Modeling Commodity Spreads with Vector Autoregressions
Transcript of Modeling Commodity Spreads with Vector Autoregressions
Modeling Commodity Spreads with Vector Autoregressions
Ted KuryThe Energy Authority ®
ICDSA 2007June 1, 2007
Rule #1 of Pricing Models
Pricing models can offer valuable insight into the behavior of simple or complex markets
Rule #2 of Pricing Models
“Markets tend to be rational in the long run, but markets can stay irrational longer than you can stay solvent”
J.M. Keynes (attributed)
The Problem
• Model the prices of two closely-related commodities– Natural gas at two different delivery points– Interest rates
• Prices usually tied together by some fundamental factor (e.g. transportation rates)
• Capture not only the evolution of prices, but the relationship between prices
Natural Gas Time SeriesN a tu ra l G a s P ric e H is to ry
-1 0 .0 0
-5 .0 0
0 .0 0
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1 0 .0 0
1 5 .0 0
2 0 .0 0
2 5 .0 0
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$/M
MB
tu
H H G a s T Z 4 G a s S p re a d
Gas Price Characteristics
Since 1994 Since 2000
Henry HubTransco Zone 4 Basis Henry Hub
Transco Zone 4 Basis
1st %tile 1.44 1.46 -0.20 2.03 2.10 -0.125th %tile 1.59 1.60 -0.06 2.41 2.46 -0.0410th %tile 1.74 1.76 -0.03 2.82 2.86 -0.0125th %tile 2.17 2.19 0.00 3.94 3.98 0.0275th %tile 5.61 5.66 0.07 6.66 6.81 0.1290th %tile 7.15 7.27 0.18 7.79 8.05 0.2795th %tile 8.01 8.22 0.30 9.64 9.92 0.4099th %tile 12.82 13.28 0.72 13.68 14.28 0.95
Presentation Outline
• Modeling Considerations• Traditional Energy Models
– Modeling Prices– Modeling Spreads
• Vector Autoregression Framework
Presentation Outline
• Modeling Considerations• Traditional Energy Models
– Modeling Prices– Modeling Spreads
• Vector Autoregression Framework
Modeling Considerations
• Relative Prices– Spread Option
• Absolute and Relative Prices– Barrier Option– Cash Flow at Risk of Forward Purchase or Sale– Absolute Product (Natural Gas) Cost
Presentation Outline
• Modeling Considerations• Traditional Energy Models
– Modeling Prices– Modeling Spreads
• Vector Autoregression Framework
The Usual Suspects
• Closed-Form Spread Option Formulas• Geometric Brownian Motion Price Model• Single Factor Mean Reversion Price Model
Traditional Spread Models
• Model such as Margrabe (1978)• Derived from Black-Scholes, so it shares its
assumptions– Lognormal price returns– Independent and identically distributed shocks– No transaction costs
Margrabe Valuation for Spread Options
• Similar to Black and Black-Scholes
where:)()(),,( 221121 dNxdNxtxxw −=
( )t
txxdσ
σ 22
121
1/ln +
=
tdd σ−= 12
2122
21
2 2 σρσσσσ −+=
Geometric Brownian Motion Model (GBM)
• Use the log formulation since we’re modeling a trending series
where:ttt SS ε+= −1lnln
),0(~ 2σε Nt
Price Evolution Under GBM
• Let’s just assume that p is the log price• At time 1
• At time 2
• At time t, collecting the shock terms
101 ε+= pp
[ ] 2102 εε ++= pp
∑=
+=t
iit pp
10 ε
Price Variance Under GBM
• Variance of each individual shock term
• So the variance of t terms
2)( σε =tVar
2)( σtpVar t =
Modeling the Spread Between Two Prices
• Assume two prices, p and q, where
and:η+= pq
),0(~ 2ppt N σε
),0(~ 2qqt N σε
),( qpcorr εερ =
Spread between GBM Prices
• So the basis at time t
• or
• With variance
∑∑==
−+−+=−t
ipi
t
iqitt pppq
11000 εεη
∑∑==
−+=−t
ipi
t
iqitt pq
110 εεη
( ) ( )qpqptt tpqVar σρσσσ 222 −+=−
Sample GBM SimulationG B M S im u la tio n
-2 .0 0
0 .0 0
2 .0 0
4 .0 0
6 .0 0
8 .0 0
1 0 .0 0
05/01/0705/03/0705/05/0705/07/0705/09/0705/11/0705/13/0705/15/0705/17/0705/19/0705/21/0705/23/0705/25/0705/27/0705/29/0705/31/0706/02/0706/04/0706/06/0706/08/0706/10/0706/12/0706/14/0706/16/0706/18/0706/20/0706/22/0706/24/0706/26/0706/28/0706/30/07
$/M
MB
tu
H e n ry H u b T Z 4 S p re a d
Single Factor Mean Reverting Model (SFMR)
• Framework of Pindyck (1999) and Schwartz (1997)
where:
α is mean reversion rateμ is log of the long run equilibrium price
tttt SSS εμα +−+= −− )ln(lnln 11
),0(~ 2σε Nt
Price Evolution Under SFMR
• Again, assuming that p is the log price• At time 1, rearranging terms
• At time 2
• At time t, collecting terms
101 )1( εααμ +−+= pp
2102 ])1()[1( εεααμααμ ++−+−+= pp
( ) ( ) ( )∑∑=
−−
=
−+−+−=t
i
iti
tt
i
it pp
10
1
0111 αεαααμ
Price Variance Under SFMR
• Variance of each individual shock term
• So the variance of t terms
• Which reduces to
2)( σε =tVar
( )∑=
−−=t
i
ittpVar
1
)(22 1)( ασ
( )( )
22
2
1111)( σ
αα−−−−
=t
tpVar
Variance ComparisonsV a ria n c e G ro w th R a te s
0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
t 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78
T im e
Varia
nce
N o M e a n R e ve rs io n S lo w e r M e a n R e ve rs io n F a s te r M e a n R e ve rs io n
Price Comparisons
0 .00
2 .00
4 .00
6 .00
8 .00
10 .00
12 .00
14 .00
16 .00
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1/06
08/0
8/06
08/1
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2/06
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G B M - 95 th % tile G B M - 5 th % tile S F M R - 95 th % tile S F M R - 5 th % tile
Spread between SFMR Prices
• So the basis at time t
• With variance
( ) ( )( )
( )( )
( )( )
( )( ) q
q
tq
pp
tp
tq
pp
tp
tt pqVar σαα
σαα
ρσαα
σαα
2
2
2
22
2
22
2
2
1111
1111
21111
1111
−−
−−
−−
−−−
−−
−−+
−−
−−=−
( ) ( )∑∑−
=
−−−
=
−−−+1
0
1
011
t
i
itppi
itq
t
iqi αεαε
[ ] [ ]ηαηαpμααpq tq
tqp
tq
tptt )1(1)1()()1()1( 00 −−+−+−−−−=−
Sample SFMR SimulationS F M R S im u la tio n
-2 .0 0
0 .0 0
2 .0 0
4 .0 0
6 .0 0
8 .0 0
1 0 .0 0
1 2 .0 0
1 4 .0 0
1 6 .0 0
05/01/0705/03/0705/05/0705/07/0705/09/0705/11/0705/13/0705/15/0705/17/0705/19/0705/21/0705/23/0705/25/0705/27/0705/29/0705/31/0706/02/0706/04/0706/06/0706/08/0706/10/0706/12/0706/14/0706/16/0706/18/0706/20/0706/22/0706/24/0706/26/0706/28/0706/30/07
$/M
MB
tu
H e n ry H u b T Z 4 S p re a d
Behavior of SFMR Expected BasisB a s is E vo lu tio n U n d e r D iffe re n t M e a n R e ve rs io n R a te s
0 .0 0
1 .0 0
2 .0 0
3 .0 0
4 .0 0
5 .0 0
6 .0 0
7 .0 0
1 20 39 58 77 96115
134153
172191
210229
248267
286305
324343
362381
400419
438457
476495
514533
552571
590
T im e
Pric
e
0 .9 8
1 .0 0
1 .0 2
1 .0 4
1 .0 6
1 .0 8
1 .1 0
1 .1 2
Bas
is (P
ropo
rtio
nal)
P rice w ith Lo w e r M e a n R e ve rs io n R a te P rice w ith H ig h e r M e a n R e ve rs io n R a te B a s is w ith B ias B as is w ithou t B ias
Behavior of SFMR ShocksD e c a y o f S F M R S h o c k s
-1 .6 0 0 0
-1 .4 0 0 0
-1 .2 0 0 0
-1 .0 0 0 0
-0 .8 0 0 0
-0 .6 0 0 0
-0 .4 0 0 0
-0 .2 0 0 0
0 .0 0 0 0
0 .2 0 0 0
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99
T im e
Stan
dard
Nor
mal
Sho
ck
S lo w e r M e a n R e ve rs io n R a te F a s te r M e a n R e ve rs io n R a te D iffe re n ce
Behavior of SFMR ShocksD e c a y o f S F M R S h o c k s
0 .0 0 0 0
0 .2 0 0 0
0 .4 0 0 0
0 .6 0 0 0
0 .8 0 0 0
1 .0 0 0 0
1 .2 0 0 0
1 .4 0 0 0
1 .6 0 0 0
1 .8 0 0 0
2 .0 0 0 0
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99
T im e
Stan
dard
Nor
mal
Sho
ck
S lo w e r M e a n R e ve rs io n R a te F a s te r M e a n R e ve rs io n R a te D iffe re n ce
Behavior of SFMR ShocksD e c a y o f S F M R S h o c k s
0 .0 0 0 0
0 .1 0 0 0
0 .2 0 0 0
0 .3 0 0 0
0 .4 0 0 0
0 .5 0 0 0
0 .6 0 0 0
0 .7 0 0 0
0 .8 0 0 0
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99
T im e
Stan
dard
Nor
mal
Sho
ck
S lo w e r M e a n R e ve rs io n R a te F a s te r M e a n R e ve rs io n R a te D iffe re n ce
Traditional Energy Models
• Margrabe Spread Model– Shares assumptions, both good and bad, with Black-Scholes
• Geometric Brownian Motion– Fixed expected basis equal to today’s basis– Infinite variance of spread
• Single Factor Mean Reverting– Variable expected basis– Finite variance of spread, but different mean reversion rates can lead to
much different decay rates– Difference in shocks can increase and may diverge from what is seen in
reality
Presentation Outline
• Modeling Considerations• Traditional Energy Models
– Modeling Prices– Modeling Spreads
• Vector Autoregression Framework
Vector Autoregressions - The Better Mousetrap
• Vector autoregression framework allows greater flexibility– Established methodology– Robust diagnostic testing– Multiple methodologies to handle shocks
• Future path of prices depends on– Historical path of all modeled prices; and– Future path of other prices
Vector Autoregression Model (VAR)
• Models prices of goods that are close substitutes
where:
qtttt
ptttt
qpq
qpp
εββα
εββα
+++=
+++=
−−
−−
1221212
1121111
),0(~ 2ppt N σε
),0(~ 2qqt N σε
),( qpcorr εερ =
Price Evolution under VAR
• Matrix representation
• Change notation to
• Price at t in terms of P0
⎥⎦
⎤⎢⎣
⎡+⎥
⎦
⎤⎢⎣
⎡⎥⎦
⎤⎢⎣
⎡+⎥
⎦
⎤⎢⎣
⎡=⎥
⎦
⎤⎢⎣
⎡
−
−
qt
pt
t
t
t
t
qp
qp
εε
ββββ
αα
1
1
2221
1211
2
1
t1tt ΕΒPΑP ++= −
)(1
0it0t ΕΑΒPΒP −
−
=
++= ∑t
i
it
The Stability of the Weighting Matrix
• Given the weighting matrix
• Subsequent powers are
⎥⎦
⎤⎢⎣
⎡=
822.0171.00.2530.7261Β
⎥⎦
⎤⎢⎣
⎡=
598.0356.00.5250.3995Β
⎥⎦
⎤⎢⎣
⎡=
659.0316.00.4670.4823Β⎥
⎦
⎤⎢⎣
⎡=
719.0266.00.3920.5712Β
⎥⎦
⎤⎢⎣
⎡=
544.0355.00.5240.34610Β
Sample VAR SimulationV A R S im u la tio n
-2 .0 0
0 .0 0
2 .0 0
4 .0 0
6 .0 0
8 .0 0
1 0 .0 0
1 2 .0 0
05/01/0705/03/0705/05/0705/07/0705/09/0705/11/0705/13/0705/15/0705/17/0705/19/0705/21/0705/23/0705/25/0705/27/0705/29/0705/31/0706/02/0706/04/0706/06/0706/08/0706/10/0706/12/0706/14/0706/16/0706/18/0706/20/0706/22/0706/24/0706/26/0706/28/0706/30/07
$/M
MB
tu
H e n ry H u b T Z 4 S p re a d
Unstable VAR SimulationV A R S im u la tio n
-4 ,0 0 0
-3 ,0 0 0
-2 ,0 0 0
-1 ,0 0 0
0
1 ,0 0 0
2 ,0 0 0
3 ,0 0 0
4 ,0 0 0
5 ,0 0 0
6 ,0 0 0
7 ,0 0 0
05/01/0705/03/0705/05/0705/07/0705/09/0705/11/0705/13/0705/15/0705/17/0705/19/0705/21/0705/23/0705/25/0705/27/0705/29/0705/31/0706/02/0706/04/0706/06/0706/08/0706/10/0706/12/0706/14/0706/16/0706/18/0706/20/0706/22/0706/24/0706/26/0706/28/0706/30/07
$/M
MB
tu
H e n ry H u b T Z 4 S p re a d
Behavior of VAR ShocksD e c a y o f V A R S h o c k s
-0 .1 0 0 0
0 .0 0 0 0
0 .1 0 0 0
0 .2 0 0 0
0 .3 0 0 0
0 .4 0 0 0
0 .5 0 0 0
0 .6 0 0 0
0 .7 0 0 0
0 .8 0 0 0
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99
T im e
Stan
dard
Nor
mal
Sho
ck
F irs t S h o ck T e rm S e c o n d S h o c k T e rm D iffe re n ce
Behavior of VAR ShocksD e c a y o f V A R S h o c k s
-1 .5 0 0 0
-1 .0 0 0 0
-0 .5 0 0 0
0 .0 0 0 0
0 .5 0 0 0
1 .0 0 0 0
1 .5 0 0 0
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99
T im e
Stan
dard
Nor
mal
Sho
ck
F irs t S h o ck T e rm S e c o n d S h o c k T e rm D iffe re n ce
Behavior of VAR ShocksD e c a y o f V A R S h o c k s
-0 .2 0 0 0
0 .0 0 0 0
0 .2 0 0 0
0 .4 0 0 0
0 .6 0 0 0
0 .8 0 0 0
1 .0 0 0 0
1 .2 0 0 0
1 .4 0 0 0
1 .6 0 0 0
1 .8 0 0 0
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99
T im e
Stan
dard
Nor
mal
Sho
ck
F irs t S h o ck T e rm S e c o n d S h o c k T e rm D iffe re n ce
Strengths of the VAR
• Construct paths for prices without associated forward curves
• Several well established tests to determine optimal number of lags
• Two methods to correlate price shocks– Actual correlation and normally distributed shocks– Resample actual historical shocks to pick up correlation
and any non-normal distributions
Weaknesses of the VAR
• More rigorous process to determine parameters• More diagnostic testing of model
– Proper functional form– Stable system of equations
• Number of parameters grows quickly (N2L) and can erode your degrees of freedom, so a larger data set may be required
Pipeline Management Risk Assessment
• Resource management problem• Value of transportation capacity• Risk depends on the price of gas at 3 hubs• Most conservative test shows the need for 100 lags
Pipeline Model SimulationS a m p le M o d e l Ite ra tio n
-2 .0 0
0 .0 0
2 .0 0
4 .0 0
6 .0 0
8 .0 0
1 0 .0 0
1 2 .0 0
1 4 .0 0
04/01/0704/15/0704/29/0705/13/0705/27/0706/10/0706/24/0707/08/0707/22/0708/05/0708/19/0709/02/0709/16/0709/30/0710/14/0710/28/0711/11/0711/25/0712/09/0712/23/0701/06/0801/20/0802/03/0802/17/0803/02/0803/16/0803/30/08
$/M
MB
tu
T ra n s c o Z o n e 1 T ra n s c o Z o n e 3 T ra n s c o Z o n e 6 Z 3 -Z 1 S p re a d Z 6 -Z 3 S p re a d
Summary
• Traditional models may not work well to model absolute price levels and commodity spreads– Infinite variance– Unstable mean spreads– Different rates of mean reversion can cause divergence
over time
• Vector autoregression offers a flexible framework– Better captures price interactions– Derive future path for prices without forward curve– Handle non-normal and heteroscedastic shocks
Questions?
• Contact Info:Ted [email protected](904) 360-1444
References• Margrabe, W., 1978, “The Value of an Option to Exchange One Asset for
Another”, Journal of Finance 33:1 p. 177-186• Pindyck, R., 1999, “The Long Run Evolution of Energy Prices”, The
Energy Journal 20:2, p. 1-27• Schwartz, E., 1997, “The Stochastic Behavior of Commodity Prices:
Implications for Valuation and Hedging”, Journal of Finance 52:3, p. 923-973