Bachelorarbeit, 2015
50 Seiten, Note: 1.0
Introduction
1 What are Real Options?
1.1 Definition
1.2 Comparison to traditional Net Present Value method
1.3 Types of real options and analogy to financial options
2 Real Options Theory
2.1 Literature Review
2.2 Stochastic Processes
2.2.1 The Basic and the Generalized Wiener Process
2.2.2 Itô Process, Geometric Brownian Motion and Itô’s Lemma
2.2.3 Jump-Diffusion Process
2.2.4 Mean-Reverting Process
3 Approaches to Real Option Valuation
3.1 Dynamic Programming
3.1.1 Discrete time optimization
3.1.2 Optimal Stopping
3.1.3 Continuous Time Optimization
3.1.4 Value Matching and Smooth Pasting Condition
3.2 Contingent Claim Analysis
3.2.1 Replicating Portfolio
3.2.2 Spanning Assets
3.2.3 Smooth Pasting
3.3 Simulation Approach
3.4 Comparison of the Approaches
4 Valuing undeveloped petroleum reserves
4.1 Valuation of a developed reserve
4.2 Valuation of an undeveloped reserve
4.3 Numerical examples
4.4 Final Remarks
Conclusion
This thesis aims to provide a comprehensive overview of real option valuation methodologies, exploring the underlying mathematical concepts and stochastic processes while investigating why their practical application in industry remains limited compared to traditional methods.
3.2 Contingent Claim Analysis
The contingent claim approach was originally proposed by Black, Scholes (1973)[5] and Merton (1971, 1973)[29][30]. Their work became by far the most groundbreaking contributions in both academic and practical finance. It was later adapted to the valuation of investment under uncertainty as an alternative method to the dynamic programming approach because one issue with dynamic programming is that the discount rate ρ is arbitrary and constant. However, it must equal to the return on other investments with similar risk characteristics.34 The contingent claim analysis targets this problem by making the assumption that there is a sufficient set of risky assets in the market such that the stochastic process inherent in an investment can be exactly replicated. Thus, we can create a riskless portfolio and apply risk neutral valuation. For this purpose, the important assumption that there are no riskless arbitrage opportunities is made. This means that the replicated portfolio must earn the risk free rate at market equilibrium.35
1 What are Real Options?: This chapter defines real options, contrasts them with the traditional NPV rule, and highlights the value of management flexibility.
2 Real Options Theory: This section provides a literature review and develops the mathematical building blocks for valuation, focusing on various stochastic processes like the Wiener process and Geometric Brownian Motion.
3 Approaches to Real Option Valuation: This chapter details the three primary quantitative frameworks used in the field: Dynamic Programming, Contingent Claim Analysis, and Simulation.
4 Valuing undeveloped petroleum reserves: The final chapter applies the theoretical models to a specific real-world investment case, using numerical examples to demonstrate the valuation of undeveloped oil reserves.
Real Options, Net Present Value, Stochastic Processes, Geometric Brownian Motion, Dynamic Programming, Contingent Claim Analysis, Monte Carlo Simulation, Option Valuation, Petroleum Reserves, Financial Mathematics, Itô's Lemma, Uncertainty, Investment Decision, Risk-Neutral Valuation, Mean-Reversion
The thesis focuses on the valuation of real options as a superior alternative to traditional Net Present Value (NPV) methods when making investment decisions under uncertainty.
The paper covers real option theory, stochastic calculus, specific valuation models (dynamic programming, contingent claims, simulation), and a practical application regarding the valuation of undeveloped petroleum reserves.
The goal is to provide a comprehensive overview of existing real option valuation methodologies and to offer arguments explaining the gap between academic theory and limited practical implementation by industry practitioners.
The paper utilizes analytical and numerical methods, specifically applying stochastic processes (Wiener process, Itô calculus) and backward induction, including dynamic programming and the least-squared simulation approach.
The main body examines the mathematical foundations of uncertainty, compares various stochastic processes, and develops the models for dynamic programming and contingent claim valuation, followed by a detailed case study.
Key terms include Real Options, Stochastic Processes, NPV, Dynamic Programming, Contingent Claim Analysis, Geometric Brownian Motion, and Petroleum Reserve Valuation.
The author notes that while they share similarities—such as granting the right but not the obligation to take action—they differ fundamentally because real options are not directly traded in financial markets and involve physical assets influenced by managerial decisions.
The author explains that while dynamic programming often relies on an exogenous, constant discount rate, Contingent Claim Analysis assumes the existence of a replicating portfolio in the market, allowing for risk-neutral valuation.
The author concludes that real options are often underutilized due to high mathematical complexity, costly implementation, and the frequent lack of sufficient market data needed to accurately model the underlying stochastic processes.
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