Optionality and risk in the LNG market Hartley Optionality.pdfRecent LNG market developments v LNG...

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Optionality and risk in the LNG market

Peter R HartleyGeorge A. Peterkin Professor of Economics and

Rice Scholar in Energy Studies, James A. Baker III Institute for Public Policyand

Kenneth B. Medlock, IIIJames A. Baker, III, and Susan G. Baker Fellow in Energy and Resource Economics

Senior Director, Center for Energy StudiesJames A. Baker III Institute for Public Policy

Rice University

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Recent LNG market developments

v LNG trade growth > natural gas trade growth ≅ TPE growth

v LNG market is becoming more global:v Many more sellers and especially buyers

v Traders becoming more dispersed geographically

v Moving away from long-term, bilateral trading contracts toward spot and short-term, and more flexible contracts

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Overall LNG trade: Long and short term

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 20180

100

200

300

400

500

600

700

800

0%

5%

10%

15%

20%

25%

30%

35%

106 m

3 liqu

id L

NG

LNG volume from liquefaction plants Spot and Short-term Trades/Total LNG Re-exports/Total LNG

Source: GIIGNL

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Spot & short-term trade, 2018Q

atar

Aust

ralia

Mal

aysi

a

U.S

.A.

Nig

eria

Rus

sia

Indo

nesi

a

Trin

idad

& T

obag

o

Alge

ria

Om

an

Papu

a N

ew G

uine

a

Oth

ers

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Proportion spot & short-term

Proportion of Global Exports

Source: GIIGNL

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US as a developing exporter

v US will become a major supplier to Asia, which still takes more than 75% of LNG

v US advantages:

v Lower capital costs of initial US projectsv Reduced need for debt finance and thus for long-term contractsv More exports likely to be short-term and more available for arbitragev Atlantic and Mediterranean markets can be accessed to south and east,

South Asia via Suez Canal and East Asia via Panama Canalv Import/export access to deep North American gas market with

substantial storage and extensive derivatives tradingv US disadvantages:

v Relatively high transportation costsv Higher average, and especially more variable, net (after liquid sales) feed

gas cost than projects based on otherwise “stranded gas”v US LNG plants are essentially real options on export netback minus HH

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US LNG Exports February 2016–July 2018Country of destination Quantity (MT) % of total US LNG exports

Mexico 6.04 19.9South Korea 5.44 17.9

China 3.97 13.1Japan 2.51 8.2Chile 1.76 5.8India 1.55 5.1

Jordan 1.44 4.7Argentina 1.02 3.4

Brazil 0.97 3.2Turkey 0.83 2.7Spain 0.73 2.4

Kuwait 0.69 2.3Portugal 0.54 1.8Taiwan 0.46 1.5Egypt 0.35 1.1U.A.E. 0.34 1.1

Pakistan 0.33 1.1Thailand 0.06 0.2

Others (13 countries) 1.36 3.8Total 30.38

Source: EIA

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LNG freight rates to S. China or Taiwan

Feb-2016 Aug-2016 Feb-2017 Aug-2017 Feb-2018 Aug-20180.0

0.5

1.0

1.5

2.0

2.5

3.0

Mon

thly

ave

rage

$/M

MBT

U

Sakhalin

Dampier Australia

Middle East

Peru

Nigeria

Algeria

Trinidad/Panama

US Gulf Panama

Norway

Trinidad

US Gulf Cape

US Gulf Suez

Source: Platts

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Operational/In construction US LNG export terminals

Terminal status and location Capacity bcf/d As % 2018 LNG exportsOperational

Sabine Pass, LA (trains 1-5) 3.5 8.4Cove Point, MD 0.82 2.0Corpus Christi, TX (train 1) 0.71 1.7Hackberry, LA (train 1) 0.71 1.7

Sub-total operational 5.74 13.7Under construction

Hackberry, LA (trains 2-3) 1.4 3.4Freeport, TX 2.14 5.1Corpus Christi, TX (trains 2-3) 1.4 3.3Sabine Pass, LA (train 6) 0.7 1.7Elba Island, GA 0.35 0.8Cameron Parish, LA 1.41 3.4Sabine Pass, TX 2.1 5.0Calcasieu Parish, LA 4.0 9.6

Sub-total under construction 13.5 32.3

Source: FERC

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Approved/Pending/Proposed US LNG export terminalsTerminal status and location Capacity bcf/d As % 2018 LNG exports

Approved, not under constructionLake Charles, LA (Southern Union) 2.2 5.3Lake Charles, LA (Magnolia) 1.08 2.6Hackberry, LA (expansion) 1.41 3.4Port Arthur, TX 1.86 4.4Freeport, TX (expansion) 0.72 1.7Pascagoula, MS 1.5 3.6Gulf of Mexico, FLNG 1.8 4.3

Sub-total approved 10.57 25.2Pending applications

Brownsville, TX (Texas LNG) 0.55 1.3Brownsville, TX (Rio Grande LNG) 3.6 8.6Brownsville, TX (Annova LNG) 0.9 2.1Jacksonville, FL 0.13 0.3Plaquemines Parish, LA 3.4 8.1Nikiski, AK 2.63 6.3Coos Bay, OR 1.08 2.6Corpus Christi, TX (expansion) 1.86 4.4Cameron Parish, LA 1.18 2.8

Sub-total pending 15.33 36.65 terminals (LA, TX) in pre-filing 7.37 17.6

Source: FERC

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Other (international) projects to 2024

Terminal and location Start year Capacity as % 2018 LNG exportsAustralia

Icthys T2 2019 1.43Prelude FLNG 2019 1.15

MalaysiaPFLNG 2 2020 0.48

IndonesiaSengkang 2019 0.64Tangguh T3 2021 1.21

RussiaYamal T3 2019 1.15

Total 6.06Post-2020

Russia Arctic LNG 2023 1.15Mozambique 2024 4.10Qatar expansion 2024 10.52PNG expansion 2024 2.55Nigeria expansion 2024 2.55

Total 20.87

Source: International Gas Union

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Model input 1: Natural gas and oil prices

v Weekly data on JKM, NBP, HH (all $/mmbtu) and Brent ($/barrel)

v VECM for JKM, NBP, Brent but cointegrating error does not affect Brent

v Univariate AR(2) with weekly dummies for HH

v Tie down oil price level by assuming the oil market slowly reacts to high or low prices

v Regime 1: Dominated by entry and declining prices

v Regime 2: Dominated by exit and rising prices

v Higher oil prices raise the probability of being in regime 1 and vice versa

v Simulated Brent price mean 60.622, standard deviation 5.896, skewness 0.386 and kurtosis 4.169

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Five example 20-year Brent price paths

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2040

50

60

70

80

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120

real

$/b

arre

l

years

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Long-term contract indexation

v Assume long-term contracts are with NE Asian importers only

v Agerton (2017) analysis of Australia/Japan LNG trade implied an elasticity of 1.0 at the mean values for crude and LNG prices in his data

v Cointegrating equation for JKM gives a spot price elasticity of 1.073

v In the period he examined, spot cargoes were about 25% of the total

v Implied elasticity for the 75% of cargoes under contract at the mean crude and LNG prices is 0.976

v Use these values to calibrate a logistic curve for the S-curve relationship:

pLNG = a1+ exp −b(Brent − c)( )

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Contract S-curve and JKM cointegrating relationship

Contract S-curve

Cointegrating relationship

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Model input 2: Shipping costs from US Gulf

v Based on longer time series of costs from Trinidad & Tobago to NW Europe (TTNWE) and Asia via Suez (TTJK)

v US rates to NW Europe, Asia via Suez, Asia via Cape and Asia via Panama over the shorter time period closely, linearly related to TT rates

v The TT rates have a unit root but are cointegrated with Brent

v Since costs involve more than the fuel cost, we estimated a non-linear (Gompertz) cointegrating relationship between TTNWE and Brent

v TT-JK via Suez satisfied an AR(2) relationship with TTNWE

TTNWEt = 0.3437(0.0378)+1.3287

(0.1606)exp −exp − 0.0343

(0.0059)Brentt − 79.723

(3.215)

⎛

⎝⎜

⎞

⎠⎟

⎛

⎝⎜

⎞

⎠⎟

⎛

⎝⎜⎜

⎞

⎠⎟⎟+ν t

Δ ln(TTNWEt ) = −0.00008(0.00178)

− 0.0427(0.0118)

⌢ν t−1 + 0.1790(0.0481)Δ ln(TTNWEt−1)+ 0.2605(0.0480)

Δ ln(TTNWEt−2 )+wt

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Model input 3: Shipping fleet

v Use GIIGNL 2018 LNG shipping fleet and exclude ships:v Restricted to Nigeria trade (Bonny Gas Transport, owner and operator)

v Restricted to Qatar trade (Maran Nakilat or Nakilat, owner and operator)

v Below 13,200 m3 in capacity, which equals the min spot & short-term trade

v Remaining 434 ships ranged in capacity from 14,000–267,335 m3 with a mean of 149,163 m3

v This distribution was approximated by a histogram with 13 bins

v Largest size category exceeds Panama Canal capacity

v Assumed larger ships tended to be used for long-term contract trade:v Ships 135,000 m3 or smaller were assumed to be devoted to spot trade

v Ships in the 9 largest categories i = 1, …, 9 allocated to spot and long-term trades to get a specified average volume shipped spot

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Example shipping size distributions

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50000 100000 130000 135000 140000 145000 150000 155000 160000 170000 180000 220000 270000

Prob

abili

ty

volume interval upper bound

30% spot volume

Spot Contract

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50000 100000 130000 135000 140000 145000 150000 155000 160000 170000 180000 220000 270000

Prob

abili

ty

volume interval upper bound

90% spot volume

Spot Contract

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Shipments per week

v Shipments per week will vary randomly for several reasons:v Ships may be unavailable

v Adverse weather events

v Planned and unplanned outages

v Capacity utilization for liquefaction plants averages less than 95%

v Shipments/week s = 0, 1,…, 7 with Pr(s=0) = 0.05 and s > 0 following a binomial with mean = project capacity ÷ (52*average tanker volume)

v The binomial probabilities were then multiplied by 0.95

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0

0.05

0.1

0.15

0.2

0.25

0.3

0 1 2 3 4 5 6 7

Distribution of shipments per week

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Model input 4: Liquefaction project parameters

v Output 16 million tonnes (35.328 million m3) pa

v 20 year (1040 weeks, 80 quarters) lifetime

v Capital cost: $12 billion, base level of debt finance $4.8 billion

v Variable operating costs/mmbtu equal to 115% of HH

v Tax-deductible (and avoidable) fixed costs from fixed labor, insurance, consumables, maintenance and spares, and the cost of tugs

v Randomly beta-distributed B(12, 12) on [4.5, 5.5] million per week

v Real interest on debt rB = 4.5% pa payable quarterly, fully tax-deductible

v Corporate tax rate of 25%, payable quarterly with full loss offset, straight-line depreciation $20 b/80 per quarter for 80 quarters

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Random variable sample paths

v For each of 1 million samples (1.5 million in base case), we draw:v 7×1040 weekly shocks from scaled beta distributions (to allow for

asymmetry and fat tails) for:• 3 innovations to price changes in the VAR• 2 error terms for the TT transport cost equations• 1 error term for the HH price change• weekly avoidable fixed costs

v 4×1040 weekly multivariate normal shocks for the four US transport cost equations

v 9×1040 weekly uniform [0, 1] shocks (representing probabilities) for determining:

• the number of cargoes 0–7 shipped each week• the sizes and spot/contract nature of the 7 possible cargoes• the shock θ to prevailing oil price regime

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Optionality measures

v Probability that netback price from NW Europe exceeds the netback spot price from NE Asia Pr(NWEnb>JKnb) ≅ 25.65%

v Probability that the best spot netback price is below the operating cost Pr(max(NWEnb, JKnb) < 1.15HH) ≅ 6.7%

v Probability that a contract trade would be better filled by a spot cargo Pr(swap profitable) ≅ 4.85%

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Results presented

v Results are presented as a function of the contract trade proportion, q

v rS is the annual real IRR to equity for CF = mean after-tax quarterly cash flows after accounting for equity’s contribution to up-front capital costs

v We also calculate:v Negative tail of cash flows Pr(CF < B/80) as an @risk measure

v Two “bankruptcy” measures Pr(NPV(rB)<0) and Pr(NPV(r)<0)

v First four moments of CF from quarters 10-80 (after effect of initial conditions has largely dissipated), hereafter denoted CF*

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Base case results, debt = $4.8 billionProportion of contract trades, q

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

rS 0.1795 0.1722 0.1650 0.1580 0.1511 0.1443 0.1376 0.1310

Pr(CF<B/80) 0.1256 0.1157 0.1068 0.0999 0.0954 0.0938 0.0948 0.0987

Pr(NPV(rB)<0) 8.7E-04 9.7E-04 1.1E-03 1.5E-03 2.1E-03 3.0E-03 4.7E-03 7.6E-03

Pr(NPV(r)<0) 1.1E-04 1.1E-04 1.5E-04 1.8E-04 2.4E-04 3.6E-04 6.1E-04 1.1E-03

Moments of stationary CF*

mean 282.02 277.13 272.43 267.71 263.13 258.47 253.88 249.25

variance 38419 34108 30446 27448 25114 23443 22436 22094

skewness 0.5869 0.5108 0.4145 0.3009 0.1729 0.0408 -0.0823 -0.1835

kurtosis 3.421 3.335 3.243 3.173 3.134 3.146 3.213 3.320

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Some observations

v rS falls, Pr(NPV(rB) < 0) and Pr(NPV(r) < 0) rise, and CF* skewness becomes less positive/more negative as q increases

v Simulated oil price is positively skewed and contract price < co-integrated JKM price for oil prices above the simulated mean

v Firm can be forced to take operating losses only on contract trades

v Exporter can better avoid losses and exploit upside options with spot trades

v Pr(CF < B/80) and CF* kurtosis attain minimums, and CF* skewness approximates zero, around 60% of contract (40% spot) trade

v What explains the rise in Pr(CF < B/80) as q falls below 60%?

v At low oil prices, spot price < contract price and low operating profits can still be insufficient to cover fixed costs, interest and debt repayment

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Effect of leverage on Pr(CF < B/80)

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

10% 20% 30% 40% 50% 60% 70% 80%Percentage of contract trade

B=2400 B=3600 B=4800 B=6000

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Leverage and Pr(NPV(rB)<0), Pr(NPV(r)<0)

0

0.002

0.004

0.006

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0.012

0.014


B=2400 B=3600 B=4800 B=6000

0

0.0005

0.001

0.0015

0.002

0.0025


B=2400 B=3600 B=4800 B=6000

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Increases in oil price volatility

Moment Base case Higher volatility

Mean 60.62 60.66

Standard deviation 5.896 6.625

Skewness 0.386 0.520

Kurtosis 4.169 4.815

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Effect of oil price volatility on Pr(CF < B/80)

0.090

0.095

0.100

0.105

0.110

0.115

0.120

0.125

0.130


Base case MPS Brent error

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Scaled changes from higher oil price variability

Proportion of contract trades, q

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

rS -1.12 0.76 1.28 1.83 3.88 3.18 3.90 6.43

Pr(CF<B/80) 44.29 46.02 50.65 57.12 63.85 73.10 83.89 92.13

Pr(NPV(rB)<0) 2.77 2.50 3.45 4.47 5.43 10.90 16.20 19.51

Pr(NPV(r)<0) 1.52 2.16 0.85 3.37 2.03 7.96 9.48 13.54


mean -9.79 -7.45 -7.02 -6.60 -5.80 -4.42 -4.45 -1.55

variance 72.71 77.27 84.69 91.29 104.06 115.70 131.77 148.17

skewness 52.79 55.81 59.07 60.20 73.38 89.35 114.15 140.18

kurtosis 28.58 37.05 44.41 28.26 26.56 27.46 30.87 24.87

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Higher oil price variability

v Oil price distribution change increases variance, positive skewness and kurtosis of CF* – especially at high q

v Pr(CF < B/80), Pr(NPV(rB)<0), Pr(NPV(r)<0) increase especially at high qv Contract price is mechanically tied to the oil price, spot prices deviate more

v Contract sales can occur even if operating profits are negative

v But higher spot price variability also increases the chance of high cash flows from optional spot sales – more important for low q

v Increase in rS at high q despite decline in mean CF* requires mean cash flow in first 10 quarters to increase

v Initial oil price is $78.22, above the mean of CF*

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Effect of higher JKM variance on Pr(CF < B/80)

0.090

0.095

0.100

0.105

0.110

0.115

0.120

0.125

0.130

10% 20% 30% 40% 50% 60% 70% 80%Proportion of contract trade

Base case MPS JKM

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Scaled changes from higher JKM variability


0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

rS 132.15 116.83 106.53 92.42 77.50 68.60 55.34 39.34

Pr(CF<B/80) 12.71 3.39 -8.06 -15.94 -21.07 -28.91 -31.90 -29.26

Pr(NPV(rB)<0) -3.88 -5.17 -4.47 -8.36 -7.83 -13.62 -16.73 -20.43

Pr(NPV(r)<0) -1.88 -1.16 -1.62 -3.57 -2.06 -5.99 -6.01 -10.28


mean 98.03 88.07 78.99 68.98 58.29 50.52 41.80 30.14

variance 306.88 241.22 183.96 133.85 90.52 54.76 27.85 7.83

skewness 155.65 150.77 145.83 127.70 105.65 78.11 46.44 22.43

kurtosis 70.75 60.67 54.66 38.00 25.08 12.37 4.09 -0.07

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Higher JKM variability

v CF* mean, variance, skewness and kurtosis all increase, with the change larger as more is sold spot, mostly at the JKM price

v Larger mean CF* also increases rS – more so when spot trade is prevalent

v Increased spot price variance raises the value of the option to avoid negative operating profits on spot trades

v Declines in Pr(NPV(rB)<0), Pr(NPV(r)<0) suggest that opportunities for high returns more than offset increased low returns

v Larger effects with higher q emphasizes the value of the opportunity to avoid low contract prices via swaps

v Pr(CF < B/80) decreases for q ≥ 30%, but increases for q < 30%

v Option value of swaps at high q – at low q, ability to use swaps is limited

v Higher spot price variability increases the prevalence of low spot price trades

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Higher NBP variability

v Increased NBP variability, like higher JKM variability, raises CF* mean (and hence rS), variance, skewness and kurtosis

v But the effects (not graphed or listed) are much weaker, especially at higher q

v Again the effects are more pronounced as more LNG is sold spot

v Increased NBP variability affects optionality in two ways:v Raises the probability that a spot sale to Europe is more profitable than a spot

sale to northeast Asia

v The “alternative” NBP price also affects the probability that

pA – 𝜏A > pJ – 𝜏J ≥ pc – 𝜏J

and hence that a swap is profitable, but the JKM price is a more important determinant of this outcome

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Effect of higher HH variance on Pr(CF < B/80)

0.090

0.095

0.100

0.105

0.110

0.115

0.120

0.125

0.130

0.135

0.140


Base case MPS HH error

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Scaled changes from higher HH variability


0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

rS -43.73 -50.31 -53.07 -61.75 -67.02 -73.22 -78.52 -86.49

Pr(CF<B/80) 237.37 265.31 287.72 308.78 329.41 342.67 354.40 365.32

Pr(NPV(rB)<0) 14.19 21.50 25.35 37.34 64.26 91.77 135.48 209.23

Pr(NPV(r)<0) 8.61 13.33 12.79 22.25 37.64 52.82 87.17 139.55


mean -29.42 -35.73 -39.55 -43.88 -49.31 -53.62 -57.27 -63.60

variance 244.86 269.08 295.04 317.94 343.14 366.27 391.20 415.37

skewness -58.40 -90.22 -123.30 -152.18 -182.58 -204.38 -222.60 -243.30

kurtosis -41.96 -46.81 -44.02 -35.04 -20.65 0.38 22.06 46.69

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Higher HH variability

v Mean-preserving spread is a similar percentage to the ones examined for Brent, JKM and NBP, but the effects are much greater

v The spread in HH affects costs and thus all trades

v rS declines, while CF* skewness becomes less positive or more negative, with a larger impact as q increases

v CF* variance increases, as do Pr(CF < B/80), Pr(NPV(rB)<0) and Pr(NPV(r)<0), more so as q increases

v Kurtosis declines except at very high q where forced sales at a loss raise the probability of negative CF*

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Contract price less sensitive to oil price

oil price

contract price

base formula

altered formula

10 20 30 40 50 60 70 80 90 100 110 120

2

4

6

8

10

12

14

16

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Effect of less sensitive contract price on Pr(CF < B/80)

0.090

0.095

0.100

0.105

0.110

0.115

0.120

0.125

0.130


base flatter contract formula

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Scaled changes from less sensitive contract price


0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

rS -22.55 -44.28 -66.85 -83.01 -106.69 -129.16 -149.93 -168.73

Pr(CF<B/80) 5.70 13.18 14.16 9.49 2.39 -9.06 -19.59 -33.21

Pr(NPV(rB)<0) 1.42 1.48 5.74 7.14 11.75 21.99 37.55 71.75

Pr(NPV(r)<0) 0.51 1.16 0.69 2.21 3.57 8.66 13.56 28.34


mean -11.87 -23.82 -34.99 -44.70 -56.75 -69.92 -81.20 -90.84

variance -14.17 -31.20 -50.12 -71.60 -96.45 -128.40 -159.86 -196.39

skewness 3.47 3.06 4.80 1.19 -8.86 -33.07 -72.68 -118.10

kurtosis 1.72 0.73 1.32 0.34 -1.70 -4.24 -3.08 0.05

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Contract price less sensitive

v Generally, the effects are larger as q increases

v Lower sensitivity of the contract price to the oil price increases the difference between contract and spot prices

v The main effect is to exaggerate effects of changing q seen in the base case

v Where CF* had negative or positive skew in the base case, it now has more

v In the base case, Pr(NPV(rB)<0), and Pr(NPV(r)<0) increased monotonically with q; the new indexation formula increases this tendency

v rS decreases monotonically with q in the base case and the new formula further reduces rS by an amount that increases with q

v In the base case, Pr(CF < B/80) is minimum at q = 60%; the new formula reduces Pr(CF < B/80) above 60% and increases it below 60%

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Summary of results

v Contract proportion q minimizing Pr(CF<B/80) increases with debt

v Higher Brent or HH variability raise Pr(CF<B/80) for all q but lower q where Pr(CF<B/80) is minimum

v An increase in JKM variability raises Pr(CF<B/80) for q < 30% but, by making swaps that avoid low contract profits more likely, reduces it for q ≥ 30%

v Return to equity rS declines as q increases

v Higher JKM variance raises rS but by more when q is smaller

v Higher HH variance reduces rS but by more when q is larger

v Positive skewness in CF* declines, becoming negative, as q increases

v Higher Brent and JKM variability both increase positive skewness in CF* but for Brent the effect increases, and for JKM it decreases, with q

v Higher HH variance decreases positive skewness, and by more as q increases

v Lower contract slope substantially reduces rS and by more as q increases, and raises Pr(CF<B/80) and positive skewness in CF* at low q, reduces Pr(CF<B/80) and increases negative skewness in CF* at high q

v Changes in various real option values explain many of these different effects

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Appendix

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Oil price stochastic difference equation

Regime 1 equation:

Regime 2 equation:

Bren ′t = Brent +15 1− 1.11+ 0.1Brent /30

⎛⎝⎜

⎞⎠⎟

Bren ′t = Brent + 5 1− 1.11+ 0.1Brent /100

⎛⎝⎜

⎞⎠⎟

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Regime determinationv Pr(Regime 1) = 0.8 F(Brent) + 0.2 θ

v Where θ is uniformly distributed on [0, 1] and

v F(Brent) is a probability distribution with the following density function

40 45 50 55 60 65 70 75 80 85 90 Forecast oil price

Pr(Regime 1) ∈ [0.8, 1.0]Pr(Regime 1) ∈ [0, 0.2]

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TT-NW Europe relationship to Brent

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Calculations for one sample path

v Specify proportion of sales under long-term contract

v Obtain cargoes per week and potential shipping sizes of each

v To allow for lagged variables, calculate weekly prices and freight costs by stepping through the dynamic equations one week at a time

v From the 1040 weekly Brent prices calculate the vector of contract prices

v Obtain other weekly freight costs from the weekly TT-NWE freight costsv Determine the lowest cost route for cargoes headed to Asia

v If a ship is too large for the Panama Canal, we inflate the cost of using the Canal for those shipments to ensure it is too costly

v Calculate the different netback prices (spot and contract)

RICE UNIVERSITY

Calculations continued

v Following the model in Hartley (2015), allow a contract trade (to Asia) to be fulfilled with a swap spot cargo when profitable to do so

v Alternative sale would be to NW Europe

v Condition: (NWEnb>JKnb)&(JKM≥pcont), and exporter gets NWEnb+pcont–JKM

v Exercise of the take-or-pay does not affect exporter revenue

v Use the weekly HH price to calculate weekly variable costs

v If the best spot netback price is less than variable operating costs, choose not to make the spot trade

v Calculate the weekly avoidable (tax deductible) fixed costs and hence weekly pre-tax cash flow

v Aggregate into quarterly (13 weeks) pre-tax cash flows and then after-tax flows CF using depreciation allowances and net-of-tax interest costs

Optionality and risk in the LNG market Hartley Optionality.pdfRecent LNG market developments v LNG...

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Transcript of Optionality and risk in the LNG market Hartley Optionality.pdfRecent LNG market developments v LNG...