Master Assignment Wouter Van Heeswijk (Final)

8/13/2019 Master Assignment Wouter Van Heeswijk (Final)

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Woer J.A. van HeeijUrech, 2012


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Thi i a dieraion bmied in flfilmen of he degree Maer of Science in IndrialEngineering & Managemen.D AhorDocmen pe Woer J.A. van HeeijMaer heiSd programmeMaer rac Indrial Engineering & ManagemenFinancial Engineering & ManagemenUniveriDeparmen Univeri of TeneSchool of Managemen & Governance

Ho companDeparmen TNOPerolem GeocienceE Fir pervior Dr. Reinod A.M.G. JooenUniveri of TeneSecond pervior Dr. Kno J.M. HimanTilbrg UniveriEernal pervior Ir. Chriian F.M. BoTNO


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Wih hi d, e aim o increae nderanding of he inigh ha real opion anali (ROA)ha o offer, pariclarl in comparion o dnamic deciion ree anali (DTA). We poin ohe fndamenal heoreical horcoming of appling a conan dicon rae in he laer ap-proach, and eplain ho real opion reolve hi ie. Baed on he fndamen of ri-neralvalaion and replicaing porfolio concep, e addre differen perpecive on ho o reanon-hedgeable ri in a real opion frameor. We adop an inegraed vie combining opionpricing and deciion anali, hich i heoreicall conien and allo an aemen of bohmare ri and privae ri.To illrae he pracical applicaion of real opion anali, e conrc a model hich deer-mine he opimal ime o ich from ga prodcion o elecrici generaion direcl a heellhead (Ga-o-Wire). To deal ih he pah-dependen price pah in hi invemen prob-lem, e e a combinaion of Mone Carlo imlaion and a bacard regreion algorihm. Weconrc forecaing model for naral ga and elecrici price. Thee model deal ih heeaonal effec, price jmp, mean-reverion and ime-varing volaili oberved pariclarlin elecrici price. Wih a comparaive d, e ho ha ROA provide rel ha ignifi-canl deviae from hoe ielded b DTA.:real opion anali,privae ri, pah-dependen, Mone Carlo imlaion, ime e-rie anali, Bermdan ap opion, bacard regreion, Ga-o-Wire


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G American opion Opion hich can be eercied a an ime dring he lifeime of he op-ion.Arbirage Opporni o mae a ri-free profi a zero co, maing e of pricinginconiencie in he mare.

Bermdan opion Opion hich can be eercied a a nmber of pre-pecified dae beforemari.Blac-Scholeformla Formla ed o calclae he arbirage-free price of a Eropean opionnder a e of rericive ampion.

Call opion Derivaive hich gran he righ o b he nderling ae a a previ-ol pecified price.Capial AePricing Model Claic model ed o eimae he rern reired b inveor baed onhe ri-free inere rae and he correlaion of he ae rern ih heprevailing mare rern.Claic ROA Real opion approach hich relie on mare replicaion of he projec,conidering privae ri a a orce of racing error.Complee mare Mare model in hich ever financial ae can be replicaed ih a e ofoher financial ae, here all agen are able o rade all ae and no

ranacion co ei.Convenience ield Benefi and co emming from poeing a commodi compared oholding i financial eivalen, caed b he opporni o profi fromemporar horage and orage co.Deciion TreeAnali Mehod ed o vale a projec ih embedded fleibiliie b incorpora-ing deciion poin and probabiliie of differen cenario, ing a con-an dicon rae for all cah flo.Derivaive Financial inrmen hich derive i vale from ha of an nderling

ae, ih he paoff depending on he pecified condiion.Diconed CahFlo Mehod ed o vale a projec b diconing fre cah flo a a con-an rae in order o obain he ne preen vale.Dicon rae Rae a hich eimae of fre cah flo are diconed, reflecing heime vale of mone and ri-adjmen.Diverificaion Redcion of ri b preading invemen, a ch redcing he varianceof he porfolio rern. Perfec diverificaion leave he inveor e-poed onl o mare movemen.

Eropean opion Opion hich can onl be eercied a he end of i lifeime.


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Fre conrac Conrac hich oblige o b/ell an ae a a cerain poin in he fre,ih he price o be paid deermined oda.GeomericBronian Moion Sochaic proce o model he behavior of ae price over ime, a-ming rern follo a normal diribion ih conan parameer.Hedging Pracice o redce ri epore b aing an offeing poiion o a ec-ri. A perfecl hedged porfolio eliminae all mare ri.Heeroedaici Term o decribe varing variance over ime. Uncondiional heeroeda-ici doe no depend on previo obervaion, condiional heeroe-daici doe.Inegraed ROA Real opion approach hich ame ha he mare i pariall complee,valing he projec par hich can be replicaed ih arbirage pricing andhe remaining par ih bjecive valaion.Leverage Increaing he poenial rern of an invemen ih deb or b ingderivaive, a he co of a higher ri.MAD ROA Real opion approach comparable o he bjecive approach, amingha he replicaing porfolio i a in ecri orh he bjecivel e-imaed vale of he projec.Mare ri Par of projec ri ha can be replicaed and hedged b financial inr-men nder he ampion of a complee mare.

Ornein-Uhlenbec model Eenion of Geomeric Bronian Moion ha incorporae mean-reverion. A he imlaed variable deviae more from i eilibrimlevel, he revering effec become ronger.Porfolio The collecion of invemen held b an inveor, hich ma inclde allform of financial inrmen.Privae ri All projec ri ha canno be hedged b mare inrmen, formalldefined a he racing error of he replicaing porfolio.P opion Derivaive hich gran he righ o ell he nderling ae a a previ-ol pecified price.Real opion Valaion mehod hich eplicil vale fleibiliie in real-orld pro-jec baed on financial opion heor.Replicaingporfolio Porfolio coniing of financial inrmen, hich replicae he paoffof a real projec in all mare ae and a all ime.Rern Logarihm of he price a a cerain ime divided b he rern of he previ-o ime poin, approimaing he fir difference of he price erie overhe pecified inerval.

Revied claic ROA Real opion approach hich applie eiher opion pricing or deciionanali, depending on he dominaing pe of projec ri.


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Ri-free rae Theoreical rae hich inveor can earn iho being bjec o anri. Ofen approimaed b he rern on governmen bond ih verlo defal ri.Ri-neralvalaion

Valaion mehod hich ame an arificial ri-neral price dirib-ion, alloing o e he ri-free rae a drif of he nderling. Providehe ame vale a real valaion if he complee mare ampion hold.Secri Term o decribe a financial inrmen, ch a a oc, a bond or a de-rivaive.

Shor-elling Selling ae borroed from a hird par, ih he inenion of bingbac idenical ae a a laer ime o rern o he hird par. Thi prac-ice allo o profi from price decline iho maing an iniial inve-men, b ha an nlimied donide poenial if no hedged.

Spo conrac Conrac o b or ell an ae a he crren ime again he prevailingmare price.Sbjecive ROA Real opion approach hich ame he bjecive eimae of he projecvale can be conidered a replicaing porfolio, ing i NPV a bai foropion valaion nder he ampion of mare compleene.Sap opion Opion hich gran he righ o ap one ream of cah flo for an-oher ream of cah flo.Time erie Serie of obervaion over a period of ime, ch a price or rern.Tin ecri (Hpoheical) ecri raded on he financial mare ha i perfeclcorrelaed ih he real projec.Vae model Mean-revering ochaic model o replicae he behavior of he inererae over ime.Volaili Sandard deviaion of he rern on an ae, being ed a a meare ofncerain of rern.Weighed AverageCo of Capial

Eimae for he average co of capial, coniing of he co of deb andhe co of ei proporional o heir hare of oal capial.

G 30/30 ambiion Goal of EBN o have 30% of Dch naral ga prodced from mall gafield b he ear 2030.APX-ENDEX Dch energ echange, on hich boh hor- and long-erm conrac onnaral ga and elecrici are raded.Balancing mare Elecrici mare on hich elecrici i raded o correc for mibalance

beeen ppl and demand on he hor erm.


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Connecivi Rae a hich he ga-conaining volme in a field are conneced o eachoher, alloing a ga flo oard he ell.Energie BeheerNederland Dch governmen-oned inie, involved in ever ga inning opera-ion in he Neherland a a faciliaing parner. Alo ha an advior a

oard he Dch governmen.Epanion facor Indicae ho mch ga ill epand hen i i rerieved from he reervoir.Ga-(Iniiall)-In-Place (Iniial) amon of ga preen in a reervoir. No all ga in a reervoir canbe economicall rerieved.GaTerra Major ga rading inie in he Neherland, co-oned b Shell, Eon,EBN and he Dch ae. Ha he pblic a o b ga prodced a mallga field hen reeed.

Ga TranporService Fll dagher compan of Ganie, reponible for he ranpor of naralga hrogh he main ranpor neor. Alo perform converion opera-ion.Ganie Governmen-oned inie hich on and manage he Dch main gadiribion neor.Ga inerecion Planned fncion of he Dch ga ranpor neor o erve a a logiiccenre for he ranpor and orage of naral ga in he norh-e ofErope.Groningen gafield

Major ga field locaed in he province of Groningen. I i he large naralga field in Erope and one of he large in he orld. Alo referred o ahe Slocheren ga field.Line-pacing Soring naral ga in he ranpor neor nder high prere. Varinghe prere allo o ore le or more ga in he pipeline.

Naral ga Ga mire conaining hdrocarbon hich ha a high energeic vale. I ifond in ndergrond reervoir. Naral ga i ed a an energ orceboh direcl and a inp o generae elecrici. I i alo ed a feedocin he chemical indr.

Nederlande Aar-dolie Maachappij E&P operaor joinl oned b Shell and Eon, being he large naralga prodcer in he Neherland and he ole eploier of he Groningen gafield.NederlandeMededinging-aorieiEnergieamer

Compeiion reglaor on he Dch energ mare, having he ahori oenforce reglaion on parie in he mare. Alo reponible for providinglicene o mare parie.Ne-o-gro raio Par of he gro volme of a formaion hich can conain ga.

Permeabili Rae a hich naral ga can flo hrogh he poro roc formaion.Poroi Percenage of a formaion hich can conain flid or ga.


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Reerve Economicall rerievable amon of ga in a reervoir.Saraion Percenage of poro volme in a formaion filled ih ga.Small field polic A Dch governmen polic o imlae he developmen of mall ga field

in order o redce he brden on he Groningen ga field. The polic com-prie a garaneed ale of prodcion, and fical advanage for projec ahe Norh Sea.Spar pread Difference beeen he price of elecrici and he price of he amon ofinp fel reired o generae he ame amon of elecrici.Tail-end ga field Ga field hich i in a mare ae of eploiaion.TenneT Governmen-oned inie hich on and manage he Dch elecric-i high-volage ranpor neor.

TTF Viral rading poin for naral ga, hich i faciliaed b energ e-change APX-ENDEX.Tbing Tbe placed in he ell hrogh hich he ga flo from he reervoir ohe rface. The diameer of he bing deermine he prere and hefricion level ihin he ell.Virgin ga field Crrenl neploied ga field conaining a mall amon of ga.Volme Gro volme of he formaion conaining ga, meared b mlipling hearea of he formaion ih i hicne.

A AC Aocorrelaion.ADF Agmened Dice-Fller.APX Amerdam Poer Echange.AR Aoregreive.ARMA Aoregreive Moving Average

ARIMA Aoregreive Inegraed Moving Average.CAPM Capial Ae Pricing Model.DCF Diconed Cah Flo.DTA Deciion Tree Anali.EBN Energie Beheer Nederland.EBT Earning Before Tae.ENDEX Eropean Energ Derivaive Echange.EPCCI Eropean Poer Capial Co Inde.


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EWMA Eponeniall Weighed Moving Average.E&P Eploraion and Prodcion.GARCH Generalized Aoregreive Condiional Heeroedaici.GBM Geomeric Bronian Moion.GIIP Ga-Iniiall-In-Place.GIP Ga-In-Place.HC High-caloric ga.LC Lo-caloric ga.MA Moving Average.MAD Mare Ae Diclaimer.MWh Megaa hor.NAM Nederlande Aardolie Maachappij.NGL Naral Ga Liid.NMa Nederlande Mededingingaoriei.NPV Ne Preen Vale.PAC Parial Aocorrelaion.

PRP Programme Reponibili Parner.ROA Real Opion Anali.UCCI Upream Capial Co Inde.UOCI Upream Operaing Co Inde.VAR Vecor Aoregreive.VEC Vecor Error Correcion.WACC Weighed Average Co of Capial. Real-orld drif of an ae (eclding dividend pamen). Ri-neral drif of an ae (eclding dividend pamen). Relaive error of opion vale Mone Carlo imlaion. Adjed relaive error of opion vale Mone Carlo imlaion. Ri-neral raniion probabili from ae o ae iho conider-ing ime vale.

Ri-neral raniion probabili from ae o ae hen conideringime vale.


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() Aocovariance of lag beeen ime erie .() Sm of aocovariance () o he hird poer providing aocorrela-ion-correced andard error of ene.

()

Sm of aocovariance () o he forh poer providing aocorre-laion-correced andard error of roi. Variable indicaing he ize of a deerminiic ime rend for a ingle imeep. Amplide parameer for annal eaonal fncion. Amplide parameer for emi-annal eaonal fncion. Correlaion beeen ae and mare rern in CAPM. Conan, ed in everal eaion. Cah flo a ime .(,) Chi-are criical vale ih degree of freedom and ignificance level. Variable in Blac-Schole opion pricing model. Variable in Blac-Schole opion pricing model. Degree of freedom. Mare vale of deb in WACC.

Average dail effec of price erie. Average dieel oil price. Eponenial decline rae parameer of prodcion rae. Lag operaor indicaing he difference beeen an obervaion a ime and at time . Mathematical constant that is the base of natural logarithms. Market value of equity in WACC.

xpected value of a variable. xpectation of real-world cash flow at time . xpectation of risk-neutral cash flow at time . th largest eigenvalue of matrix in Johansen procedure. xpansion factor of gas in a reservoir. andom error term at time . Market risk premium defined as the harpe ratio.

Intensity of Poisson arrival process in jump diffusion model.


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Intensity of Poisson arrival process for a downward price jump. Intensity of Poisson arrival process for an upward price jump. Price of futures contract maturing at time .

Average fuel oil price. Matrix in Johansen procedure. Number of time lags indicating the number of time steps an observation

lies before the observation at time . Number of autoregressive lags. Number of moving-average lags. Hurst exponent indicating the persistence of a time-series trend.

Indicator for null hypothesis in statistical tests. Indicator for a certain state. Indicator for a certain state other than . Average jump size in jump diffusion model. Jarque-Bera test statistic. Jarque-Bera test statistic adjusted for autocorrelation. Johansen procedure test statistic for rank and time series.

Dividend payment or net convenience yield. Mean-reversion rate in Ornstein-Uhlenbeck process. matrix in Johansen procedure. matrix in Johansen procedure. Total expected return of an asset including possible dividends or net con-

venience yield. Average jump size in jump diffusion model.

th central moment of a time series. Average monthly effect of price series, for month . Number of observations in a data set. Cumulative normal distribution. Net-to-gross ratio of a rock formation. Constant volatility, square root of variance of returns. Time-dependent volatility. tandard deviation of jump size in jump diffusion model.


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hifting parameter in annual seasoning function. hifting parameter in semi-annual seasoning function. Value of a tracking portfolio at time .

Pearson correlation coefficient. Porosity of a rock formation. ignificance level. Transition probability from state to state . Discounted risk-neutral transition probability from state to state . Mathematical constant . isk-neutralarbitrage-free value of a derivative.

Ljung-Box test statistic. Correlation coefficient. equired return on debt in WACC. Initial production rate of a gas field. Production rate of a gas field at time . equired return on equity in WACC.

isk-free interest rate. Market return. ank in Johansen procedure. Coefficient of determination. esidual at time . esidual for state at time. esidual for state at time .

esidual sum of squares. Input variable in an artificial neural network. aturation level of a rock formation. igmoid function in an artificial neural network. Average asset price over a certain amount of time. Price of electricity. Price of natural gas. Price of the underlying asset at time .


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park spread at time. matrix in Johansen procedure, being the product of and .

matrix in Johansen procedure used to remove autocorrelation at lag

. Time point expressed in days, also used as indicator of the value of anothervalue at time .

, Critical value of t-distribution with significance level and degrees offreedom. Maturity date of an option or contract, expressed as the total number of

time steps. Corporate tax rate.

Vector containing deterministic parameters in Johansen procedure. Volume of a rock formation. Parameter reflecting weighted long-term volatility in GACH model. Wiener process at time . Weighting variable with indicator. Weighting variable for diesel oil price Weighting variable for fuel oil price.

Prespecified strike price of an option. trike price of an option at time . Average return over a certain amount of time. Logarithmic return of an asset at time ., Logarithmic return of electricity spot price at time ., Logarithmic return of gas spot price at time . Payoff occurring only in state ., Binar variable hich ha a vale of 1 if and 0 if . Op of arificial neral neor. Vecor decribing ime erie. Variable in Hr e, decribing he mmed deviaion from he mean po poin .() Point in joint distribution of return . Vector in Johansen test.


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Abstract ............................................................................................................................................................................................. iv Glossary .............................................................................................................................................................................................. v

Glossary financial theory ....................................................................................................................................................... v Glossary energy market ...................................................................................................................................................... vii Acronyms and abbreviations .............................................................................................................................................. ixMathematical notations used............................................................................................................................................... x

Table of contents ......................................................................................................................................................................... xv 1. Inrodcion ...................................................................................................................................................................... 2

1.1. Bacgrond ................................................................................................................................................................. 31.2. Reearch prpoe ................................................................................................................................................... 41.3. Reearch eion .................................................................................................................................................. 41.4. Worflo ...................................................................................................................................................................... 5

2. Inrodcion o naral ga and energ in he Neherland ........................................................................ 82.1. Properie of naral ga ....................................................................................................................................... 9

2.2. Naral ga in he Neherland ........................................................................................................................... 92.3. Naral ga prodcion in he Neherland ................................................................................................ 102.4. Characeriic of he Dch energ mare .............................................................................................. 122.5. Parie in he Dch energ mare ............................................................................................................... 13 2.6. Vale chain of he Dch energ mare ..................................................................................................... 16 2.7. Ga-o-Wire............................................................................................................................................................... 182.8. Energ rading in he Dch energ mare .............................................................................................. 19

3. Real opion heor ...................................................................................................................................................... 223.1. Tradiional valaion mehod ........................................................................................................................ 233.2. Dicon rae ............................................................................................................................................................ 243.3. Inrodcion o real opion anali .............................................................................................................. 26 3.4. Ri-neral valaion ......................................................................................................................................... 293.5. Replicaing porfolio concep ........................................................................................................................... 323.6. Tpe of ncerain ............................................................................................................................................. 343.7. Ri adjmen ...................................................................................................................................................... 35


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3.8. Integrated risk-neutral approach ................................................................................................................... 36 3.9. stimating volatility ............................................................................................................................................. 38 3.10. Variable rie price ............................................................................................................................................. 383.11. Compeiion .............................................................................................................................................................. 383.12. Unpecified eercie and mari dae .................................................................................................... 393.13. Sbopimal eercie policie ............................................................................................................................ 39

4. Real opion modelling in Ga-o-Wire prodcion ....................................................................................... 404.1. Problem decripion ............................................................................................................................................. 414.2. Uncerain abo reerve ize ........................................................................................................................ 41

4.3. Well prodcivi ................................................................................................................................................... 424.4. Price erie for energ commodiie ............................................................................................................. 444.5. Sale raegie ........................................................................................................................................................ 464.6. Diance o ranpor neor ......................................................................................................................... 464.7. Generaor properie ........................................................................................................................................... 464.8. Dch a regime .................................................................................................................................................... 474.9. Legal developmen .............................................................................................................................................. 48

5. Diagnoic eing of price daa ............................................................................................................................. 505.1. Prpoe of diagnoic eing ........................................................................................................................... 515.2. Hiorical daa e ................................................................................................................................................ 515.3. Vial obervaion of price erie .................................................................................................................. 525.4. Saionari ............................................................................................................................................................... 555.5. Coinegraion ........................................................................................................................................................... 575.6. Aocorrelaion....................................................................................................................................................... 595.7. Time effec .............................................................................................................................................................. 625.8. Mean-reverion of price erie ........................................................................................................................ 675.9. Normali................................................................................................................................................................... 685.10. Cro-correlaion ................................................................................................................................................... 715.11. Price jmp ............................................................................................................................................................... 73

5.12. Chaper mmar .................................................................................................................................................. 756. Technie for modelling energ price .......................................................................................................... 76


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6.1. Geomeric Bronian Moion ............................................................................................................................ 776.2. Ornein-Uhlenbec proce ............................................................................................................................. 776.3. Jmp diffion model ........................................................................................................................................... 776.4. Binar variable...................................................................................................................................................... 786.5. Sinoid ime fncion ......................................................................................................................................... 786.6. Co-dependenc modelling ................................................................................................................................. 796.7. Conan volaili ................................................................................................................................................. 796.8. GARCH ......................................................................................................................................................................... 806.9. Oher modelling echnie .............................................................................................................................. 81

6.10. Eimaing ri-neral drif ih fre price .................................................................................... 827. Conrcion of price forecaing model ........................................................................................................ 847.1. Eimaing ri-neral drif ........................................................................................................................... 857.2. Modelling ga price erie .................................................................................................................................. 867.3. Modelling elecrici price erie .................................................................................................................... 887.4. Cro-correlaion beeen rern erie .................................................................................................... 94

8. Conrcing he real opion model .................................................................................................................... 96 8.1. Real opion rcre ........................................................................................................................................... 978.2. Deciion ree rcre ........................................................................................................................................ 978.3. Parameer vale ................................................................................................................................................... 978.4. Valaion mehod for real opion ............................................................................................................ 1028.5. Longaff-Scharz algorihm ....................................................................................................................... 1028.6. Spar pread a ae variable and prodcion hrehold ................................................................ 1058.7. Ke ampion ................................................................................................................................................. 1059. Simlaion d ....................................................................................................................................................... 1089.1. Sep of imlaion d ................................................................................................................................ 1099.2. Approimaion of re opion vale ........................................................................................................... 1099.3. Comparion beeen real opion and deciion ree anali ......................................................... 1109.4. Scenario anali ................................................................................................................................................. 111

9.5. Simlaion rel ............................................................................................................................................... 11310. Conclion ................................................................................................................................................................. 116


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10.1. Theoreical evalaion of real opion anali ....................................................................................... 11710.2. Pracical evalaion of real opion anali ............................................................................................ 11810.3. Applicaion of real opion model o Ga-o-Wire prodcion ......................................................... 118

11. Dicion and frher reearch ....................................................................................................................... 12011.1. Validi of opion heor ampion ...................................................................................................... 121 11.2. Treamen of non-diverifiable privae ri ........................................................................................... 121 11.3. Co-dependenc beeen mliple opionaliie ................................................................................... 12111.4. Upper bond of opion vale ......................................................................................................................... 12211.5. Improvemen of he elecrici price forecaing model ................................................................... 122

11.6. Co-dependenc beeen price erie ........................................................................................................ 12311.7. Opion model improvemen ......................................................................................................................... 12311.8. Volaili redcion ............................................................................................................................................ 12312. Bibliograph ............................................................................................................................................................... 124Appendice .................................................................................................................................................................................. 144

Appendi I: Oher modelling echnie .................................................................................................................. 144Appendi II: Diagnoic eing of model reidal ............................................................................................. 148 Appendi III: Sorce coding ........................................................................................................................................... 150

13.


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Chaper 11. I

The energ indr i becoming increaingl comple and ncerain. Operaor

in he indr face more difficl invemen deciion, calling for advanced and

fleible deciion maing ool. In hi d he applicaion of real opion anali

i reearched, hich rcre real orld invemen deciion in a a imilar

o financial opion. To bring real opion anali ino pracice, in hi d e

conrc an opion model for an invemen problem in he field of Ga-o-Wire

prodcion.

We provide a bacgrond of he hiorical, preen and fre ae of he Dch

energ indr. Energ prodcer have o deal ih developmen in legilaion,

mare, echnolog and reorce. The involved ncerainie ma preen

profiable opporniie, b more han ever reire ic and adeae re-

pone o changing condiion. From hi perpecive, i i inereing o con-

ider he role ha real opion anali cold pla in fre deciion maing. The

prpoe of hi d i o increae inigh in he role real opion anali cold

have in he valaion of projec, pariclarl in he energ indr. We formall

ae or reearch goal in a nmber of reearch eion. Finall, e eplainhe mehodolog e e for hi d.


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3

1.1. BAt the time of writing this thesis, the Dutch energy market was going through a series of impor-tant developments. As a consequence, exploiters of gas fields in the Netherlands face uncertain-ties, opportunities and problems not encountered before. Decision making in such an environ-ment is difficult, requiring support from advanced decision tools. uch a decision tool is realoption analysis. By modelling real investment problems in an option framework, it explicitlyaddresses managerial flexibility with respect to changing and uncertain market conditions. Inthis study we focus on the application of real option valuation in the energy industry.The subsurface of the Netherlands is rich in natural gas. The Groningen gas field is the largestfield in all of urope and is in the top ten of largest fields worldwide Correlj et al., 2003. Doz-ens of medium-sized gas fields also contribute to the amount of gas significantly. The Nether-lands have made heavy use of this natural resource, having met the bulk of energy requirementswith natural gas for the last decades. The exploitation of gas fields has been a major source ofincome for the Dutch state. Historically the large Groningen gas field helped providing all the

natural gas demanded in the Netherlands. After decades of production its supply capacity hasdecreased significantly. oon, the field will no longer be able to cover the gap between demandand the supply of other Dutch gas fields. To maintain the balancing function of the Groningen gasfield as long as possible, the government stimulates the development of other gas fields in theNetherlands with protection and fiscal measures. The Groningen gas field holds natural gaswhich contains relatively little hydrocarbons, making it a low-caloric gas. Other Dutch gas fieldsoften contain high-caloric gas. The main transport network and many applications are fitted tothe composition of the Groningen gas, posing difficulties when the percentage of productionfrom oher field increae (Energieeze, 2011). Energie Beheer Nederland (financial govern-men parner paricipaing in all ga field eploi) ha he e he goal o prodce 30 billion m3

of naral ga from field oher han he Groningen field in 2030, he o-called 30/30

ambiion.No onl in he Neherland, b alo globall, foil fel orce ffer from rong depleion. Inpariclar he ma-cale eploiaion of oil ha noable effec; e approach he poin he de-mand for oil ill eceed ppl permanenl. To be able o ill mee energ demand in he f-re more orce need o be fond. Hdrocarbon reorce ha ere previol naraciveeconomicall (e.g., oil/ga field a ea and in difficll acceible formaion) are eploiedno or ill be in he near fre, hen prodcion of he eaier acceible field i declining.Technological developmen a ell a mare condiion are imporan for he eploiaion ofch field.

Anoher major rend i he raniion o reneable energ. To imlae ch developmen,prodcer of reneable energ have a preferred poiion in he energ mare; he are im-laed ficall and he energ he ell ha priori over convenional energ. Hoever, rene-able energ orce ofen have an inermien op. To enre ha demand can be me a allime, naral ga ha an imporan balancing fncion de o i fleible and fa prodcionopporniie. A ch, an increaing hare of reneable energ affec he role of naral ga(Roadmap 2050, 2011). Eropean energ mare ere previol largel governmen-conrolled, ince abo a decade ago he Eropean Union ha been riving for an inegraed andliberalied energ mare. The Dch energ mare ha been open ince 2004, e compeiioni ill ie nderdeveloped. I i ncerain ho liberaliaion of he energ mare ill developand ha effec hi ill have.Under hee circmance companie ee innovaive mehod o develop field ih lo eco-nomic aracivene on he fir vie, ch a marginal or almo depleed field. A poible


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manner to exploit such fields is Gas-to-Wire production, which is the generation of electricityfrom natural gas by placing a motor or turbine at the field itself. Laying pipelines and compress-ing natural gas is then not required. elatively high investments are required for Gas-to-Wire,therefore the profitability of such a project strongly depends on correct responses to uncertain-ties. In this study, we will assess the application of real option valuation on Gas-to-Wire produc-tion.

1.2. eal option analysis is a valuation technique based on financial option theory, treating realworld projects as if they were financial assets. It distinguishes itself from traditional techniquesby explicitly incorporating managerial flexibility during the project and the effect of altering therisk profile due to decisions made. The main goal of this study is to increase understanding ofreal option valuation and the possible merits it has regarding the valuation of flexibility com-pared to those of other decision tools. We perform an extensive literature study to assess thedifferent views on real options.

A central issue in real option theory is the distinction between market and private risk; as op-tions are valued under the assumption that the risk of price changes in the underlying asset canbe hedged, in principle only risks that are liquidly traded on the market are eligible for optionvaluation. We address the presence of non-hedgeable private risk in real projects, and howsuch uncertainties can be treated in a real option structure. Another core aspect we research isthe adjustment of discount rates to the changing risk profile of a real option. We describe howcash flows can be adjusted properly for risk, explaining how risk-neutral valuation can be ap-plied for this purpose.We illustrate the application of real option valuation with a simple real option model applied to

Gas-to-Wire production, determining the economically optimal point to switch from gas produc-tion to electricity generation. The construction of this model shows how we can deal with thetheoretic issues in a practical setting. The main goal of constructing the model is to compare theinsights it offers compared to traditional decision making tools. We refrain from drawing strongconclusions about the real-world attractiveness of Gas-to-Wire; reliable data for this innovativeproduction method is limited, and results are strongly influenced by project-specific factors.The value of the real option at a given point in time partially depends on the prices of natural gasand electricity respectively. We try to increase insight to the nature of the behaviour of theseprice series. Issues that we consider are the theoretical behaviour of the series, diagnostic test-ing on historical price series, modelling techniques used in commodity pricing and estimating

the risk-neutral drift. For the option model presented in this study we attempt to create realisticprice models, without attempting to optimally reflect the observed behaviour.1.3.

The main goal of the study is to answer in which respects real option analysis can provide addi-tional value compared to thestate-of-the-art decision tools that are applied in practice. We posethe main research question here:

1.3.1. How does the insight in the value of flexibility stemming from real option analysis on Gas-to-

Wire production compare to the insight obtained from dynamic decision tree analysis


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1.3.2. We pose seven sub-questions which together answer the main question. Table 1 provide anovervie of he chaper in hich hee b-eion are reaed. To mae he comparionbeeen ROA and DTA, e m fir pecif ha definiion of ROA e e. For hi, e addre

o core ie for hich no rivial aner are available. Theoreicall, privae ri are noviable for opion pricing; e d everal viepoin on ho ROA can deal ih ch ri(eion 1). The oher ie i ho e can adj he dicon rae oard he changing riprofile of a projec (eion 2). Anering hee o b-eion la he grondor of orreal opion model. To appl ROA on a Ga-o-Wire cenario, e m or o a rcre forhe invemen problem. Or foc lie on he behavior of he price erie of naral ga andelecrici (eion 3), and ho hee characeriic can be modelled. We alo conider a-pec on he echnical ide of prodcion for boh reglar ga prodcion and Ga-o-Wire (e-ion 4), b do o in le deail. Afer idenifing he eparae procee, e ee ho e cancombine hem in a real opion model (eion 5). We e ho he real opion model diin-gihe ielf b comparing i ih a imilarl rcred deciion ree model. We rn everalcenario ih boh model, o ha e can compare heir rel (eion 6). Finall, e aim oprovide ome efl ggeion for frher reearch, hich cold frher increae he inighgained from ROA (eion 7).

1) Ho can e addre he preence of privae ri in a projec in a real opion frameor?2) Ho can e accon for he changing ri profile of a fleible projec in diconing?3) Ho can e model he behavior of price erie for naral ga and elecrici?4) Wha are he echnical characeriic of ga- and Ga-o-Wire prodcion?5) Ho can e conrc a real opion for valing Ga-o-Wire prodcion?6) Ho do he rel of real opion valaion compare o dnamic deciion ree anali

ih repec o he vale of fleibili?

7) Wha frher reearch can be performed o increae inigh in he applicaion of real op-ion valaion in pracical eing?

C 1 Chaper 3 Real opion heor2 Chaper 3 Real opion heor3 Chaper 4Chaper 5Chaper 6Chaper 7Inrodcion o price behavior of energ commodiieDiagnoic eing of hiorical daaDecripion of modelling echnieModelling and eimaion of price model

4 Chaper 4Chaper 8 General ie of Ga-o-Wire opionModel-pecific ie of Ga-o-Wire opion5 Chaper 8 Conrcion6 Chaper 9Chaper 10 Simlaion ep and relConclion7 Chaper 11 Sggeion for frher reearch 1: .

1.4. In hi ecion e decribe he chronological ep e ae in hi d. The fir ep of heprojec i o increae inigh in ho he Dch ga and elecrici mare or. Thi inclde adecripion of he phical prodcion procee, legilaion and he main parie involved. Ahe eploiaion of a ga field can ae mliple decade, i i imporan o have an inigh in boh


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current and future developments in the energy market. The main information sources we useare standard works and internal sources within the Petroleum Geosciences department of TNO,complemented with information made publicly available by the main parties in the market.A great body of literature on real option analysis exists. Different schools of real option theory

co-exist, each with their own view on what real options are and how they should be applied. Wetry to distinguish between these approaches and their implications, without attempting to ex-plicitly compare them. Instead, we deduce the most suitable approach by testing their fits withoption pricing theory. For this purpose, we pay special attention to the concept of risk-neutralvaluation in option pricing, replicating portfolios and the distinction between market and pri-vate risk.The behaviour of commodity price series is known to be notably different from that of financialstocks. In particular electricity prices follow a unique and complex pattern. We assess literatureconcerning the characteristics of these price series to gain more insight in their behaviour.Based on this information, we perform several diagnostic tests to check whether the actual be-haviour of historical price data is consistent with theory. For each test we provide a theoreticalintroduction. We investigate a number of techniques in price series modelling. These techniquesare used as building blocks of the eventual price series model. Based on the results of the diag-nostic tests and the characteristics of the techniques assessed, we build price models for naturalgas and electricity. We estimate their parameters based on the available price data.After assessing the theoretical issues we perform research on the characteristics of a Gas-to-Wire project in a real option framework. Notable issues that we treat are the uncertainty of thereservoir size, physical production constraints and the required investments. We combine theprice series models and several other components with models on well productivity and reser-voir size into a real option model. We also build a dynamic decision tree on the same investmentproblem, to compare the performance and differences of both valuation techniques. Afterwards,we perform scenario to test for several developments we deem uncertain. The study is com-pleted with a conclusion and recommendations for further research. These focus on both theapplication of real option analysis in general and the performance and possible improvements ofthe Gas-to-Wire model.


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Chapter 2

2. I

To provide a background on the natural gas industry, we give a brief introduc-

tion in this chapter. First we discuss the physical nature of natural gas and its

main applications. Then we give an overview of natural gas in the Netherlands,

describing its importance in a historic, present and future perspective. In par-

ticular, we pay attention to the role of the major Groningen gas field. Also we

explain the production process of natural gas, with a focus on exploitation in the

Netherlands.

After describing the role of natural gas in the Netherlands in general, we go in

more detail about the Dutch energy market. We describe the market structure

for both natural gas and electricity. We pay attention to the transition from a

national, government-controlled market to an integrated and liberalised uro-

pean energy market, and the issues that play a role in this transition. Also we

discuss some important trends, such as the transition to renewable energy and

the preservation policy of the Groningen gas field.

We describe the main parties involved in both the natural gas and electricity

market, linking their roles in flowcharts. In a separate section, we explain what

Gas-to-Wire production is and the position it has in the energy market. Finally,

we explain the pricing processes for gas and electricity. We describe how prices

are established, what characteristics they have and how future developments

might affect prices.


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2.1. Natural gas, as referred to in this study and as found in the Netherlands, is a gas mixture whichconsists mainly of several hydrocarbon gases particularly methane and some other gases(NaralGa.org, 2011a). I i a combible ga mire; i high energeic vale mae i a e-fl energ orce. Naral ga can be convered ino elecrici, b i i alo ed direcl bend-er. Some of i main applicaion b end-er are heaing of bilding, aer heaingand cooing. Finall, naral ga i alo ed a a feedoc in he chemical indr. Naral gai a relaivel clean reorce, caing le pollion han oher foil fel (Eroga, 2007;Unied Sae Environmenal Proecion Agenc, 2007). Brning naral ga emi onl minoramon of oo, and releae far le carbon dioide ino he amophere han oher foil en-erg reorce. When peaing in he cone of energ orce, naral ga i ofen impldbbed ga. The erm ga and naral ga are ed inerchangeabl in hi d.Naral ga i fond beneah he rface, accmlaed in poro geological formaion hich liender a dener laer of roc (NaralGa.org, 2011a). To releae he ga, a hole m be drilledhrogh hi laer of roc, alloing he ga o reach he rface. A he ga i all nder highprere, i flo p b ielf hrogh he ell. Afer being rerieved, he ga i led hrogh aprificaion proce in order o mae he mire feaible for ranporaion and age. Thecompoiion of naral ga depend on i origin. In Table 2, e ho he componen allpreen in naral ga and compare hem o hoe preen in Groningen ga (Van Thillo, 2008).The precie compoiion deermine he energeic vale of he ga (NaralGa.org, 2011a). TheWobbe inde relae he relaive deni of ga o i caloric vale, and erve a an indicaor forhe inerchangeabili of fel gae. Naral ga m be proceed in ch a a i can be of-fered in a cerain compoiion. If he ga mire differ oo mch in ali, hi old poeproblem for he inallaion reling on naral ga. In pracice hi mean he ga in a dirib-

ion em m ala have a Wobbe inde ihin a cerain margin.C Tpical compoiion Groningen gaMehane 70-90 % 81.2 %Higher hdrocarbon 0-20 % 3.6 %Carbon dioide 0-8 % 0.9 %Ogen 0-0.2 % 0 %Nirogen 0-5 % 14.3 %Hdrogen lphide 0-5 % 0 %Rare gae Trace Trace 2: G .

2.2. In 1959, a major ga field a dicovered in he province of Groningen (Correlj e al., 2003). Aone of he large ga field in he orld, i had he poenial o ppl he conr ih energfor decade. Upon i dicover he Dch ae decided o conrc a naionide ga ranporneor o mae e of hi nefond reorce (Van Overbeee, 2001; GaTerra, 2008). Noa-da mo bilding in he Neherland are heaed ing naral ga, acconing for 78% of hedirec naral ga conmpion b Dch hoehold (Jeeninga, 1997; Energieereld.nl, 2009).The remaining conmpion coni of heaing aer and cooing. Beide he large direc con-mpion of naral ga, i i alo he main orce for generaing elecrici in he Neherland.According o he Dch Cenral Brea of Saiic (CBS), in 2010 62.3% of he oal amon ofelecrici generaed in he Neherland emmed from naral ga (Cenraal Brea voor deSaiie, 2011). In Table 3 he hare of energ orce in elecrici generaion are provided.


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For the Dutch government, the exploitation of natural gas has been a major source of income,stemming from direct income, profit taxes and royalties on natural gas Correlj et al., 2003.Aside from the large share natural gas has in electricity generation, natural gas is also of impor-tance to guarantee a stable electricity output towards end-users Trster et al., 2011). Nclearplan and coal-fired poer aion are infleible in reponding o change in hor-erm de-mand. Reneable energ orce are dependen on varing eaher condiion ch a indand nligh, and herefore provide an inermien op. Frhermore, i i difficl, energ-inefficien and epenive o ore an overprodcion of elecrici (He, 2007). In order o re-pond o changing demand paern icl, he generaion of elecrici from ga i a necei.Elecrici can icl be generaed from naral ga b ndergoing a relaivel imple proce.Sorage faciliie and he ga ranpor em ielf allo oring large amon of ga for faelecrici generaion hen reired.

% D

Natural gas 62.3 %Coal 18.5 %Oher foil fel 3.7 %Nclear energ 3.4 %Reneable energ 9.4 %Oher orce 2.7 %

3: D (CB, 2010).

Several chemical can be made from naral ga, hich in rn are ed o creae prodc cha plaic, plood, pain ec. Alo naral ga become increaingl imporan a a fel orcefor car. In Table 4 e caegorie he orldide e of mehanol, he main ra maerial pro-dced from naral ga (Weelingh

e al.

, 1991). % Mehanal 36 %Ra maerial o prodce eer 22 %Solven and divere prodc 22 %Aceic acid 11 %Fel 9 % 4: ( , 1991).

Wih he increaing collaboraion beeen member ae of he Eropean Union, he role of he

Dch ga indr ha become increaingl imporan from a Eropean perpecive a ell. TheDch ga reerve are he bigge of he Eropean Union. The Neherland crrenl eporabo o-hird of heir prodced ga (Cenraal Brea voor de Saiie, 2012). Hoever, bhe ime of 2025 he governmen epec he Neherland o be a ne imporer of ga(Rijoverheid.nl, 2012b). The governmen plan for he Neherland o fncion a ga iner-ecion in he fre, ing he Dch ranpor neor and orage capaci o pla a cenralrole in imporing and eporing ga in he norhe of Erope (Van der Hoeven, 2009a).2.3. The fir ep in dicovering naral ga field i eimological reearch. Ga field in he Neher-land are all fond beeen 2000 and 4000 meer beneah he rface (Toal E&P, 2012).

B generaing arificial vibraion and analing he rerning echoe, geophici can map hegeological rcre of a cerain area. Thi i an imporan inp o eablih he probabili ofhe preence of a ga field. If a high probabili of a banial volme of naral ga ihin a


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certain structure is interpreted, an exploration well must be drilled to prove that gas is actuallypresent (NaralGa.org, 2011b). Wih an eploraion ell a more accrae eimae of he re-ervoir ga volme and ga ali can be obained (Ga-Iniiall-In-Place: GIIP). In cae hedicovered naral ga reerve can be prodced in an economicall feaible manner, prodcion

ell are drilled on he ie. Some proceing of he ga i performed direcl a he ellhead(NaralGa.org, 2011c). Waer and pariclae olid (e.g., and, al) are removed on he po,a hee cold damage or cloa he pipeline. Sand and oher olid paricle are removed bing crbber. Aociaed aer i removed b a dehdraion proce.Afer he iniial filering of he rerieved naral ga, i i ranpored o a prodcion facili bmaing e of lo-prere pipeline. Ofen hee faciliie are locaed on or nearb he field. Inhe facili heavier hdrocarbon, ch a ehane, propane and bane are condened in a epa-raor b changing he emperare and prere, o ha he can be rerieved a individalcommodiie (NaralGa.org, 2011c). Thee eparae end-prodc are called naral ga li-id or NGL (Energ Informaion Adminiraion, 2006). If hdrogen lphide, carbon dioideor oher acidic gae are preen, he m be removed from he mire. If lphide are ob-ained, he can be ed for he prodcion of lphr. Finall, race of oher maer ch ahelim and mercr hold alo be eparaed from he ga mire. Once he ga ha been proc-eed o a mire ih a cerain percenage of mehane (roghl 80% for he lo-caloric ne-or and abo 90% for he high-caloric neor (Hoezoanderga, 2012)) i i compreed, oha i can be ranpored hrogh he high-prere ranpor neor (NaralGa.org,2011e). The Nederlande Ganie reire a prere of 66 o 80 bar hen receiving he ga onheir ranpor neor (Ganie, 2008). Varing he prere allo o ore le or more gain he neor depending on demand. Thi orage mehod i called line-pacing. More deneregional diribion neor connec he ranpor neor o he end-er.

The demand for naral ga i ignificanl higher dring iner, becae of i applicaion oheaing (Aalber e al., 2007; NaralGa.org, 2011d). Alo ihin maller ime frame demandpaern can be oberved, for eample difference beeen da and nigh. Prodced ga can beored hen here i no immediae demand. The ranpor neor ielf provide an amon oforage capaci de o i high prere. Frher, ndergrond reervoir are ed o ore ga.In general, more ga han demanded i prodced from he primar ga field in he mmer,hile in he iner demand eceed prodcion (Nederlande Aardolie Maachappij, 2011).Thi allo meeing pea demand dring cold da hen prodcion capaci i infficien. Theprere of a ga field decreae hen i i gradall depleed. A compreor or injecion ellma be reired o increae he prere of he prodced ga arificiall in order o mee he

reired prere of he ranpor neor, hi rel in higher marginal co (Energeia,2011). Alo he locaion and ize of a ga field deermine he co involved. Each field reireell o be drilled, he placemen of inallaion and connecing he ie o he ga diribionneor. Therefore, he amon of ga and he prevailing ga price m be fficien o maehe eracion of ga from a mall field economicall feaible. Eploiing ga field on ea re-ire higher invemen han on land. The profiable eracion of maller ga field i rongldependen on he echnologie available (TNO, 2008). Mo ga field have a lifeime beeenen and hir ear. A he end of prodcion, he inallaion hold be dimanled and heenvironmen hold be brogh bac ino i original ae (Shell, 2006). Safe meare mbe aen o enre ha no remaining ga ecape o he rface or o anoher brface forma-ion (Barcla e al., 2002). Afer inpecion and approval of he meare aen he ie i re-rned o i original oner.


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2.4. C D The Dutch energy market has quite a complex structure with interaction between the privateand public domain. Since he Elecrici Ac (1998) and Ga Ac (2000) ere inrodced in heNeherland, he energ mare hifed from a governmen-conrolled mare o a more liberalone (Nederlande Mededingingaoriei, 2012d). Thi liberaliaion a in compliance ihEropean Union gideline, eeing o reach an open Eropean energ mare in he long erm.Since 2004 Dch conmer have been free o decide hich energ compan provide hemih ga and elecrici, hile he companie can e heir on price (Rijoverheid.nl, 2012d).Depie aemp o creae an open mare, everal facor give cae for conined governmeninervenion on he mare. We briefl decribe ome of he main facor in hi ecion.

2.4.1. I Energ i a driving facor for a naion ocie and econom. I i herefore imporan ha areliable and able energ ppl ei (Minierie van Economiche Zaen, Landbo & Inno-vaie, 2011). The governmenal polic-maer have o conider mliple facor o garaneech a ppl. There are cerain ri involved hen imporing energ from poliicall nableconrie, reling on infleible energ orce, maing e of energ orce ih npredicableop and o on. Alo ome long-erm viion old liel no be aen ino conideraion in arl liberal mare (Correlj e al., 2003). The governmen herefore reain a cenral role in heenerg mare a a polic maer.

2.4.2. Thogh he nmber of energ companie ha increaed in recen ear, he radiional one re-ain a poerfl poiion in he mare (Eropa.e, 2007; Sia Parner, 2011). Energ companiefall nder he perviion of he Dch compeiion reglaor (Nederlande Mededinging-aoriei or NMa) o preven abe of heir poiion and o enre ha he energ marefncion properl. For eample, he reglaor oblige companie o preen fficien informa-ion o end-er (Nederlande Mededingingaoriei, 2012d). Anoher monopoliic apec ofhe energ mare i ha he ame ranpor- and diribion neor are ed for all ga andelecrici. I old be nfeaible if ever energ compan old have o conrc i on ne-or. A a coneence, he ranpor- and diribion neor are in he hand of o-callednaral monopoli. Thee parie are bjec o reglaion a ell.

2.4.3. Searching for and eploiing ga field reire large invemen, and i ala paired ihncerain of he amon of ga ha can be rerieved. To imlae prodcer o dicover and

eploi mall ga field, he governmen oblige he (pariall ae-oned) ga rading companGaTerra o b all naral ga prodced for a fair price and nder reaonable condiion(Rijoverheid.nl, 2012c). One of he reaon for hi mall field polic i o lo don heeploiaion of he Groningen ga field. The erm mall field refer o all ga field oher hanhe Groningen ga field; he acal field ize ma be banial. The polic help o preerve hefncion of he Groningen ga field a ing prodcer and long-erm naral ga reerve (Cor-relj e al., 2003).2.4.4. A he reerve of foil fel are epeced o be depleed dring he folloing decade, oherorce of energ m be aeed o mee energ demand in he fre. In addiion, he Ero-

pean Union ha e goal o rongl redce carbon dioide emiion (Minierie van Econo-miche Zaen, Landbo & Innovaie, 2011). For hee reaon, a large-cale raniion from hecrren energ orce o reneable energ i planned. The governmen inervene o imlae


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the development and use of renewable energy sources, enabling them to compete with thecheaper fossil fuels.

2.4.5. Producers must obtain a license from the government if they wish to explore a certain area for

ga, eploi a ga field or ore ga (Rijoverheid.nl, 2012c). A earching for and eploiing gafield are aciviie ha ma have coneence for he environmen and he afe of henearb-living reiden, ga prodcer are no alloed o do o iho formal approval of heDch ae. For each licene applicaion, a d i performed o ae he coneence ofpropoed aciviie.2.4.6. A energ companie have a reponibili oard ocie, he are bjec o reglaion andm obain a licene before enering he mare. Some of he reiremen an energ companha o mee are financial abili, providing clear informaion o end-er and offering reaon-

able pamen arrangemen before hing don he energ ppl o an end-er (Neder-lande Mededingingaoriei, 2012d).2.5. D 2.5.1. GThe N.V. Nederlande Ganie, ofen referred o impl a Ganie, i a ae-oned companhich poee and manage he Dch main ga ranpor neor. Their core a are bild-ing and mainaining he ga ranpor neor, and he ranporaion and orage of naralga (Ganie.nl, 2012). Ta relaed o ranporing ga are performed b Ga Tranpor Ser-vice, a 100% bidiar compan of Ganie. Ganie operae o main ranpor neor,

one for high-caloric ga (HC) and one for lo-caloric ga (LC) (Correlje al.

, 2003; Rijover-heid.nl, 2012a). The naral ga from he Groningen ga field i LC, oher Dch field provideHC. LC i delivered o hoehold and epored o oher conrie, hile HC i ed b largeindrial parie and for generaing elecrici b energ companie. Ga Tranpor Servicecan conver HC o LC b adding nirogen. Converion he oher a arond i merel an admin-iraive ap: LC i no phicall rned ino HC. Beide oning he Dch ga ranpor ne-or, Ganie alo (co-)on everal neor in oher Eropean conrie. In hee conrie,he aciviie of Ganie are bjec o he reglaion of he repecive conrie.2.5.2. There are everal regional parie hich on and conrol he ga diribion neor in a cer-

ain area (Rijoverheid.nl, 2012a). Thee regional neor have a high deni, reire a loga prere (ome 8 bar), and diribe ga from he main ranpor neor o he end-er.The la oblige hee parie o be legall independen from he energ companie(Galich.com, 2011). A ome energ companie in he pa alo oned regional diribionneor, he had o place hee neor in hand of independen eniie. The Dch aeha a majori hare in all hee parie. Ga Tranpor Service charge a ranpor fee o re-gional ga ranmiion em operaor. A mo ga i prodced in Groningen, hi ranporfee depend on he diance from heir ga field, hence ranporaion become increainglmore epenive he farher aa from Groningen. The ranmiion em operaor rechargehi fee o end-er.2.5.3. G

GaTerra i a former par of he Nederlande Ganie. In 2005, i a pli off from he Ganie,a Eropean gideline precribe ha he ranpor and ale of ga hold be carried o b


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separate entities (GaTerra, 2012b). Ganie reained he a of managing he ranpor ne-or, hile GaTerra oo over he a o rade in naral ga. While he Ganie i compleeloned b he Dch ae, onl half of he hare of GaTerra remained in he hand of he ae(10% direcl, 40% via Energie Beheer Nederland). The oher hare are oned b Roal Dch

Shell (25%) and EonMobil (25%) (GaTerra, 2012a). GaTerra b naral ga direcl fromhe prodcer, coneenl elling i o energ companie, indrial er or eporing i ooher conrie. The are obliged b la (pecificall, Aricle 54 of he 2000 Ga Ac) o procrega from mall field prodcer again a reaonable price (Van der Hoeven, 2009b; Energie Be-heer Nederland, 2012b). GaTerra ha had a fied annal profi of 36 million ero for ear;he correc heir conracal price a he end of he ear o obain hi profi (GaTerra, 2009).2.5.4.

In addition to GasTerra, several other gas traders shippers are active in the Netherlands.These traders only entered the market recently as a result of the liberalisation; GasTerra is stillthe major gas trader in the Netherlands Van der Hoeven, 2010). Unlie GaTerra, oher raderhave no obligaion o procre ga obained from mall field. All rader m obain a licenebefore being alloed o rade in naral ga.

2.5.5. A The Nederlande Aardolie Maachappij (NAM) i he large prodcer of naral ga in heNeherland, prodcing abo 75% of Dch ga (Nederlande Aardolie Maachappij, 2012).The compan i oned b Shell (50%) and EonMobil (50%). NAM i he ingle eploier of heGroningen ga field (in parnerhip ih EBN), and a ch operae he large naral ga re-erve of he Neherland. Half of he ga he prodce em from he Groningen ga field, 25%come from maller field in he Neherland, and 25% i prodced from field in he Norh Sea.2.5.6. E&

Beide NAM, here are everal oher parie acive in he dicover and eploiaion of gafield in he Neherland. Sch parie are called Eploraion & Prodcion-operaor (E&P-operaor or impl operaor). A he Groningen ga field i eploied olel b NAM, he re-maining operaor are acive on oher Dch ga field ling boh on- and offhore.2.5.7. E B Energie Beheer Nederland B.V. (EBN) i a compan oned b he Dch ae, fncioning a aparner in he dicover and eploiaion of naral ga. EBN i no involved in hee aciviie aan operaor, b raher a a financial parner. EBN alo faciliae Dch E&P-operaor ih ieperie and noledge (Energie Beheer Nederland, 2012a). When an operaor obain a li-

cence alloing i o eplore for naral ga in a cerain area, b la i can ree EBN o par-icipae in he eploraion proce (Energie Beheer Nederland, 2012b). In hi cae, EBN illobain a 40% hare in he paricipaion, aiding he operaor in he earching proce. The arebjec o he ame ri-and-reard profile a he operaor. B pliing he co, he prodcerha a loer financial hrehold for aring he eploraion proce. Afer an operaor ha ac-ired a prodcion licene, EBN all paricipae on a 40% bai a ell. Unlie he eplora-ion proce, he operaor ha no a in hi; onl he governmen can decide o ihhold EBNfrom paricipaion (Energie Beheer Nederland, 2012b). The operaor and EBN again are bjeco he ame ri-and-reard profile. In cae EBN did no paricipae in he eploraion proce,40% of he co for hi proce ill be reimbred. Beide heir paricipaion in a large nm-

ber of prodcing ga field, EBN ha an advior role oard he polic maer of he govern-men a ell. Frher EBN on a 40% hare in GaTerra, and herefore ha an inflence in herade of naral ga.


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2.5.8. E Energ companie ell ga and/or elecrici o end-er. The companie prchae naral gafrom ga rader, elecrici i bogh from an inermediae rading par (Programme Repon-ibili Par), hich i ofen oned b he energ compan ielf (Anoordvoorbedrijven.nl,

2012). Energ companie operaing in he Neherland fall nder he reglaion of he NMa, bare free o e heir on conmer price. In recen ear, ne energ companie have eneredhe mare, increaing he compeiion in he energ mare. Mo energ companie on in-allaion o generae elecrici, b elecrici can be procred from oher prodcer or fromabroad a ell. When an energ compan ell reneable energ iho oning inallaiono prodce i, he m prchae cerificae repreening a hare in reneable energ prodc-ion. The mliple role of energ companie mae i ambigo hich role i referred o henpeaing of an energ compan. Therefore, in hi d e onl e he erm energ companhen referring o i role a pplier of ga and elecrici o end-er conneced o he diri-bion neor, ch a hoehold and mall indrial er. For i oher role, e e heerm elecrici prodcer and Programme Reponibili Parner (rader) repecivel.2.5.9. TenneT TSO B.V. (hereafer TenneT) manage he Dch high-volage neor (TenneT, 2008).I can be een a he conerpar of Ganie reponible for he elecrici ranpor neor,operaing in a highl imilar rcre. TenneT i compleel oned b he Dch ae (TenneT,2012). I i reponible for he afe and effecive ranporaion of elecrici hrogho heNeherland.2.5.10. Lo-volage regional diribion neor are conneced ih he high-volage neor ofTenneT, alloing he diribion of elecrici o hoehold and oher end-er. Thee re-

gional neor are oned and managed b everal parie. The Dch ae ha a majorihare in all ranmiion em operaor. Energ companie are no longer alloed o on aregional diribion neor. The ranporaion co recharged o end-er are he ame forall region in he Neherland (Galich.com, 2011).2.5.11. E Elecrici can be prodced from everal orce, all reiring a pecific inallaion. In mancae, hee inallaion are oned or pariall oned b energ companie, b independenprodcer ei a ell. A legal diincion i made beeen reglar elecrici and reneableelecrici. When energ companie ell reneable elecrici o conmer, he do no nece-aril on ch inallaion hemelve. Hoever, he need o prchae cerificae from a

prodcer of reneable energ, o prove ha he elecrici old em from a reneable energorce (Ween.overheid.nl, 2012).2.5.12. In he Dch energ mare, elecrici i raded b an inermediae par beeen prodcerand energ companie, named a Programme Reponibili Par or PRP (TenneT, 2011). Theeparie mae ranacion for he ppl of elecrici. B reporing hee ranacion o Ten-neT on a dail bai, TenneT can meare and ele he difference beeen he acal and heranaced amon of elecrici. PRP m obain a licene from TenneT in order o be acive aa rader. There are o pe of PRP: parie ha are onl alloed o rade, and parie ha are

reponible for he connecion ih he diribion neor a ell. Mo energ companiehave heir on PRP o prchae elecrici (Anoordvoorbedrijven.nl, 2012).


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2.5.13. EThe Nederlandse Mededingingsautoriteit, abbreviated to NMa, is the Dutch competition regula-tor. Its nergiekamer nergy department is the department of NMa responsible for the regu-lation of the Dutch energy market (Nederlande Mededingingaoriei, 2012a). I a are

decribed in he Elecrici Ac and Ga Ac. Among i core a are he licening of parieacive in he energ mare, eing ariff, evalaing he effecivene of he ranpor and di-ribion neor, and monioring he developmen in he energ mare (NederlandeMededingingaoriei, 2012c). The NMa Energieamer ha an inflence on all parie involvedin he energ mare, from he iniial dicover procee o he final ppl of elecrici andga o end-er. The goal of he reglaor are o proec conmer, imlae an open energmare and enre ha governmen policie are carried o.2.6. D Having decribed he main parie involved in he Dch energ mare in he previo ecion,a brief overvie ill be provided of ho hee parie are lined ogeher. In Figre 1 e pro-

vide an overvie of he Dch ga mare. Abo 75% of Dch naral ga i prodced b heNAM and 25% b oher operaor. EBN ha a ae in virall all ga eploiaion. Prodcedga can be old o GaTerra or oher ga rader; recall ha GaTerra i obliged o prchae gafrom mall field again a fair price. Naral ga i delivered o one of he o main ranporneor (oned and managed b Ganie) nder high prere: one ih high-caloric ga, oneih lo-caloric ga. Thee ranpor neor alo allo for orage. From he main ranporneor, LC i diribed frher o end-er b mean of lo-prere regional diribionem, oned and conrolled b regional operaor. Naral ga conmed b end-er iraded via energ companie. LC i alo epored o oher conrie direcl via he high-prere neor. HC can be old o energ prodcer, ho beenl generae elecrici

from he ga. Alo HC can be direcl delivered o large indrial er. The HC ranpor ne-or allo for epor o oher conrie a ell.


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F 1: D .

In Figure 2 we provide an overview of the Dutch electricity market. The structure of the Dutchelectricity market is quite similar to that of the gas market. A major difference however is theabsence of storage facilities, so that a constant balance between all parties must be established.As a consequence, the actual behaviour of gas and electricity markets is quite different, despitetheir comparable market structures. lectricity is produced from several sources, with a rele-vant distinction made between renewable and non-renewable sources. Programme esponsibil-ity Parties buy the produced electricity on a daily basis, consequently selling it to energy compa-nies, industrial users or other countries. TenneT is responsible for the transportation of electric-ity over the high-voltage network, after which regional operators transfer it to end-users using a

denser low-voltage network.


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F 2: D .

2.7. GGas-to-Wire is a term used to describe the process of generating electricity from natural gas ator close to the source Thomas & Dawe, 2003. This is considered a different production methodthan the generation of electricity from gas at a centralized power plant. Gas-to-Wire places a gasmotor or a gas turbine close to the gas field, allowing to convert gas into electricity directly.Generally the more efficient but heavier gas motors are placed on land, while the less efficientgas turbines are used at sea because of their lower weight which needs a lighter supportingstructure. From here on, a gas motor or turbine will be referred to as generator.Gas-to-Wire is a potential alternative to regular gas production for exploiting smaller fields inparticular, which may not be worth the investments required to connect to the main gas trans-port network Van den Berg, 2011; ABTechnolog, 2012). Correlj e al. (2003) claim ha heeconomic feaibili of eploiing a minor ga field i ofen dependen on he diance o heranpor neor. I migh be poible o eploi a marginal ga field hich i no economicallfeaible ih reglar eploiaion (called virgin field), or a ga field hich i an advanced ageof depleion (alo called a ail-end field). The elecrici generaed can be old o energ com-panie, b alo be ed o poer faciliie cloe b, for eample an inallaion a ea.The hor diance beeen he ga field and moor or rbine doe no allo for oring largeamon of ga, herefore managing he ga ppl o he generaor i one of he difficl fea-re of Ga-o-Wire prodcion (Van den Berg, 2011). The generaor i able o prodce elecric-i in a fleible manner and i able o deal ih monoone change in ga ppl on hor noice.Hoever, echnical problem occr hen dirbing ple ch a impriie in he ga arepreen in he ga ppl.

According o Thoma & Dae (2003), inalling a pipeline connecing o he ga ranpor ne-or and a iring connecing o he elecrici grid are almo eall epenive hen he di-ance i he ame. I i herefore imporan o conider he neare diance o he main elecric-


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ity network and the main gas network when determining whether Gas-to-Wire is more profit-able than regular gas production.

2.8. E D 2.8.1.

G When natural gas consumption in the Netherlands started, the price of natural gas was indexedto the oil price or that of oil-based products. The reason for this is that gas could be priced justbelow the price of oil equivalents, thereby stimulating the use of natural gas. This relationshipstill exists, however not as directly. The spot market for gas has become increasingly importantsince the liberalisation of the market Clingendael International nergy Programme, 2008;Lei, 2010), alo pricing formla referring o oil price allo for deviaion of he ga price.When acive on he po mare, an operaor can hal prodcion or ore ga hile aiing for afavorable momen o ell.

The Tile Tranfer Facili (TTF) i a viral rading hb for naral ga in he Neherland,hich a eablihed b he Ganie in 2002. The TTF i faciliaed b he Dch energ e-change APX-ENDEX, ih APX reponible for hor-erm conrac and ENDEX for long-ermconrac (Vlam & Cer, 2010). Price on he TTF are deermined b ppl and demand (GaTranpor Service, 2012). Several conrac lengh are available on he TTF, for eample a daahead, a monh ahead, a arer ahead and a ear ahead. The oal volme of ga preen in heGTS ranpor neor a a cerain momen can be raded a man ime a aned, hereforehe amon of ga raded a he TTF normall eceed he phical volme b a facor called hechrn facor. Wih a chrn facor of 3.66 in 2007, he TTF a conidered a moderael liidmare (Eropean Energ Reglaor, 2007).

A large rader in naral ga, GaTerra offer varing pe of conrac ne o he TTF. Theeconrac have price indeed o crde oil or oil prodc, copled o eigh hich deerminehe inflence of hee price on he ga price (von Bannieh, 2008b; GaTerra, 2012c). Theeformla are noaed in he form [nmber of monh over hich he average price i aen nmber of monh beeen averaging period and conrac period nmber of monh for hich

he average price i valid]. The rcre of a 6-2-6 conrac i illraed in Figre 3.

F 3: 626 .The pricing formla applied can be valid for one or mliple ear. GaTerra alo offer he po-ibili of fied price for he draion of a ear, ga conrac baed on he TTF price, and inome cae pecific price arrangemen. For large er, o oil-baed price formla can beed; he 3-0-3 and he 6-0-3 mehod. Thee conrac e a price for hree monh, baed on heaverage fel oil and dieel oil price for he pa hree and i monh repecivel. The eigh-ing variable for hee price can be adjed b GaTerra. The folloing formla i applied:

(2.1)

ih


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as the gas price as a fixed component as the average diesel oil price as the average fuel oil price as the weighting variable for diesel oil as the weighting variable for fuel oil

For suppliers to small users, the 6-2-6 method is applied. With this method the average fuel oilprice over six months is taken, and set as contract price for the next six months with a delay oftwo months. The regional charge includes a fee which comprises the majority of the price. Thefollowing formula is used (Kofman & Ophi, 2012):

22.00 (2.2)with

s the vege fue oi piceAccoding to Aten (2010), the constnt nd the weighting vibes in the fomus GsTeppies to detemine pices e modified in such w tht the coincide most pefect withthe TTF pices. This obsevtion is in ine with GsTes intention to bndon the diect piceink to oi (Ymoh, 2007; on nnisseht, 2008). The tione behind this intention is tht,since the ibeistion of the eneg mket, GsTe cnnot ow its pices to devite muchfom the gs pices estbished t the echnge (Lomme, 2008). This woud esut in bitgeoppotunities nd distub the mket.

2.8.2. E lectricity cannot be stored easily after it has been generated. When electricity is generated andcannot be used right away, it can be converted to another type of energy, allowing to regenerateelectricity when required. xamples of this are storing electricity in a battery, or pumping waterinto a reservoir. uch conversions come at a cost, while a significant amount of energy is lostduring the process as well vans & Guthrie, 2007. Therefore the industry continuously seeksto match supply to the energy demand as well as possible scribano et al., 2002. The demandpattern for electricity is highly variable. lectricity prices on the spot market are set by the hour,or even on shorter time intervals. lectricity is traded on the Dutch energy exchange APX-NDX, comprising both spot contracts and future contracts. The spot market includes day-ahead contracts, agreeing to physically deliver electricity the next day against the specifiedprice. pot prices are set via a bidding procedure that involves minimum and maximum pricesset by APX-NDX Verkuyl et al., 2005. A significant amount of electricity trading takes placeoutside the APX-NDX as well. The German energy exchange X, as the leading energy ex-change in Central urope, also plays a role of note in the Netherlands. The Netherlands are a netimporter of electricity, with the shortage of Dutch production often covered via X importArmstrong et al., 2004. Furthermore, there is a specific balancing market for correcting short-term misbalances in electricity supply.The variance of APX returns has shown a decreasing trend over the past years. Also large pricejumps have occurred less. A possible explanation for this trend is the liberalisation and interna-tional integration of the energy market, allowing to strike a better balance between supply anddemand A. Huygen, personal communication, 30 March 2012). Anoher recen developmen ihe abili o inflence demand o ome degree, enabling a beer balance a ell. A he ame


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time, there are also reasons to believe that variance will increase in the future. The increasingshare of renewable energy makes the electricity production less predictable, the resulting im-balances could have a strengthening effect on variance in returns.We can make a distinction between base-load and peak-load power plants Cordaro & New York

Affordable eliable lectricity Alliance, 2008. Base-load power plants generate electricity at aconstant rate. When chosen rationally, base-load power plants are able to generate electricity atthe lowest marginal costs. Physical constraints play a role as well; not all power plants are ableto dpt thei output in feibe nd/o efficient w. neg compnies e contctubound to meet custome demnd. hen bse-od poduction fs shot to meet demnd, pek-od pnts e theefoe ctivted to fi the gp between supp nd demnd (He, 2007). Themount of time pek-od pnt is unning cn v stong (depending on thei mgincosts nd feibiit), fom poducing on di bsis to on seve hous e. hen demndinceses pices ise, which cn ende powe pnts with highe mgin costs economicfesibe. Howeve, meeting demnd hs pioit ove mimising mgin pofit fom pek-pnt poduction. hen poduction is unpofitbe, on the demnded output is povided.hen poduction is pofitbe, the pnt coud poduce t mimum cpcit nd se the ecesspoduction on the spot mket.


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Chpte 3

3.

The previous chapter provided a background regarding the state of the Dutch

energy market. It is now time to introduce the concepts of real option theory.

Chapter 3 starts with an overview of traditional valuation methods. We point out

which shortcomings these methods have, and how real options address them.

The rationale behind real option analysis is illustrated by explaining how the

famous Black-choles option pricing model can be applied to real world projects.

We relax the strict assumptions of this model later on.

As option pricing theory relies on risk-neutral valuation, we provide an explana-

tion of this concept. Option pricing theory only applies to liquidly traded finan-

cial instruments. Therefore it should be possible to construct a replicating port-

folio of financial instruments equivalent to the value and risk profile of the real

project. We describe several approaches on this subject. Due to the inability to

hedge against non-traded risks, real option theory distinguishes between private

and market risk. We dedicate a section on how different types of uncertainty can

be dealt with.

Finally we treat various issues in real option pricing, especially where it differs

from standard financial options. Volatility is noted to be particularly hard to es-

timate in real options when having multiple sources of uncertainty. Co-

dependencies may exist in the investment problem, notably between the size of

the project and the investments required. Other issues considered are the effect

of competition, the absence of a fixed maturity date, and suboptimal decisionmaking.


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3.1. A large number of valuation methods exists to quantify the attractiveness of investments. In thissection we will discuss the main valuation methods and their properties briefly, to allow forcomparison with the real option methodology explained later in this chapter. Drury 2008identifies four valuation methods widely applied in practice, namely the Accounting ate of e-turn, the payback method, the discounted cash flow analysis, and the Internal ate of eturn.The Accounting ate of eturn A divides the estimated annual profit by the average in-vestment the initial investment minus final salvage value, thereby obtaining the expected re-turn of the project. The payback method calculates the time required to earn back an initial in-vestment. The most commonly used valuation methods are based on discounted cash flow DCFtechniques. Using such methods, the cash flows during the lifetime of the project are identifiedand consequently discounted at a rate reflecting both the time value of money and the riskinessof the project. Net present value NPV or traditional DCF analysis assumes that future cashflows are deterministic, as soon as the production decision is made. To reflect both the time

value and the riskiness of the project, a constant discount rate is applied to future cash flows.Usually the Weighted Average Cost of Capital WACC of the firm is used as discount rate. Thediscounted sum of cash flows is the present value of the project. If this value is bigger than 0,then it is a signal to accept the project. The Internal ate of eturn I applies discounting onthe cash flows, seeking the discount rate which sets the discounted benefits equal to the re-quired investment. tated otherwise, it provides the maximum discount rate which provides anonnegative project value. Often both NPV and I are applied to obtain both a realistic value ofthe project and an upper bound discount rate.The traditional valuation methods have so

Master Assignment Wouter Van Heeswijk (Final)

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