july 12, 2006 identification of real options in projects prof. richard de neufville massachusetts...
DESCRIPTION
July 12, Real Options A real option is a right, but not an obligation, to do something for a certain cost within or at a specific period of time Real options can be categorized as: –“ON” projects, these are financial options, but on technical things, they treat technology as ‘black box’” –“IN” projects, these are options created by designing technical system. They require understanding of technology.TRANSCRIPT
July 12, 2006
Identification of Real Options “In” Projects
Prof. Richard de NeufvilleMassachusetts Institute of TechnologyRoom E40-245, Cambridge, MA 02139
Dr. Tao WangMorgan Stanley
3 Exchange Square, Central, Hong [email protected]
July 12, 2006
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The Question
• Future is inherently unknown and flexibility has value
• Options theory offers an convenient tool to define and model flexibility
• However, real options “in” projects are
– A priori undefined, complex, and interdependent
– Have many more options than designers could consider
• How to identify the real options most likely to offer good flexibility and the most value?
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Real Options
• A real option is a right, but not an obligation, to do something for a certain cost within or at a specific period of time
• Real options can be categorized as:
– “ON” projects, these are financial options, but on technical things, they treat technology as ‘black box’”
– “IN” projects, these are options created by designing technical system. They require understanding of technology.
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Development of Options Theory
Real Options "in" Projects
Real Options "on" Projects
Financial Options
Real Options "on" Projects
Financial Options
Financial Options
Options Theory
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Comparing Real Options “In” and “On” Projects
Real Options “On” Projects Real Options “In” Projects
Value Opportunities Design flexibility
Valuation important Decision important (go or no go)
Relatively easy to define Difficult to define
Interdependency/path-dependency less an issue
Interdependency/path-dependency an important Issue
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Difficulties Facing Identification of Real Options “In” Projects
• The problem is not that we cannot discern options
• But too many variables and possible real options
• Less than a small fraction of possible options can be considered
• The designers need to identify options most likely to offer good flexibility
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Proposed Options Identification Process
Consists of two parts:
Part 1: A screening model
• Simplified, conceptual, low-fidelity model
• Can be run many times to explore possible flexibilities
• Can be simplified in a number of ways
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Proposed Options Identification Process (continued)
Part 2: A simulation model
• A high fidelity model
• Main purpose is to examine, under technical and economic uncertainties, the designs preliminarily identified by the screening model for their:
– Technical robustness and reliability
– Expected benefits under uncertainties
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General Procedure for Establishing A Screening Model
1. Reduce complexity of system by eliminating some secondary dimensions of problem
2. Develop a screening model based on the reduced dimensions
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General Procedure for Using the Screening Model to Identify Options
1. List uncertain variables
2. Find out the standard deviations or volatilities for the uncertain variables
3. Perform sensitivity analysis on the uncertain variables to pick out the several most important uncertain variables for further analysis
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General Procedure for Using the Screening Model to Identify Options
(continued)4. List different levels of the important uncertain
variable(s) as inputs for the established screening model to identify where the most interesting real options are:
– The design elements that vary across the sets are those that may be good real options; and
– Conversely, the design elements that are included for all sets, that are insensitive to uncertainty, or design elements where settings are always constant do not present interesting real options
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Case Studies to Illustrate the Identification Process in Details
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Case I:Water Resources Development
• Decisions on building dams and hydropower stations regarding:
– Choice of sites
– Capacity of power plants (H) and capacity of reservoirs (V)
– Sequence of projects construction
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Developing Screening Model for the Water Resources Development Case
• Reduce dimensions of problem
– Drop stochasticity of water flow and electricity price
– Neglect the transitional phase and assume steady state
• Develop the optimization screening model
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Screening Model
Max mYm cmYm i
s.t. TY t (technical constraints)
EY e (economic constraints)
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Formulation of the Screening Model
Max PPst crf FCs VCsVs sHs s
t
s (1)
s.t. (Technical constraints Part I Continuity constraints)Ss,t1 Sst E st X st kt s,t (2)E st X s 1,t Fst s,t (3)
(Technical constraints Part II Reservoir storage /capacity constraints)Sst Vs 0 s,t (4)Vs yrsCAPDs 0 s (5)Sst s Ast 0 s,t (6)
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Formulation of the Screening Model (continued)
(Technical constraints Part III Hydropower constraints)Pst 2.73es kt X st Ast 0 s, t (7)Pst YsthtHs 0 s, t (8)AMINs Ast 0 s, t (9)Ast AMAXs 0 s, t (10)AMAXs 2AMINs 0 s, t (11)Hs CAPPs 0 (12)
(Budget constraints)
FCs VCs Vs s Hs s B s (13)
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List of Variables for the Screening Model
Variable Definition Units
yrsInteger variable indicating whether or not the reservoir is constructed at site s
Sst Storage at site s for season t 106m3
Xst Average flow from site s for season t m3/s
Est Average flow entering site s for season t m3/s
Pst Hydroelectric power produced at site s for season t MWh
Ast Head at site s for season t m
Hs Capacity of power plant at site s MW
Vs Capacity of reservoir at site s 106m3
AMAXs Maximum head at site s m
AMINs Minimum head at site s m
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List of Parameters for the Screening Model
Parameter Definition Units ValueQin,t Upstream inflow for season t m3/s (374,283)
CAPDs Maximum feasible storage capacity at site s 106m3 (9600, 25,
12500)
CAPPs Upper bound for power plant capacity at site s MW (3600, 1700,
3200)
FstIncrement to flow between sites s and the next site for season t m3/s
(212, 105) for site 3, others
are 0es Power plant efficiency at site s 0.7
kt Number of seconds in a season 106 sec 15.552
ht Number of hours in a season Hours 4320
Yst Power factor at site s for season t 0.35
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List of Parameters for the Screening Model (continued)
Parameter Definition Units Value
P Hydropower benefit coefficient 103 RMB/MWh 0.25
FCs Fixed cost for reservoir at site s B RMB (11.19, 0, 8.41)
VCs Variable cost for reservoir at site s B RMB/106m3
(4.49 x 10-4, 0, 6.68 x 10-4)
s Variable cost for power plant at site s B RMB/MW
(7.65 x 10-4, 1.85 x 10-3, 8.80 x 10-4)
r Discount rate 0.086crf Capital recovery factor for 60 years 0.087B Total budget available 103 RMB 80,000,000
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Using the Screening Model to Identify Real Option
• Step 1. List uncertain variables. For the purpose of illustration, we pick out three important uncertain variables for further scrutiny:
– Electricity price,
– Fixed cost of reservoir, and
– Variable water flow
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Using the Screening Model to Identify Real Option (continued)
• Step 2. Find out the standard deviations or volatilities for the uncertain variables:
– Volatility of future electricity price derived by experts’ expectation;
– Standard deviation of the construction cost was estimated from the standard deviation for cost of mega projects (Flyvbjerg, et al., 2003, pp. 16);
– Variable water flow calculated from historical record
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Using the Screening Model to Identify Real Option (continued)
• Step 3. Perform sensitivity analysis on the uncertain variables to pick out the most important uncertain variable(s) for further analysis:
– A tornado diagram is a useful tool for such sensitivity
analysis
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Tornado Diagram
• This kind of diagram expresses the change of net benefit due to 1 standard deviation/volatility change of each important uncertain variables, with other uncertain variables kept at the expected value
Change of Net Benefit (in M RMB) Due to 1 S.D. Change of Important Uncertain Variables
-80
-325
-418
18
332
596
(Million RMB)
Fixed cost for reservoir
Electricity price
Waterflow
2196
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Tornado Diagram (continued)
• The most important uncertainties are fixed cost and electricity price:
– But the fixed cost of reservoir is a one time cost, offering less opportunity for ongoing flexibility
– The electricity price is a stochastic process that offers interesting flexibility over the life of the project
• As a first step to illustrate the approach, we focus on one uncertainty of electricity price in this paper
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Back to Using the Screening Model to Identify Real Option
• Step 4. List different levels of the important uncertain variable (electricity price) as inputs for the established screening model to identify where the most interesting real options are.
– Although the current price of electricity was 0.25 RMB/KWh, we studied the conditions when it might be 0.10, 0.13, 0.16, 0.19, 0.22, 0.28, 0.31 RMB/KWh
– The levels cover most of the range of the experts’ pessimistic (0.18 RMB/KWh) to optimistic estimate (0.315 RMB/KMB)
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Results from Step 4
H 1 V 1 H 2 V 2 H 3 V 3(MW) (106m3) (MW) (106m3) (MW) (106m3)
1 0 0 0 0 0 02 3600 9600 1700 25 0 03 3600 9600 1700 25 0 04 3600 9600 1700 25 1564 65935 3600 9600 1700 25 1723 95936 3600 9600 1700 25 1946 122427 3600 9600 1700 25 1966 125008 3600 9600 1700 25 1966 12500
Candidate Design ID
Robust design
Potential flexibility
Screen at different
electricity prices
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Simulation Model to Pick Up the Final Real Options
• Testing the preliminary candidate designs from the screening model
• Studying the effects of stochastic variation of hydrologic and economic uncertain parameters
• Key considerations:
– Robustness and reliablity of designs
– Expected benefits under uncertainties
• Choose the best candidate(s) as the real options identified
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Real Options Identified
Design Specifications Exercise Time
Option at Site 1 H1 = 3600 MW, V1 = 9.6 x 109 m3 Any time
Option at Site 2 H2 = 1700 MW, V2 = 2.5 x 107 m3 Any time
Option at Site 3
H3 = 1564 MW, V3 = 6.93 x 109 m3
One of H3 = 1723 MW, V3 = 9.593 x 109 m3
H3 = 1946 MW, V3 = 12.242 x 109 m3
Any time
Sources of Option ValueValue of timing Value of flexible design
Design at site 1 Yes NoDesign at site 2 Yes NoDesign at site 3 Yes Yes
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Case II: Staged Development of Satellite Communications Systems
• Iridium and Globalstar
• How about deploying satellites progressively, starting with small capacity that can be increased as needed?
Forecast in 1991 Actually…
U.S. terrestrial cellular phone
market
40 million subscribers by the year 2000
The standardization of terrestrial cellular networks resulted in over 110 million
subscribers in 2000, 275% of the projection
U.S. satellite cellular phone
market
3 million subscribers by
2000
Iridium only aroused the interest of 50k initial subscribers and filed for bankruptcy in August 1999. Globalstar went bankrupt
on February 2002
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Screening Model for Satellite Systems
• de Weck, de Neufville, Chaize, (2004) developed a design space by optimization and numerical experiment
• It is a screening model in effect
• Based on the demand growth scenario, possible staged development architecture path can be recognized
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Further Real Options Analysis After Identifying the Real Options
Screening Model
Simulation Model
Options Identification Options Analysis
Timing Model
Execute and redesign when new information arrives
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Results - Water Resources Planning
• Optimal value = 7,129 Million RMB (optimal value of the traditional design without real options = 5,195 Million RMB)
• Options value is difference, 1,934 Million RMB, or 37.2% of the traditional design without real options
Price = 0.466 RMB/KwHProject 3 build
(H3 = 1564 MW, V3 = 6.93 Χ 109 m3)
Price = 0.374 RMB/KwHProject 1 build Price = 0.300 RMB/KwH
Price = 0.300 RMB/KwH Project 3 no build Project 3 no buildProject 1 no buildProject 2 build Price = 0.300 RMB/KwH
Project 3 no build Price = 0.241 RMB/KwH Project 1 no buildProject 1 no build Project 3 no buildProject 3 no build
Price = 0.193 RMB/KwHProject 1 no buildProject 3 no build
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Results - Satellite Communication Systems
• Results
– We would first invest $0.5 Billion to test the market
– If market is not good, only lose $0.5 Billion, rather than $2.0 Billion in the best traditional architecture
– Serve up to 7.8 million subscribers readily, while the best traditional design can only serve up to 2.8 million
• Downside significantly cut, upside possible
• If Iridium and Globalstar had used this design…
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Decision TreeYear 0 Year 2.5 Year 5 Year 7.5 Year 10
Demand = 41.85
Demand = 13.84Demand = 4.57
Demand = 4.57Deploy A5 Demand = 4.57
Demand = 1.51Demand = 0.5
Demand = 1.51Deploy A4 Demand = 4.57
Deploy A5Demand = 1.51
Demand = 0.5
Demand = 0.50Demand = 0.5
Demand = 0.17Demand = 0.05
Demand = 0.5Deploy A2 Demand = 4.57
Deploy A5Demand = 1.51
Deploy A4 Demand = 0.5
Demand = 0.50Demand = 0.5
Demand = 0.17Demand = 0.05
Demand = 0.17Demand = 0.5
Demand = 0.17Demand = 0.05
Demand = 0.05Demand = 0.05
Demand = 0.02Demand = 0.01
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Additional References
• Wang, T. (2005) “Real Options ‘in’ Projects and Systems - Identification of Options and Solution for Path Dependency”, PhD Thesis, MIT, Cambridge, MA
• Wang, T. and de Neufville, R. (2004) “Building Real Options into Physical Systems with Stochastic Mixed-Integer Programming,” 8th Annual Real Options International Conference, Montreal, Canada
Email ([email protected]) if you need copies of above
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Summary
• The options identification discovers the design elements most likely to provide worthwhile flexibility
• The proposed identification process includes a
screening and a simulation model:
– Screening model is a simplified, conceptual, low-fidelity model of the system. It that represents most important issues. It can be easily run many times to identify sets of options;
– Once the screening model has identified plausible options, a detailed simulation model can validate and refine the options
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Thank You!
Questions?