Real Options "in" Projects and Systems Design ... - Title Page - MIT

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3203 389 154;Scalars es /0.7/;Scalars Kt /15.552/;Scalars ht /4320/;Scalars Fst /0.35/;Scalars BetaP /0.21/;ParametersFC(s) /1 11.192 03 8.41/;ParametersVC(s) /1 0.0004492 03 0.000668/;ParametersHBar(s) /1 36002 17003 1732/;ParametersVBar(s) /1 96002 03 9593/;Table ABar(s,t)1 21 262 2622 280 2803 240 253;Table YBar(s,t)1 21 0 02 0 0
3213 -63.6 63.6;Scalars f /0.226/;ParametersCheta(s)/1 0.0007652 0.001853 0.00088/;Scalars r /0.086/;ParametersPV(i) /1 6.5322 2.8633 1.254/;ParametersPVO(i) /1 0.8962 0.9433 0.963/;ParametersPVC(i) /1 12 0.4383 0.192/;Scalars crf /0.087/;*The following defintion of variables are based on List of Variable.*The order <strong>and</strong> the unit are strictly folloiwng the List of Variables.VariablesXsti(s,t,i)Psti(s,t,i)Rsi(s,i)BCNIB;
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Real Options "in" Projects and Syst
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4Committee Member: Dr. David H. Mar
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6Table of Contents:Acknowledgements
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84.4.3. Binomial Tree______________
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108.7. Contributions and Conclusion
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12Figure 5-3: Scenario tree _______
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14Table 6-12 Sources of options val
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16fFC s∆F sthiH sh tThe ratio of
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18X styyY jYYjAverage flow from sit
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201.1. The problemWith the recognit
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22The first thread - engineering sy
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24The second thread - options theor
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26The third thread - mathematical p
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28especially the difference between
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30every area in a more and more det
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32made between forecasts of the lev
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34memory (whereas modern computers
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36model and presented a multiple-yi
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38such designs exist) by simply rec
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40mathematical properties of maximi
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43that incorporates important perfo
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45Dixit and Pindyck (1994) stressed
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47The real options “in projects
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491980s, Merck began to use simulat
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51that the integrated approach (the
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53blurs the exercise condition, bec
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55Goldberg and Read (2000) found th
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57In general, real options “in”
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59- And, mathematical programming,
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61Example of a stock call option:Jo
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63or $300. Arbitrage opportunities
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659 × 0.25 =2.25Regardless of whet
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67∆Z = ε ∆twhere ε denotes a
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69standard deviation of 1.0. It fol
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71price rather than as a function o
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73The stock price process is the on
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75Equation 3-9 is the Black-Scholes
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7710,000 Trials Frequency Chart90 O
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79The two are equal whenorS0ux− f
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81S uuS uf uuS f u S udfS df udf dS
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83Figure 3-10 Decision Tree for Opt
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85the Black-Scholes formula is $1.9
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87The expected growth of the stock
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89option, the holder has the right
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91Boston expects to receive a cash
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93the value of a European put on a
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95Chapter 4 Real Options“The clas
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97In addition to understanding the
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99gas. The development was unsucces
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101But if we employ Real Options th
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103Option to switch (e.g.,outputs o
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105The fifth step, by comparing the
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107Real Options "in" ProjectsReal O
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109- 5 and 3.5 billion dollars resp
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111Real options “on” projectsVa
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1134.3.4. A Case Example on analysi
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115Step 1: Table 4-6 illustrates th
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11710EXPECTED NPV ($, MILLIONS)50-5
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119PROBABILITY1.00.90.80.70.60.50.4
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1214.4.1. Black-Scholes FormulaAs d
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123and buy the real options to earn
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125computationally prohibitive to c
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1274.5. Some Implications of Real O
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129Value of flexibility Appropriate
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1314.6.2. What is the definition of
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133- It aims at an expected value o
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135Chapter 5 Valuation of Real Opti
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137uncertain environment. Specifica
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139assuming steady state, i.e. all
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141modify this standard simulation
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143Table 5-1: Decision on each node
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145The objective function is to get
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1475.3.2. Stochastic mixed-integer
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149q⎛ R ⎞1⎜ ⎟qR = ⎜ ...
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151Table 5-4: Stochastic programmin
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153The real options timing model fo
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1555.4. A case example on satellite
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157Table 5-6 Evolution of demand fo
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1595.4.3. ParametersWe consider thr
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161Year 0 Year 2.5 Year 5 Year 7.5
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163scenario trees based on better s
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165The total length of the river is
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167Project 1 or 3. A shallow gradie
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169Table 6-5: Stream flow for Proje
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171The reservoir cost coefficient a
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173Q in,tQ in,tProject CSite 3Site
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175k t= (60Sec/ Min)× (60Min/ Hour
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177Reservoir cost: α ( Vss) = FCs+
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179[Power plant cost curve]δ 1 is
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181Complete formulation of the scre
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183Table 6-9 List of Parameters for
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185Step 1. The important uncertain
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187From the tornado diagram, we und
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189design feature where we consider
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191Table 6-13 List of design variab
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193Table 6-15 List of Parameters fo
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195A Realization Path of Electricit
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197Case II: there is enough water t
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199where the average head*Astis cal
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201Ball©. All designs from the scr
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203options sees Table 6-17. This is
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205XX= Q + Y ∑ Ri31 in,131 j331 i
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207Objective functionThe objective
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209According to the screening model
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211The hydropower benefit coefficie
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213Table 6-19 List of parameters fo
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215time to build and gradually incr
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217example, at year 20, if the pric
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219from 1 to 8. The hydropower bene
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221Our planning horizon is three st
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2235 6 7 82s= R2s= R2sR2sR =1 23sR3
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225yrsIndicating whether or not the
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227The overall expected net benefit
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229contingency plan is to build Pro
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231Table 6-26 Different water flow
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233Chapter 7 Policy Implications -O
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235excess water when water is more
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237The options methodology develops
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2397.2.2. Organizational Wisdom to
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241success. In the bank, the author
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243method. Sensitivity analysis wit
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245Chapter 8 Summary and Conclusion
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2478.2. Real options “in” proje
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249Note the difference between real
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251Carlo simulation model to value
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2538.3.1. Options identificationFor
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255can catch upside of the uncertai
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257where r is the drift rate (in th
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259A joint realization of the probl
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261Note there is an exercise in sce
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263The real options decision variab
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265development case example, we sim
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267The standard deviation of the co
Page 269 and 270: 269reservoir fixed cost is less und
Page 271 and 272: 271time series of the water flow co
Page 273 and 274: 273Table 8-8 Configurations set Y f
Page 275 and 276: 275Table 8-9: Results for real opti
Page 277 and 278: 277inconvenient features and high c
Page 279 and 280: 279Year 0 Year 2.5 Year 5 Year 7.5
Page 281 and 282: 281Following is a report on the com
Page 283 and 284: 283can be found in Chapter 6, whose
Page 285 and 286: 285This dissertation has successful
Page 287 and 288: 2878.7. Contributions and Conclusio
Page 289 and 290: 289ReferencesAberdein, D. A. (1994)
Page 291 and 292: 291Copeland, T.E. and Antikarov, V.
Page 293 and 294: 293Jacoby, H.D. and Loucks, D. (197
Page 295 and 296: 295Mason, S.P. and Merton, R.C. (19
Page 297 and 298: 297Schoenberger, C. R. (2000) “Si
Page 299 and 300: 299AppendicesAppendix 3A: Ito’s L
Page 301 and 302: 301p;Binary variablesX(i,j);X.l('4'
Page 303 and 304: 303Appendix 5B: GAMS Code for Ameri
Page 305 and 306: 305n5 non-antipativity constraint 5
Page 307 and 308: 3072 0.50 1.51 4.57 7.5 4.573 0.50
Page 309 and 310: 309R(q,i,s)RTD(q,i,s)P1(q,i)P2(q,i)
Page 311 and 312: 311na28na29na30na31na32na33na34na35
Page 313 and 314: 313na26(s)..na27(s)..R('14','3',s)
Page 315 and 316: 315Table DeltaF(s,t)1 21 0 02 0 03
Page 317 and 318: 317EquationsObjBeneCostCont1Cont2Co
Page 319: 319Appendix 6B: GAMS code for the t
Page 323 and 324: 323Hydro1Hydro2Budget;*costs and be
Page 325 and 326: 325Scalars es /0.7/;Scalars Kt /15.
Page 327 and 328: 327VariablesXstiq(s,t,i,q)Pstiq(s,t
Page 329 and 330: 329f)*Rsiq(s,i,'1') )))))*0.001 + P
Page 331 and 332: 331Appendix 6D: GAMS code for real
Page 333 and 334: 3331 2 31 9600 9600 96002 0 0 03 95
Page 335 and 336: 335Rsiq.l('1','3','4') = 0;Rsiq.l('
Page 337: 337Cont1(i,q).. Xstiq('3','1',i,q)
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