adjustment when WTI–Canadian Light

narrows, and Canadian Heavy crude price actively responses to the widening in the price gap of WTI–

Canadian Heavy.

6North American crude oil companies invest and own large amounts of assets in both Canada and the U.S. Infrastruc-tures are designed to transport large amount of oil from Canada to the U.S.

Data

Figure 1: Price Differentials

0.00

0.05

0.10

Monthly Log Difference of WTI and Canadian Light Crude

Time

Log

diffe

renc

e

Jan, 86 Jan, 92 Jan, 99 Jan, 05 Jan, 11 May, 15

0.05

0.15

0.25

Monthly Log Difference of WTI and Canadian Heavy Crude

Time

Log

diffe

renc

e

Jan, 86 Jan, 92 Jan, 99 Jan, 05 Jan, 11 May, 15

0.05

0.15

0.25

Monthly Log Difference of Canadian Light and Heavy Crude

Time

Log

diffe

renc

e

Jan, 86 Jan, 92 Jan, 99 Jan, 05 Jan, 11 May, 15

This paper focuses on price differences between Canadian crude oil prices and WTI. Natural

Resources Canada provides reliable dataset for this study, it contains monthly average spot prices of

crude oils at respective terminal locations: Canadian Light at Edmonton, Canadian Heavy at Hardisty,

and WTI at Cushing. Three price differentials are constructed: WTI – Canadian Light represents the

price gap between similar qualities of oil; WTI – Canadian Heavy is the price spread between differ-

ent qualities of oils; and Canadian Light – Canadian Heavy is the domestic price differential, it allows

us to study the domestic price adjustment process. This paper uses data between January 1986 and

May 2015 for the analysis. All prices are in Canadian dollars and price differentials are in logarithm

differences, Figure 1 shows price movements of the series. WTI–Canadian Heavy and Canadian

Light–Canadian Heavy shares similar price differential levels and movements. WTI–Canadian Light

has higher volatilities in recent years. All three price differentials are bounded overtime, which in-

dicates there could exist long-run relationships between these oil prices. The plot also shows price

differential is a non-constant series, it varies throughout data’s time frame.

Table 1: Descriptive StatisticsMean Maximum Minimum Standard

Deviation

Spot PricesWTI: Cushing, Oklahoma 50.69 137.13 16.65 30.02Canadian Light: Edmonton, Alberta 47.83 138.03 15.83 28.66Canadian Heavy: Hardisty, Alberta 39.89 121.74 11.69 25.72

Price DifferentialsWTI–Canadian Light 0.02760 0.12450 -0.02479 0.01974WTI–Canadian Heavy 0.11730 0.29740 0.02873 0.04708Canadian Light–Canadian Heavy 0.08970 0.26080 0.01496 0.04294

Notes: All descriptive statistic for Benchmarks are in Nominal CDN dollars. Price spread are in log-differences.

Table 1 summarizes descriptive statistics for three crude oil spot prices and three price differen-

tials. The WTI has the highest average price in our samples as it has the best quality among studied

crude oils. Canadian Heavy crude oil receives the lowest average price as it contains lower levels of

useful hydrocarbon molecule content. The quality differences also affected price differentials. The

WTI–Canadian Light spread has the lowest average, as two crude oils have similar qualities. The

price differentials of Canadian Heavy–WTI and Canadian Heavy–Canadian Light are relatively large.

These high price gaps can be contributed to quality differences of oils and refineries’ adjustment cost

if they decide to switch to other types of oil.7

Methodology

Cointegration test is used to examine the existence of long-run relationships between crude oil

prices. The classic example of two drunks’ walk illustrates the idea of cointegration. Suppose we

7Light crude oils are easier to process than heavy crude oils. Additional special equipments are required when refiningheavy crude oils.

have two drunks walking toward some place together. Use x and y to denote respective walking

speed of two drunks. If we observe each person’s movement, x and y could be completely random.

However, the relative speed between two persons: z = |x− y| should be relatively stable over time,

as they are unwilling to deviate from each other too far away. Therefore, we call x and y cointegrate,

from a stationary series z8. Mathematically, we can express the relationship between two drunks as:

z = αx+βy. To simplify the illustration, normalize α =−1, we will have:

x = βy− z (1)

By studying the stationarity property of series z, we can determine whether two drunks move toward

some place together (cointegrate) and from a long-run relationship.

Above mathematical formula resembles the simple linear relationship between two crude oil

prices:

P1 = βP2 + et (2)

Where P1 and P2 are prices of two types of crude oil. The residual represents the price differential of

two crude oil prices. The expected value of et is the long-run equilibrium level of the price differen-

tial. By testing the stationarity of et , we will be able to determine whether two series cointegrate or not.

Bringing back two drunk’s walk example, we are not only interested in whether two people walk

together, but also interested in how do two persons maintain their relative speed, who will increase

or decrease his speed when the relative speed is out of sync. Suppose two people walk in discrete

cases, each person will observe last period’s his own speed change, his friend’s speed change, and

most importantly the relative speed between them. Each person’s decision is how to change his own

speed in the current period to prevent the distance between two people grow too large. The same types

8Staionary in this paper refers to weakly stationary process: a time series’ average and variance do not vary withrespect to time, it means the series is well-behaved and mathematically predictable.

of logic also apply to the crude oil market. Today’s market will decide how much current price will

change, after they take into account of past information on change in P1, change in P2 and price gap

between the two. If two crude oil prices cointegrate and form a long-run relationship, we would like

to know the specific mechanism of the adjustment process. This study uses Vector Error-Correction

Models (VECM) to investigate adjustment processes of crude oil differentials:

∆P1t = c+λ1et−1 +α1∆P1

t−1 +β1∆P2t−1 + εt (3)

∆P2t = c+λ2et−1 +α2∆P1

t−1 +β2∆P2t−1 + εt (4)

Equation (3) and (4) simulated the link between current and last period oil prices. Change in current

price is influenced by information from the past, as market will response to last period price changes

∆P1t−1, ∆P2

t−19 and price differentials et−1. λ is the long-run speed of adjustment, it captures the rate

at which P1 adjusts to push price differentials toward the equilibrium state after observing a shock.

Suppose |λ1|= 0.3, it indicates 30% of the disequilibrium in the last period is reverted in the current

period through changes in P1. The adjustment process is a dynamic multiple period information up-

date process. The Market will try to revert the price differential back toward equilibrium levels in the

following periods.

This paper studies adjustment processes under different market conditions, we can expand the

VECM model to include two types of threshold effects to simulate market conditions. It uses dummy

variable D to categorize residuals into two groups: D · e and (1−D) · e. The general Threshold

Autoregressive (TAR) VECM model is:

∆Pit = ci +D ·λ 1,1et−1 +(1−D) ·λ 1,2et−1 +

L

∑k=1

α1k ∆Pi

t−k +L

∑k=1

β1k ∆P j

t−k + εit (5)

∆P jt = c j +D ·λ 2,1et−1 +(1−D) ·λ 2,2et−1 +

L

∑k=1

α2k ∆Pi

t−k +L

∑k=1

β2k ∆P j

t−k + εj

t (6)

i, j represent types of crude oil. The length of Lagged values of ∆Pit−k and ∆P j

t−k can be determined by

9Lagged differences of oil price: ∆Pt−1 = Pt−1−Pt

Bayesian Information Criterion10. This specification allows us to investigate the speeds of adjustment

when D = 1 and D = 0, respectively. The dummy variable D is defined by following specifications:

1. Level based model:

For testing adjustment processes with respect to price differential above and below certain threshold,

the dummy indicator is defined as:

D =

0 i f et−1 < τ

1 i f et−1 > τ

(7)

Where τ is the estimated threshold level in the tested price differential. D will take on the value

of one when the price differential is above the threshold, zero when the price differential below the

threshold. This definition allows us to test how does crude oil price response to high and low levels

of price differentials. It is a necessary specification, as high levels of arbitrage may not exist in the

market until the price differential reaches certain profitable level; therefore, market responses will be

different for price differentials above and below the threshold.

2. Momentum based model:

For testing the adjustment process in response to the momentum in the price differential, we define D

as:

D =

0 i f ∆et−1 < τ

1 i f ∆et−1 > τ

(8)

τ is the estimated threshold level of movement in lagged price differentials et−1. D will take on the

value of one when the price differential was widened in the last period, zero if the price differential

was narrowed. This definition allows us to test how does crude oil price response to widening and

narrowing price differentials. For example, if estimated |λ 1,1| is greater than |λ 1,2| in equation (5),

10The length of autoregressive models have significant influence on statistical test results. This paper uses the BayesianInformation Criterion to determine the optimal length for each model.

it indicates the price of type i oil is more actively adjusted when the price is widening. This type of

asymmetric response to price movements has been found in major international oil price differentials

by Hammoudeh et al. (2008).

Empirical Results

The procedure for empirical test as follows: first, test to see whether data are fit for cointegration

test; second, use cointegration test to examine the existence of long-run relationships; third, after con-

firming the existence of long-run relationships, use error-correction model to investigate the specific

adjustment process.

The first step is to test whether we can use crude oil prices in cointegration test, as it requires I(1)

non-stationary series11. This paper uses three tests to analyse time series stationarities: Augmented

Dickey-Fuller test (Dickey and Fuller 1981); Phillips Perron test (Phillips and Perron, 1988) which

expands the ADF test allows for heterogeneous error terms; and KPSS test (Kwiatkowski et al., 1992).

Both ADF and PP tests investigate the existence of unit root12 in the time series, KPSS tests the sta-

tionarity of time series directly. Table 2 summarizes the unit root/stationary test results. All three tests

show logarithm prices are non-stationary, and first differences (∆P) are stationary process13. We can

conclude all three crude oil price PWT I, PLight , PHeavy are non-stationary time series data and fit for

cointegration test.

The second step is to use crude oil prices in cointegration test. It is an important step as we

want to know whether there exist long-run relationships between oil prices. The existence of long-

run relationships indicate oil prices follow each other’s movement, and their price differentials are

stationary series. The standard cointegration test following Engle and Granger’s procedure (1987)

11I(1): integrated of order one, non-stationary time series. The first difference of the series ∆Pt = Pt −Pt−1 is I(0),integrated of order zero, a stationary series. It is the necessary to use I(1) time series in cointegration test.

12The existence of unit root signals the time series follows a random walk, as it is non-stationary.13Testing first differences is to confirm logarithm prices are integrated of order one I(1) non-stationary time series data.

Table 2: Unit Root TestsADF PP KPSS

PWT I -1.780 -1.6381 0.517**∆PWT I -12.020** -14.4851** 0.0314

PCanadianLight -2.000 -1.7353 0.441**∆PCanadianLight -11.846** -13.9907** 0.0271

PCanadianHeavy -2.320 -2.0271 0.422**∆PCanadianHeavy -12.607** -14.5991** 0.021

Notes:Numbers are test statistics, ** indicates test statistic is significant at 5% level.5% critical value for ADF test with 345 degrees of freedom is -2.87. 5% critical value for Z statistic used in PP test with347 degrees of freedom is -2.87. 5% critical value for KPSS test is 0.146.Null hypothesis for ADF and PP test is the series has unit root(non-stationary). KPSS test’s null hypothesis is the series isstationary.

analyse the unit root property of residual term; however, it only allows symmetric adjustment. Since

we suspect crude oil price differential’s adjustment process could be asymmetric based on different

levels of price gap and momentums in price differential movement, we need to adjust cointegration

test accordingly. This study adopts Enders and Siklos (2001) threshold cointegration procedure:

∆et = D ·ρ1et−1 +(1−D) ·ρ2et−1 +k

∑i=1

βk∆et−k + ε (9)

Where D =

0 i f et−1 < τ

1 i f et−1 > τ

(10)

D is defined with price differential movement or level, as specified earlier. Table 3 and Table 4 show

test results. Two hypotheses are tested in each specification, the first hypothesis test is the existing

of cointegration relation through Ho,1 : ρ1 = ρ2 = 0. All tested series rejected the null hypothesis

of no cointegration relationship. This result allows us to move forward to examine the adjustment

processes. The second hypothesis test is the asymmetric test as Ho,2ρ1 = ρ2, test results indicate most

series have asymmetric adjustment processes.

Table 3: Level based Cointegration TestsWTI–Canadian Light WTI–Canadian Heavy Canadian Light–Heavy

τ -0.014 0.039 -0.032

ρabove -0.2639 -0.2512 -0.1679

ρbelow -0.3125 -0.1487 -0.0768

ρabove = ρbelow = 0 a 29.791*** 24.972*** 16.314***

ρabove = ρbelow a 0.4127 3.2977* 3.0445*

Lags 1 1 1

Notes:Lags are selected according to Bayesian Information Criterion.Significant levels: 1%(***), 5%(**),10%(*).a F test statistics

The third step is to investigate the specific adjustment process in oil price differentials. It estimates

the speed of adjustments within each type of crude oil and offers insight into which oil price does most

of the adjustment. I use Bayesian Information criterion to decide the lag length in past price changes.

Tests indicate all error correlation models should use lagged length of 1, this result simplifies the

equation (5) and (6) to:

∆Pit = ci +D ·λ 11et−1 +(1−D) ·λ 12et−1 +α

1∆Pi

t−1 +β1∆P j

t−1 + εit (11)

∆P jt = c j +D ·λ 21et−1 +(1−D) ·λ 22et−1 +α

2∆Pi

t−1 +β2∆P j

t−1 + εj

t (12)

λ ’s are speed of adjustment toward long-run equilibrium14, α and β represents oil price’s response to

changes in last period prices.

Level based analysis

This group of tests examines different market responses when price gap exceed or less than an

14As mentioned in the last section, |λ | is the percentage of disequilibrium fixed in current period.

Table 4: Momentum based Cointegration TestsWTI–Canadian Light WTI–Canadian Heavy Canadian Light–Heavy

τ -0.006 0.010 0.005

ρ+ -0.2122 -0.305 -0.1891

ρ− -0.441 -0.141 -0.1024

ρ+ = ρ− = 0 a 34.544*** 27.252*** 16.164***

ρ+ = ρ− a 8.5397*** 7.3221*** 2.767*

Lags 1 1 1

Notes:Lags are selected according to Bayesian Information Criterion.Significant levels: 1%(***), 5%(**),10%(*).a F test statistics

estimated threshold15: dummy variable D takes on the value of one when the price differential moves

above the estimated threshold. Table 5 shows the empirical test results. In WTI–Canadian Light

spread λ Above and λ Below are both significant at 10% level for Canadian Light crude; on the other

hand, the coefficients (λ ) for WTI are non-statistical significant. It indicates Canadian Light is mainly

responsible for maintaining the equilibrium price differential level when price gaps on both sides of

the estimated threshold. F-test confirms there is no significant asymmetric effect around the threshold.

WTI–Canadian Heavy spread presents different results. Canadian Heavy will only actively ad-

just when the price differential exceeds the estimated threshold level. λ above for Canadian Heavy is

statistically significant at 1% with 0.432. It shows whenever large disequilibrium occurs, 43% of the

last period disequilibrium is reduced through the adjustment of Canadian Heavy in the current period.

The lack of other detectable adjustment processes in WTI–Canadian Heavy spread indicate the price

gap can move freely until it exceeds the estimated threshold. This feature is visible in Figure 1: the

WTI–Canadian Heavy price differential fluctuates across time, pull back very quickly when the price

differential grows too large.

15Threshold level is estimated in the Threshold Cointegration Models as the value minimize the results of sum ofsquared errors (SSE).

Table 5: Level based analysisWTI–Canadian Light WTI–Canadian Heavy Canadian Light–Heavy∆p1

t ∆p2t ∆p1

t ∆p2t ∆p1

t ∆p2t

Level based analysisEstimated Threshold Level 0.013 0.039 0.035

λ Above -0.080 0.265 0.108 0.432 0.036 0.191(0.135) (0.149)* (0.070) (0.105)*** (0.081) (0.108)*

λ Below 0.169 0.353 -0.075 0.060 -0.006 0.117(0.173) (0.192)* (0.071) (0.106) (0.084) (0.113)

α1, α2 0.403 0.436 0.154 -0.017 -0.063 -0.372(0.159)** (0.176)** (0.114) (0.170) (0.140) (0.189)**

β 1, β 2 -0.154 -0.065 0.078 0.309 0.282 0.527(0.141) (0.157) (0.078) (0.116)*** (0.107)*** (0.144)***

F-test: λ Above = λ Below 1.137 0.116 3.000 5.502 0.112 0.198p-values 0.287 0.734 0.084* 0.020** 0.738 0.657

Notes:Standard errors are in parentheses.Lags are selected according to Bayesian Information Criterion.Significant levels: 1%(***), 5%(**),10%(*).

The leadership of WTI is confirmed in both price differentials. Canadian Light is more responsive

to low-level price differentials. It is worth to note, Canadian light also sensitive to past movement in

WTI price, as α2 = 0.436 is significant at 5% level. The result shows Canadian Light is sensitive to

movement in WTI price ,and responsive to price gaps with WTI. This result is not unexpected given

WTI’s leading position in high quality light crude oil market.

Momentum(movement) based analysis:

The second group of tests is based on classification of movements in last period price differentials.

The dummy variable D will take on one (zero) if the price differential widened (narrowed) in the last

periods. Table 6 lists the test results. In the WTI–Canadian Light price differential, both crude oil

prices are sensitive to the narrowing of the spread. The estimated values of λ− are statistically sig-

nificant, take on values of 0.315 for WTI, and 0.717 for Canadian Light. It means WTI will attempt

to reduce 31% of the disequilibrium, and Canadian Light is responsible for reducing 71% of the dis-

equilibrium in the following period. The result shows industry participants are very active when the

price gap reduces. This result can be explained with the already low-level price gap between WTI

and Canadian Light. The average spread is 0.02760 from Table 1. It is significantly lower than other

price spreads analysed in this paper. The low-level price spread is sensitive to any reduction in the

spread, the market will response actively to maintain a minimum level of price differentials. Since the

estimated coefficient 0.315 < 0.717, it indicates most of the adjustment come from changes in Cana-

dian Light. The result also shows both current period WTI and Canadian Light responses to lagged

price change in WTI oil prices. It indicates existing information in WTI have significant influence

over itself and Canadian Light crude.

Table 6: Momentum based analysisWTI–Canadian Light WTI–Canadian Heavy Canadian Light–Heavy

∆p1t ∆p2

t ∆p1t ∆p2

t ∆p1t ∆p2

t

Estimated Threshold Level -0.006 0.010 0.005λ+ -0.101 0.133 0.128 0.505 0.044 0.246

(0.116) ( 0.128) (0.080)* (0.120)*** (0.089) (0.120)***

λ− 0.315 0.717 -0.046 0.101 -0.003 0.095(0.185)** (0.204)*** (0.059) (0.089) (0.072) (0.096)

α1, α2 0.440 0.482 0.113 -0.114 -0.075 -0.413(0.159)*** (0.175)*** (0.117) (0.175) (0.145) (0.195)***

β 1, β 2 -0.196 -0.128 0.116 0.400 0.294 0.568(0.143) (0.158) (0.082) 0.123*** (0.113)*** (0.152)***

λ+ = λ− 3.593 5.797 2.894 7.034 0.165 0.920p-values 0.059* 0.017*** 0.090** 0.008*** 0.685 0.338

Notes:Standard errors are in parentheses.Lags are selected according to Bayesian Information Criterion.Significant levels: 1%(***), 5%(**),10%(*).

WTI–Canadian Heavy price differential shows different results: two crude oil prices are sensitive

to widening of the price differential. λ+ = 0.128 for WTI is statistically significant at 10% level, and

λ+ = 0.505 for Canadian Heavy is statistically significant at 1% level. Both crude oil prices resist

to fast widening in price differentials. Similar to WTI-Canadian Light price differential, Canadian

crude does most of the adjustment. It also shows Canadian Heavy responses to its own lagged price

change as β 2 = 0.400, but past WTI movements have little effect on Canadian Heavy’s current price

movement. Momentum based test reveals strong asymmetric effect in the adjustment process for both

crude oil price differentials.

Domestic prices

This study also investigates the adjustment process in domestic price differentials. The result

indicates Canadian Heavy crude oil price follows the price movement of Light crude. Canadian

Heavy crude oil does modest adjustment when the price differential widened, and when the price gap

exceeds a certain level. It is also heavily influenced by lagged price changes in Light Crude and itself.

This result indicates Canadian Heavy crude, as a low quality type of crude oil, have little price setting

power within domestic market.

Conclusion

One of the most prominent feature of test results is less liquid Canadian crude oils do most of the

adjustments. This is contrary to the result of Hammoudeh et al. (2008, 2010) in which they found

most liquid crude oil does most of the adjustment on the international stages. This paper’s result con-

firmed WIT’s role as price setting benchmark in North America, Canadian oil prices actively adjusted

to follow the price movement of WTI in several cases. It is not surprising given the dominant role of

the American economy in the North American energy market.

Price shocks from the American oil market have significant effects on Canadian light crude oil

market. The price of Canadian Light crude shows a tendency to adjust after observing price changes

in WTI and is sensitive to WTI–Canadian Light price differentials. Test results show the combina-

tion of WTI and WTI–Canadian Light spread are good price indicators for Canadian Light crude,

the oil industry can use observed WTI price level and WTI–Canadian Light spread to forecast price

movement in Canadian light crude. Oil producers can predict the price of Canadian light crude oil

to decide production levels and use derivatives to hedge price shocks. Refiners will be able to know

the price of future inputs, it allows them to predict upcoming production costs and schedule buying

orders accordingly. It is also important to midstream companies. Predict future oil price movement

allows oil transportation companies predict demand for their pipelines, oil tankers in the near future.

This will allow midstream companies to improve efficiency in logistics.

Canadian Heavy crude shows limited response to WTI and the WTI–Canadian Heavy spread.

Canadian Heavy oil only sensitive to sharp increase and high levels WTI–Canadian Heavy price

gaps. It is also responsive to its own price changes in the last period. The statistical test results

offer limited support to use WTI and WTI–Canadian Heavy spread as predictors for Canadian heavy

crude oil prices. More study is needed to explore the dynamics of Canadian Heavy crude oil prices.

Additionally, this paper’s results and its applications in the industry could be improved if higher

frequency of data is available.

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