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Due to climate change concerns, high oil prices and nuclear dangers there is increasing support for renewable energy. At the forefront of the debate for government support of renewable energy are wind energy and biofuels. Used primarily for power generation and transportation, respectively, there have been many debates surrounding the reliability and efficiency of these resources. These debates often address the uncertainty in the economic value of the resource through time, however it is often difficult to quantify this uncertainty, which stems from the random behavior of prices and the unpredictable nature of the resource itself. In this thesis we use well developed theory taken from quantitative finance, more specifically real options theory, as well as various mathematical and statistical techniques and models used in option pricing to determine the economic value of these resources. Market design and policy are key considerations throughout the analysis. A simplified model of a corn ethanol plant is analyzed using a simple Margrabe exchange option as well as numerical techniques such as bootstrapping and finite difference methods for solving partial differential equations. It is determined that, as correlation between corn price and gasoline price increases, the value of the ethanol plant decreases. The level of decrease is substantial, and the economic and political consequences are discussed. A simplified wind-storage model is also developed and analyzed as a dynamic program, allowing for analytic solutions. This allows for the determination of an optimal bidding strategy when the penalty for failing to meet a committed generation level is defined. The market consequences of these results are discussed and compared with numerical results obtained from a more complex wind-storage model where analytical solutions are not available. These results are obtained using a modified block bootstrapping method, and the optimal storage size is determined for a specific penalty structure.
Real R&D options are among the earliest modelled real options, with now ten primary practical uses: general R&D planning, planning R&D in stages, evaluating test information, new product development timing, operations, abandonment, risk sharing, market funding, industry strategy and regulation. This book was partly motivated by requests to identify and develop real option models for R&D in telecommunications, petroleum technology and biotechnology. Nine new models cover information and implementation costs, analytical solutions for mean reverting, or fat tailed revenues, endogenous learning and exogenous and experiential shocks, American sequential options, and innovator advantages. Four new applications include forward start development options, exploration options, innovation with information costs, and innovator's real values with changing market share. R&D directors and researchers will find several uses for these models: general R&D planning evaluating test information new product development timing risk sharing industry strategy and regulation A practical guide to how organizations can use Real Option techniques to effectively value research and development by companies Provides a rigorous theoretical underpinning of the use of Real Option techniques Real Options applications are orientated around the economies of North America, Europe and Asia, for an international perspective
Examines the ways in which real options theory can contribute to strategic management. This volume offers conceptual pieces that trace out pathways for the theory to move forward and presents research on the implications of real options for strategic investment, organization, and firm performance.
The aim of this paper is to provide a suitable method to estimate the volatility parameter for the purpose of real option project valuation, especially that of renewable and traditional energy projects. The method is based on the concept of implied volatility of financial options. Then, we obtain implied volatilities for renewable and traditional energy firms by using their debt-to-equity ratios instead of "moneyness" or the strike price used in the case of financial options. For a given debt-to-equity relation of the project, the implied volatility is obtained by employing the stochastic alpha-beta-rho (SABR) model. Our methodology may be extended to find the volatility of any real option project, subject to the availability of market data. Our empirical results show that the annual volatility for renewable energy projects ranged between 16.44% and 38.15% in the period from April 2014 to June 2016.
This book presents the application of real options approach (ROA) to analyze investment decisions for switching energy sources from fossil fuels to alternative energy. Using the Philippines as a case, the ROA models presented here explore how uncertainties including fossil fuel prices, electricity prices, discount rates, externality, renewable energy (RE) costs, and RE investment growth affect investment decisions that focus on developing countries, particularly to fossil-importing countries. The book is a collection of academic papers published in peer-reviewed journals. The first paper analyzes investments in various RE sources including wind, solar, hydropower, and geothermal over using coal. The second paper compares investments between RE and nuclear energy considering the risk of nuclear accident. The third paper applies the proposed ROA model with the case of Palawan island and analyzes investment in RE over diesel fuel for electricity generation. The fourth paper focuses on investment drivers that make RE sources as a better option than using fossil fuels.
We propose a new real options analysis method for evaluating R&D investments using a novel Poisson process to simulate the discrete progress typical of R&D breakthroughs. We take explicit account of the technical risk of an R&D project, while the market risk and the effect of learning-by-doing in operational technologies are also explicitly modelled. We present a compound real option structure, where a European real option is used to model the fixed length term typical of early phase research, which is exercisable into an American real option to model later phase R&D. In this later phase, a successful outcome will be acted on immediately to operationalise the technology. We propose a Monte Carlo simulation approach, which models R&D progress in a stylised logistic function or 'S-bend' form, capturing the typically slow rate of R&D progress at the start of the early phase, through to more rapid improvement as the R&D advances, which then slows again as the limitations of the R&D are approached. We demonstrate that this method is applicable for evaluating the R&D investment potential in CO2 recycling technology, where an energy commodity (such as methane) is produced, using appropriate modelling for the price of the energy commodity. The method may be applied widely to R&D technology projects.
Benefits analysis of US Federal government funded research, development, demonstration, and deployment (RD3) programs for renewable energy (RE) technology improvement typically employs a deterministic forecast of the cost and performance of renewable and nonrenewable fuels. The benefits estimate for a program derives from the difference between two forecasts, with and without the RD3 in place. The deficiencies of the current approach are threefold: (1) it does not consider uncertainty in the cost of non-renewable energy (NRE), and the option or insurance value of deploying RE if and when NRE costs rise; (2) it does not consider the ability of the RD3 manager to adjust the RD3 effort to suit the evolving state of the world, and the option value of this flexibility; and (3) it does not consider the underlying technical risk associated with RD3, and the impact of that risk on the programs optimal level of RD3 effort. In this paper, a rudimentary approach to determining the option value of publicly funded RE RD3 is developed. The approach seeks to tackle the first deficiency noted above by providing an estimate of the options benefit of an RE RD3 program in a future with uncertain NRE costs. While limited by severe assumptions, a computable lattice of options values reveals the economic intuition underlying the decision-making process. An illustrative example indicates how options expose both the insurance and timing values inherent in a simplified RE RD3 program that coarsely approximates the aggregation of current Federal RE RD3. This paper also discusses the severe limitations of this initial approach, and identifies needed model improvements before the approach can adequately respond to the RE RD3 analysis challenge.
How should firms decide whether and when to invest in new capital equipment, additions to their workforce, or the development of new products? Why have traditional economic models of investment failed to explain the behavior of investment spending in the United States and other countries? In this book, Avinash Dixit and Robert Pindyck provide the first detailed exposition of a new theoretical approach to the capital investment decisions of firms, stressing the irreversibility of most investment decisions, and the ongoing uncertainty of the economic environment in which these decisions are made. In so doing, they answer important questions about investment decisions and the behavior of investment spending. This new approach to investment recognizes the option value of waiting for better (but never complete) information. It exploits an analogy with the theory of options in financial markets, which permits a much richer dynamic framework than was possible with the traditional theory of investment. The authors present the new theory in a clear and systematic way, and consolidate, synthesize, and extend the various strands of research that have come out of the theory. Their book shows the importance of the theory for understanding investment behavior of firms; develops the implications of this theory for industry dynamics and for government policy concerning investment; and shows how the theory can be applied to specific industries and to a wide variety of business problems.
"Mun demystifies real options analysis and delivers a powerful, pragmatic guide for decision-makers and practitioners alike. Finally, there is a book that equips professionals to easily recognize, value, and seize real options in the world around them." --Jim Schreckengast, Senior VP, R&D Strategy, Gemplus International SA, France Completely revised and updated to meet the challenges of today's dynamic business environment, Real Options Analysis, Second Edition offers you a fresh look at evaluating capital investment strategies by taking the strategic decision-making process into consideration. This comprehensive guide provides both a qualitative and quantitative description of real options; the methods used in solving real options; why and when they are used; and the applicability of these methods in decision making.
The study of investment under uncertainty was stagnant for several decades until developments in real options revitalized the field. The topics covered in this book include the reasons behind the under-investment programme.