Dynamics of the Heavy-Light Spread in the N. American Oil Marketjparsons/Presentations/Heavy...

Dynamics of the Heavy-Light Spread

in the N. American Oil MarketRomain H. Lacombe and John E. Parsons

MIT Energy and Environmental Policy Workshop6-7 December 2007

2

The Issue

North American crude oil marketsLight sweet crude: global light marketHeavy sour crude: Mexican and Venezuelan oilNew entrant: heavy products from Canadian oil sands

Question: how do heavy and light crude prices relate?Is there a reliable long run equilibrium?• Fixed percent spreads? • Fixed differentials?• Other?

What about the dynamics of the market?• Short-run responses to shocks?• Long-run shifts?

3

The Data

Focus on three key marker crudes: WTI, LLB and MayaWest Texas Intermediate Blend global light crude marketLloydminster Blend Canadian heavy crude market (benchmark for Diluted Bitumen from the Athabasca oil sands)Maya Blend Central and South Am. heavy crude market

Data: weekly prices for the 1998 - 2007WTI: NYMEX front month contract for delivery at Cushing, OKLLB: spot contract for delivery at Hardisty, Alb.Maya: sold CIF to USGC based on Pemex marked price

4

Historical Evolution of Prices

02

04

06

08

01

00

1998w1 2000w1 2002w1 2004w1 2006w1 2008w1Time (weekly)

WTI 1st Month NYMEX Maya BlendLloydminster Blend

1998/01:WTI: 16.63Maya: 11.12LLB: 6.70

2007/11:WTI: 95.10Maya: 81.98LLB: 67.02

Global rise in prices

Volatility periods:

KatrinaIrak9/11

5

Absolute Spreads: WTI-Maya and WTI-LLB

01

02

03

04

0


WTI-LLB Differential Fitted valuesWTI-Maya Differential Fitted values

01

02

03

04

0


WTI-LLB Differential Fitted valuesWTI-Maya Differential Fitted values

Shell shutdowns

Suncor shutdowns

Hurricane Wilma

1998/01:LLB: -9.93Maya: -5.51

2007/11:LLB: -28.08Maya: -13.12

6

20

40

60

80

100


Fitted values Fitted valuesMaya/WTI Spread (%) LLB/WTI Spread (%)

Percent Spreads: Maya/WTI and LLB/WTI

1998/01:LLB: 40.9%Maya: 64.6%

2007/11:LLB: 63.5%Maya: 82.8%

9/11Hurricane Keith?

Hurricanes Ivan & Jeanne

Hurricane Katrina

7

Early Conclusions

No simple long run equilibrium relationshipFixed price differentials exhibit heteroskedasticityFixed percent spreads are shifting with time

Differential shocks impact all marketsGlobal shocks have differentiated local effectsLocal shocks have repercussions on other markets

Need for thorough time series analysis

Time Series Analysis

9

Estimating a Model of Price DynamicsProblem in inference on time trended time series…

very easy to erroneously find a relationship between 2 series if they are not stationaryE.g. oil prices went up while steel price went up too: Causality? Correlation?

10


very easy to erroneously find a relationship between 2 series

One solution is to first detrend the series, e.g., by taking first differences

this works sometimes, but the underlying problem is sometimes more subtle and undermines the validity of this simple solutionE.g. for oil and steel -- if energy prices impact steel price, the following structure may prevail:

In that case, differencing ignores long run equilibrium between the variables due to the shared stochastic trend

εε

βα

OilEnergyOil

SteelEnergySteel

PPPP

+=+=

11


Very easy to erroneously find a relationship between 2 series

One solution is to first detrend the series, e.g., by taking first differences

This works sometimes, but the underlying problem is sometimes more subtle and undermines the validity of this simple solution

Resolution: cointegration analysisSearch for the cointegration vector… a more robust search through a broader universe of possible stationary linear combinations of the non-stationary variablesIf variables cointegrated…

stationary reversal to a long run equilibriumPP OilSteel βα

−

12

Traditional Diagnostics

Standard VAR(p) model:

Test lag order p

Standard estimation method: VAR(p) modelWorks for stationary variablesStandard form assumes no contemporaneous effect of variables on each other Structural form (informed by standard form) can allow contemporaneous effects

ε t

p

i itit PAP +∑==

−1

Structural VAR(p) model:

ε t

p

i ititt PABPP +∑+==

−1

Structural assumptions

13

Traditional Diagnostics Estimation method for non-stationary variables: VECM model

First differences of VAR(p) model in standard formImplies linear combination of lagged price levels is stationaryHence need to choose a constraint on rank Johansen test

VECM(p,r) model:

ε tt

p

i itit PPP ++∑= ΠΔΠΔ −

−

=− 1

1

1

Test lag order p

Johansen test for rank r Stationary linear combination

CECM(p,r) model:

ε tt

p


−

=− 1

1

0Structural

assumptions

14

Overview of the Path for Estimation

Standard VAR(p) model:

Test lag order p

ε t

p

i itit PAP +∑==

−1

Structural VAR(p) model:

ε t

p

i ititt PABPP +∑+==

−1

VECM(p,r) model:

ε tt

p


−

=− 1

1

1

Test lag order p

Johansen test for rank r

CECM(p,r) model:

ε tt

p


−

=− 1

1

0

Unit root test

Stationary VAR Integrated VECM

15

Traditional Diagnostics: Unit Root Tests

Philipps-Perron unit root testNull hypothesis: price variables exhibit a unit root

First differences are found stationary by the same test

Conclusion: price variables are integrated of order 1 they behave like random walks

Therefore… need for co-integration analysis!VECM to reveal long run equilibrium and link with short run dynamicsCECM if specific structure is found

79.29%67.94%90.25%

P-value for null hypothesis

log Mayalog LLBlog WTIVariable

Variables exhibit unit roots

16

Bottom-line: Cointegration of Crude PricesPart #1. Long-run equilibrium relationship: co-integration

framework between WTI, Maya and LLBDiagnostics: lag order 4, rank 2Reveals long run equilibrium

Part #2. Linking short-run to long-run dynamics: Vector Error Correction Model (VECM)

Highlights relationship between long run equilibrium and short rum dynamicsReveals underlying asymmetry between WTI and the other variables

Part #3. Imposing structure on short run dynamics: Conditional Error Correction Model (CECM)

WTI is assumed exogenousWe study its contemporaneous and long-run effect on heavy crudes prices

17

Part #1Bottom Line: Long-run Equilibrium

Long run equilibrium between LLB and WTI:log LLB = (- 1.0613) + (1.115015) log WTI

Predicted ‘equilibrium’ in price levels:

0

20

40

60

80

100

30 40 50 60 70 80 90 100

WTI price

LLB

pric

e

LLBWTI

@$30: 51% spread to

WTI

@$100: 59% spread to

WTI

18

Part #1Bottom Line: Long-run Equilibrium (cont.)

Historical pricesActual and predicted prices Departure from equilibrium

02

04

06

08

0


Lloydminster Blend LLB (LR)

-10

-50

51

0

1998w1 2000w1 2002w1 2004w1 2006w1 2008w1w_time

Disequilibrium (LLB to LLB LR) Reference

19

0

20

40

60

80

100

30 40 50 60 70 80 90 100

WTI price

LLB

pric

e

MayaWTI


Long run equilibrium between Maya and WTI:log Maya = (- .2773277) + (1.02387) log WTI

Predicted ‘equilibrium’ in price levels:

@$30: 82% spread to

WTI

@$100: 85% spread to

WTI

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Historical Maya pricesActual and predicted prices

02

04

06

08

0


Maya Blend Maya (LR)

Departure from equilibrium

-10

-50

5

1998w1 2000w1 2002w1 2004w1 2006w1 2008w1w_time

Disequilibrium (Maya to Maya LR) Reference

21

Part #2Bottom Line: Short-run Dynamics

Shocks to WTI Affect LLB and Maya in the short runImpose a strong drag to equilibrium on both heavy crudes

Shocks to LLB and MayaAffect WTI in the short runBut drag to equilibrium is not significant: WTI is weakly exogenous

Shocks to LLB Affect Maya in the short runImbalance between LLB and WTI affects Maya in the long run

Shocks to Maya Affect LLB in the short runImbalance between Maya and WTI does not affect LLB

22

Part #2Bottom Line: Short-run Dynamics (cont.)

Shocks to WTI cause short run shocks to Maya and LLBOnce WTI is stabilized, shocks are persistent and impact long run prices of Maya & LLB

Convergence to long-run equilibrium takes over after 9 weeks

-0.10

-0.05

0.00

0.05

0.10

0 5 10 15 20 25 30 35 40 45 50

Time (weeks)

Shoc

k to

log

varia

bles

DeltaWTIDeltaMayaDeltaLLB

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30

35

40

45

50

55

60

65

70

0 5 10 15 20 25 30 35 40 45

WTI

LR Maya

LR LLB

Maya

LLB

WTI price shockLong term

persistence: 57.1% of

initial shock

Long run pass-through to Maya: 75% of persistent shockLong run pass through to LLB: 54% of persistent shock

Initial adaptation of prices

Long run convergence to

equilibrium

24


Shocks to LLB cause short term shocks to other variables Once other variables have stabilized, LLB has limited further impact on long-run prices


-0.10

-0.05

0.00

0.05

0.10

0 5 10 15 20 25 30 35 40 45 50

Time (weeks)

Shoc

k to

log

varia

bles


25

Long run pass-through to WTI: 43% of initial shockLong run pass through to Maya: 42% of initial shock


30

35

40

45

50

55

60

65

0 5 10 15 20 25 30 35 40 45

WTI

LR Maya

LR LLB

Maya

LLBLLB price shock



equilibrium

Long term persistence:

31.2% of initial shock

26


Shocks to Maya cause short term shocks to other variables Once other variables have stabilized, Maya has no further impact on long-run prices


-0.10

-0.05

0.00

0.05

0.10

0 5 10 15 20 25 30 35 40 45 50

Time (weeks)

Shoc

k to

log

varia

bles


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30

35

40

45

50

55

60

65

0 5 10 15 20 25 30 35 40 45

WTI

LR Maya

LR LLB

Maya

LLB

Maya price shock

Long run pass-through to WTI: 16.0% of initial shockLong run pass through to LLB: 13.6% of initial shock



equilibrium

Long term persistence:

19.1% of initial shock

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Part #3Bottom-line: Exogenous impact of WTI

Implications of the VECM:Short run and long run movements of heavy oil prices are linked to WTI price through different channelsHowever, the model misses the contemporaneous effect of WTI on other variables

New model: Conditional Error Correction Model (CECM)WTI is assumed exogenous with a contemporaneous effect on heavy crudesResult: fit is much better! (R2 = 12% 52% for LLB, 8% 59% for Maya)But we loose information on the feedback from heavy crudes to WTI

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Part #3Bottom-line: Exogenous impact of WTI (Cont.)

CECM estimates the following short run dynamics:

-0.10

-0.05

0.00

0.05

0.10

0 5 10 15 20 25 30 35 40 45

Time (weeks)

Shoc

k to

log

varia

bles


Differential hedging ration between long run and short runHedging ratios are dependent on the level of prices

WTI price shock

Short term response

Long term response

Implications

31

Optimal Hedging StrategyFor a natural long with heavy oil to sell:

There is no futures contract on heavy oilCan one hedge with the NYMEX WTI front month contract? CECM

Naïve hedging strategySingle, unconditional hedge ratio with NYMEX WTI 1st month to 1 year swapBMO (formerly Bank of Montreal): 78.1%

32

Optimal Hedging Strategy

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Optimal Hedging StrategyFor a natural long with heavy oil to sell:

There is no futures contract on heavy oilCan one hedge with the NYMEX WTI front month contract? CECM

Naïve hedging strategySingle, unconditional hedge ratio with NYMEX WTI 1st month to 1 year swapBMO (formerly Bank of Montreal): 78.1%

Conditional long run strategyConditional hedge ratio for NYMEX WTI 1st month contractWTI @ $30/bbl ratio 51%, vs. WTI @ $100/bbl 59%

Short-run strategySingle hedge ratio for NYMEX WTI 1st month contract: 84.5%Position informed by reversal to long run equilibrium

The End

Dynamics of the Heavy-Light Spread in the N. American Oil Marketjparsons/Presentations/Heavy...

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