REAL OPTIONS
in THEORY and PRACTICE
in THEORY and PRACTICE
All rights reserved.
Table of Contents
Chapter 1: Introduction
1.1 The aim of real options analysis
1.2 Placing real options analysis in context
1.3 Outline of the book
1.4 Spreadsheets
Part I: Foundations
Chapter 2: The modeling framework
2.1 Choosing an objective function
2.2 Modeling risk and time
2.3 Static versus dynamic decision making
2.4 Problems
Chapter 3: Valuing single-period cash flows
3.1 Arbitrage-free asset prices
3.2 Valuation when the state variable is the price of a traded asset
3.3 Valuation using forward and futures contracts
3.4 Valuation when the state variable is not necessarily the price of a
traded asset
3.5 Summarizing the valuation approach
3.6 Problems
3.A Appendix: Proofs
Chapter 4: Valuing multi-period cash flows
4.1 Valuing distant cash flows using backward induction
4.2 Two situations where valuation can be streamlined
4.3 Valuing multi-period cash flows as portfolios
4.4 Valuing multi-period cash flows using backward induction
4.5 Summarizing the valuation approach
4.6 Problems
4.A Appendix: The RADR-form of the CAPM
4.B Appendix: Valuation with personal taxes
Chapter 5: Combining valuation and decision making
5.1 A simple example of a real option
5.2 Decision trees
5.3 Fundamental valuation equation
5.4 Filling in the trees using dynamic programming
5.5 Summarizing our approach to analyzing real options
5.6 Problems
Part II: Component Real Options
Chapter 6: Options that do not affect the state of a project
6.1 Examples of options that do not affect the state of a project
6.2 Solving the production-suspension problem
6.3 Solving problems involving options that do not affect the state of a
project
6.4 Problems
Chapter 7: Simple timing options
7.1 Examples of simple timing options
7.2 Solving the investment timing problem
7.3 Solving the abandonment timing problem
7.4 Solving the R&D timing problem
7.5 Solving problems involving simple timing options
7.6 Problems
Chapter 8: Compound timing options
8.1 Examples of compound timing options
8.2 Solving the sequential investment problem
8.3 Solving the resource extraction problem
8.4 Solving the multi-stage R&D timing problem
8.5 Solving problems involving compound timing options
8.6 Problems
Chapter 9: Uber-compound timing options
9.1 Examples of uber-compound timing options
9.2 Solving the land-development problem
9.3 Solving the time-to-build problem
9.4 Solving problems involving uber-compound timing options
9.5 Problems
Chapter 10: Switching options
10.1 Examples of switching options
10.2 Solving the production-suspension problem
10.3 Solving the machinery-replacement problem
10.4 Solving problems involving switching options
10.5 Problems
Chapter 11: Learning options
11.1 Examples of learning options
11.2 Modeling information gathering
11.3 Staging the roll-out of a new venture
11.4 Solving the oil exploration problem
11.5 Solving problems involving learning options
11.6 Problems
Part III: Calibrating the Model
Chapter 12: Calibration using spot and futures price data
12.1 Calibrating a tree of prices using historical data
12.2 Calibrating risk-neutral probabilities
12.3 Deciding which approach to use
12.4 Problems
12.A Appendix
Chapter 13: Calibration using option price data
13.1 Implied volatility
13.2 Implied binomial trees: European options
13.3 Implied binomial trees: American options
13.4 From a futures-price tree to a state-variable tree
13.5 Problems
13.A Appendix
Chapter 14: Calibrating trees of alternative state variables
14.1 Non-price state variables
14.2 Market values as state variables
14.3 The choice of state variable
14.4 Problems
14.A Appendix
Part IV: Putting the Pieces Together
Chapter 15: Forestry management and valuation
15.1 Setting up the model
15.2 The solution procedure
15.3 Data and calibration
15.4 Results
15.5 Problems
Chapter 16: Developing a gas field
16.1 Setting up the model
16.2 The solution procedure
16.3 Data and calibration
16.4 Results
16.5 Problems
16.A Appendix
Chapter 17: Mothballing an ethanol plant
17.1 Setting up the model
17.2 The solution procedure
17.3 Data and calibration
17.4 Results
17.5 Problems
Chapter 18: Where to from here?
18.1 Improved numerical algorithms
18.2 Greater econometric sophistication
18.3 Multiple state variables
Bibliography
1.1 The aim of real options analysis
1.2 Placing real options analysis in context
1.3 Outline of the book
1.4 Spreadsheets
Part I: Foundations
Chapter 2: The modeling framework
2.1 Choosing an objective function
2.2 Modeling risk and time
2.3 Static versus dynamic decision making
2.4 Problems
Chapter 3: Valuing single-period cash flows
3.1 Arbitrage-free asset prices
3.2 Valuation when the state variable is the price of a traded asset
3.3 Valuation using forward and futures contracts
3.4 Valuation when the state variable is not necessarily the price of a
traded asset
3.5 Summarizing the valuation approach
3.6 Problems
3.A Appendix: Proofs
Chapter 4: Valuing multi-period cash flows
4.1 Valuing distant cash flows using backward induction
4.2 Two situations where valuation can be streamlined
4.3 Valuing multi-period cash flows as portfolios
4.4 Valuing multi-period cash flows using backward induction
4.5 Summarizing the valuation approach
4.6 Problems
4.A Appendix: The RADR-form of the CAPM
4.B Appendix: Valuation with personal taxes
Chapter 5: Combining valuation and decision making
5.1 A simple example of a real option
5.2 Decision trees
5.3 Fundamental valuation equation
5.4 Filling in the trees using dynamic programming
5.5 Summarizing our approach to analyzing real options
5.6 Problems
Part II: Component Real Options
Chapter 6: Options that do not affect the state of a project
6.1 Examples of options that do not affect the state of a project
6.2 Solving the production-suspension problem
6.3 Solving problems involving options that do not affect the state of a
project
6.4 Problems
Chapter 7: Simple timing options
7.1 Examples of simple timing options
7.2 Solving the investment timing problem
7.3 Solving the abandonment timing problem
7.4 Solving the R&D timing problem
7.5 Solving problems involving simple timing options
7.6 Problems
Chapter 8: Compound timing options
8.1 Examples of compound timing options
8.2 Solving the sequential investment problem
8.3 Solving the resource extraction problem
8.4 Solving the multi-stage R&D timing problem
8.5 Solving problems involving compound timing options
8.6 Problems
Chapter 9: Uber-compound timing options
9.1 Examples of uber-compound timing options
9.2 Solving the land-development problem
9.3 Solving the time-to-build problem
9.4 Solving problems involving uber-compound timing options
9.5 Problems
Chapter 10: Switching options
10.1 Examples of switching options
10.2 Solving the production-suspension problem
10.3 Solving the machinery-replacement problem
10.4 Solving problems involving switching options
10.5 Problems
Chapter 11: Learning options
11.1 Examples of learning options
11.2 Modeling information gathering
11.3 Staging the roll-out of a new venture
11.4 Solving the oil exploration problem
11.5 Solving problems involving learning options
11.6 Problems
Part III: Calibrating the Model
Chapter 12: Calibration using spot and futures price data
12.1 Calibrating a tree of prices using historical data
12.2 Calibrating risk-neutral probabilities
12.3 Deciding which approach to use
12.4 Problems
12.A Appendix
Chapter 13: Calibration using option price data
13.1 Implied volatility
13.2 Implied binomial trees: European options
13.3 Implied binomial trees: American options
13.4 From a futures-price tree to a state-variable tree
13.5 Problems
13.A Appendix
Chapter 14: Calibrating trees of alternative state variables
14.1 Non-price state variables
14.2 Market values as state variables
14.3 The choice of state variable
14.4 Problems
14.A Appendix
Part IV: Putting the Pieces Together
Chapter 15: Forestry management and valuation
15.1 Setting up the model
15.2 The solution procedure
15.3 Data and calibration
15.4 Results
15.5 Problems
Chapter 16: Developing a gas field
16.1 Setting up the model
16.2 The solution procedure
16.3 Data and calibration
16.4 Results
16.5 Problems
16.A Appendix
Chapter 17: Mothballing an ethanol plant
17.1 Setting up the model
17.2 The solution procedure
17.3 Data and calibration
17.4 Results
17.5 Problems
Chapter 18: Where to from here?
18.1 Improved numerical algorithms
18.2 Greater econometric sophistication
18.3 Multiple state variables
Bibliography
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