Rotterdam School of Management, Erasmus University, Mar 2014
This thesis consists of 10 chapters. Chapter 1 covers the purpose, background information, method... more This thesis consists of 10 chapters. Chapter 1 covers the purpose, background information, methodology, and structure of the thesis. The value chain will be discussed in Chapter 2. Chapter 3 to Chapter 7 introduced the tools needed to solve the case study. Case study and proposed solution presented in Chapter 8 and 9. Finally, Chapter 10 is concluded the study.
To understand investment decisions, one need to understand the industry’s value chain. Chapter 2 analyzed value chain in upstream petroleum industry, they are: (1) prospect, (2) exploration and appraisal, (3) development, (4) production, and (5) abandonment.
Chapter 3 starts by explaining how the stochastic process can be used to forecast the oil price. Simply put, a stochastic process is a mathematically defined equation that can create a series of outcomes overtime, outcomes that are not deterministic in nature. Two stochastic process approaches: (1) brownian motion and (2) mean-reverting will be discussed.
Chapter 4 revisited classical valuation approach of oil and gas properties. In an oil and gas environment, the assumption of perpetuity is not realistic, thus a cash flow forecast should run for the entire life of the asset being valued. To accurately forecast the ‘free-cash-flow-to-the-firm’, a proper financial model is needed. The financial models consist of (1) profit loss, (2) balance sheet, (3) cash flow, and (4) valuation.
Since the financial model produced only a single-point estimate result, Chapter 5 presented Monte Carlo simulation as a tool to get a set of probable outcome. Monte Carlo simulation is undertaken by modelling a project and its key factors affecting the profitability of the project. Using @RISK software, a simulation can be done as many times as possible to plot a frequency distribution of the outcomes.
Chapter 6 talked about decision tree analysis. In contrast to Monte Carlo simulation which evaluate pre-determined project scenarios, decision tree focus on managerial decisions. Decision tree also take account of uncertainty, but they do so in a more rudimentary way, typically, by specifying the probabilities of limited classes such as “large”, “small” or “zero”.
Still, either simulation or decision analysis could not capture value of flexibility like real options. However, the models described in the real options literature often greatly oversimplify the problems. The integrated approach presented in Chapter 7 tried to bridge this gap by noting that there are two types of risk associated with most corporate investments: public (non-diversifiable) and private (diversifiable). It presented an approach that covered traditional decision analysis at one extreme to option pricing at the other.
To illustrate where real options analysis can be used to add value in valuation and decision making process, a case study will be introduced in Chapter 8. This case is based on generalized experience, with a fictional story and characters, but the salient features resemble the development of Ivar Aasen field in the North Sea.
Chapter 9 offered practical solution that demonstrates the possibility of actually implementing real options valuations in a meaningful way by an analyst.
Chapter 10 presented an important conclusion: the integrated approach resulted with a probability distribution that combines real options, diversifiable risk, and non-diversifiable risk effects: The right-hand side of the distribution has fatter tails (upward potential), while losses on the downside are clearly cut off.
Insight and techniques derived from option pricing 1 enable the quantification of elements of man... more Insight and techniques derived from option pricing 1 enable the quantification of elements of managerial flexibility and strategic interactions, which have thus far been ignored or underestimated by standard net present value or discounted cash flow methods.
Rotterdam School of Management, Erasmus University, Mar 2014
This thesis consists of 10 chapters. Chapter 1 covers the purpose, background information, method... more This thesis consists of 10 chapters. Chapter 1 covers the purpose, background information, methodology, and structure of the thesis. The value chain will be discussed in Chapter 2. Chapter 3 to Chapter 7 introduced the tools needed to solve the case study. Case study and proposed solution presented in Chapter 8 and 9. Finally, Chapter 10 is concluded the study.
To understand investment decisions, one need to understand the industry’s value chain. Chapter 2 analyzed value chain in upstream petroleum industry, they are: (1) prospect, (2) exploration and appraisal, (3) development, (4) production, and (5) abandonment.
Chapter 3 starts by explaining how the stochastic process can be used to forecast the oil price. Simply put, a stochastic process is a mathematically defined equation that can create a series of outcomes overtime, outcomes that are not deterministic in nature. Two stochastic process approaches: (1) brownian motion and (2) mean-reverting will be discussed.
Chapter 4 revisited classical valuation approach of oil and gas properties. In an oil and gas environment, the assumption of perpetuity is not realistic, thus a cash flow forecast should run for the entire life of the asset being valued. To accurately forecast the ‘free-cash-flow-to-the-firm’, a proper financial model is needed. The financial models consist of (1) profit loss, (2) balance sheet, (3) cash flow, and (4) valuation.
Since the financial model produced only a single-point estimate result, Chapter 5 presented Monte Carlo simulation as a tool to get a set of probable outcome. Monte Carlo simulation is undertaken by modelling a project and its key factors affecting the profitability of the project. Using @RISK software, a simulation can be done as many times as possible to plot a frequency distribution of the outcomes.
Chapter 6 talked about decision tree analysis. In contrast to Monte Carlo simulation which evaluate pre-determined project scenarios, decision tree focus on managerial decisions. Decision tree also take account of uncertainty, but they do so in a more rudimentary way, typically, by specifying the probabilities of limited classes such as “large”, “small” or “zero”.
Still, either simulation or decision analysis could not capture value of flexibility like real options. However, the models described in the real options literature often greatly oversimplify the problems. The integrated approach presented in Chapter 7 tried to bridge this gap by noting that there are two types of risk associated with most corporate investments: public (non-diversifiable) and private (diversifiable). It presented an approach that covered traditional decision analysis at one extreme to option pricing at the other.
To illustrate where real options analysis can be used to add value in valuation and decision making process, a case study will be introduced in Chapter 8. This case is based on generalized experience, with a fictional story and characters, but the salient features resemble the development of Ivar Aasen field in the North Sea.
Chapter 9 offered practical solution that demonstrates the possibility of actually implementing real options valuations in a meaningful way by an analyst.
Chapter 10 presented an important conclusion: the integrated approach resulted with a probability distribution that combines real options, diversifiable risk, and non-diversifiable risk effects: The right-hand side of the distribution has fatter tails (upward potential), while losses on the downside are clearly cut off.
Insight and techniques derived from option pricing 1 enable the quantification of elements of man... more Insight and techniques derived from option pricing 1 enable the quantification of elements of managerial flexibility and strategic interactions, which have thus far been ignored or underestimated by standard net present value or discounted cash flow methods.
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Papers by Andika Rivai
To understand investment decisions, one need to understand the industry’s value chain. Chapter 2 analyzed value chain in upstream petroleum industry, they are: (1) prospect, (2) exploration and appraisal, (3) development, (4) production, and (5) abandonment.
Chapter 3 starts by explaining how the stochastic process can be used to forecast the oil price. Simply put, a stochastic process is a mathematically defined equation that can create a series of outcomes overtime, outcomes that are not deterministic in nature. Two stochastic process approaches: (1) brownian motion and (2) mean-reverting will be discussed.
Chapter 4 revisited classical valuation approach of oil and gas properties. In an oil and gas environment, the assumption of perpetuity is not realistic, thus a cash flow forecast should run for the entire life of the asset being valued. To accurately forecast the ‘free-cash-flow-to-the-firm’, a proper financial model is needed. The financial models consist of (1) profit loss, (2) balance sheet, (3) cash flow, and (4) valuation.
Since the financial model produced only a single-point estimate result, Chapter 5 presented Monte Carlo simulation as a tool to get a set of probable outcome. Monte Carlo simulation is undertaken by modelling a project and its key factors affecting the profitability of the project. Using @RISK software, a simulation can be done as many times as possible to plot a frequency distribution of the outcomes.
Chapter 6 talked about decision tree analysis. In contrast to Monte Carlo simulation which evaluate pre-determined project scenarios, decision tree focus on managerial decisions. Decision tree also take account of uncertainty, but they do so in a more rudimentary way, typically, by specifying the probabilities of limited classes such as “large”, “small” or “zero”.
Still, either simulation or decision analysis could not capture value of flexibility like real options. However, the models described in the real options literature often greatly oversimplify the problems. The integrated approach presented in Chapter 7 tried to bridge this gap by noting that there are two types of risk associated with most corporate investments: public (non-diversifiable) and private (diversifiable). It presented an approach that covered traditional decision analysis at one extreme to option pricing at the other.
To illustrate where real options analysis can be used to add value in valuation and decision making process, a case study will be introduced in Chapter 8. This case is based on generalized experience, with a fictional story and characters, but the salient features resemble the development of Ivar Aasen field in the North Sea.
Chapter 9 offered practical solution that demonstrates the possibility of actually implementing real options valuations in a meaningful way by an analyst.
Chapter 10 presented an important conclusion: the integrated approach resulted with a probability distribution that combines real options, diversifiable risk, and non-diversifiable risk effects: The right-hand side of the distribution has fatter tails (upward potential), while losses on the downside are clearly cut off.
To understand investment decisions, one need to understand the industry’s value chain. Chapter 2 analyzed value chain in upstream petroleum industry, they are: (1) prospect, (2) exploration and appraisal, (3) development, (4) production, and (5) abandonment.
Chapter 3 starts by explaining how the stochastic process can be used to forecast the oil price. Simply put, a stochastic process is a mathematically defined equation that can create a series of outcomes overtime, outcomes that are not deterministic in nature. Two stochastic process approaches: (1) brownian motion and (2) mean-reverting will be discussed.
Chapter 4 revisited classical valuation approach of oil and gas properties. In an oil and gas environment, the assumption of perpetuity is not realistic, thus a cash flow forecast should run for the entire life of the asset being valued. To accurately forecast the ‘free-cash-flow-to-the-firm’, a proper financial model is needed. The financial models consist of (1) profit loss, (2) balance sheet, (3) cash flow, and (4) valuation.
Since the financial model produced only a single-point estimate result, Chapter 5 presented Monte Carlo simulation as a tool to get a set of probable outcome. Monte Carlo simulation is undertaken by modelling a project and its key factors affecting the profitability of the project. Using @RISK software, a simulation can be done as many times as possible to plot a frequency distribution of the outcomes.
Chapter 6 talked about decision tree analysis. In contrast to Monte Carlo simulation which evaluate pre-determined project scenarios, decision tree focus on managerial decisions. Decision tree also take account of uncertainty, but they do so in a more rudimentary way, typically, by specifying the probabilities of limited classes such as “large”, “small” or “zero”.
Still, either simulation or decision analysis could not capture value of flexibility like real options. However, the models described in the real options literature often greatly oversimplify the problems. The integrated approach presented in Chapter 7 tried to bridge this gap by noting that there are two types of risk associated with most corporate investments: public (non-diversifiable) and private (diversifiable). It presented an approach that covered traditional decision analysis at one extreme to option pricing at the other.
To illustrate where real options analysis can be used to add value in valuation and decision making process, a case study will be introduced in Chapter 8. This case is based on generalized experience, with a fictional story and characters, but the salient features resemble the development of Ivar Aasen field in the North Sea.
Chapter 9 offered practical solution that demonstrates the possibility of actually implementing real options valuations in a meaningful way by an analyst.
Chapter 10 presented an important conclusion: the integrated approach resulted with a probability distribution that combines real options, diversifiable risk, and non-diversifiable risk effects: The right-hand side of the distribution has fatter tails (upward potential), while losses on the downside are clearly cut off.