Browsing by Subject "Real options"
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Item A Tool for the Analysis of Real Options in Sustainability Improvement Projects(2012-10-19) Boonchanta, NaponThe major challenges in sustainable implementation are the financial issue and uncertainties. The traditional financial budgeting approach that is commonly used to evaluate sustainable projects normally neglects future decisions that might need to be made over the course of a project. The real options approach has been suggested as a tool for strategic decision making because it can provide flexibility which can increase the project value. Researchers have been trying to identify the potential of the real options approach, and provide the frameworks for a real options evaluation and flexible strategy in sustainability improvement. However, some important variables and financial impacts explanation of real options are missing. Models can be improved to show the variation of possible project values along with its behavior. This work aims to improve the real options model in sustainable projects to provide understanding about the financial impacts of flexible strategy to sustainable improvement projects and to be used as a tool to assist decision making. The results showed that real options can have a positive financial impact to the project. The extension of this model can assist the analysis and development of decision policies.Item Application of real options to valuation and decision making in the petroleum E&P industry(2010-12) Xu, Liying, 1962-; Van Rensburg, W. C. J.; Sepehrnoori, Kamy, 1951-This study is to establish a binomial lattice method to apply real options theory to valuation and decision making in the petroleum exploration and production industry with a specific focus on the switching time from primary to water flooding oil recovery. First, West Texas Intermediate (WTI) historical oil price evolution in the past 25 years is studied and modeled with the geometric Brownian motion (GBM) and one-factor mean reversion price models to capture the oil price uncertainty. Second, to conduct real options evaluation, specific reservoir simulations are designed and oil production profile for primary and water flooding oil recovery for a synthetic onshore oil reservoir is generated using UTCHEM reservoir simulator. Third, a cash flow model from producing the oil reservoir is created with a concessionary fiscal system. Finally, the binomial lattice real options evaluation method is established to value the project with flexibility in the switching time from primary to water flooding oil recovery under uncertain oil prices. The research reaches seven conclusions: 1) for the GBM price model, the assumptions of constant drift rate and constant volatility do not hold for WTI historical oil price; 2) one-factor mean reversion price model is a better model to fit the historical WTI oil prices than the GBM model; 3) the evolution of historical WTI oil prices from January 2, 1986 to May 28, 2010 was according to three price regimes with different long run prices; 4) the established real options evaluation method can be used to identify the best time to switch from primary to water flooding oil recovery using stochastic oil prices; 5) with the mean reversion oil price model and the most updated cost data, the real options evaluation method finds that the water flooding switching time is earlier than the traditional net present value (NPV) optimizing method; 6) the real options evaluation results reveals that most of time water flooding should start when oil price is high, and should not start when oil price is low; and 7) water flooding switching time is sensitive to oil price model to be used and to the investment and operating costs.Item Assessing reservoir performance and modeling risk using real options(2012-05) Singh, Harpreet; Srinivasan, Sanjay; Lake, Larry W.Reservoir economic performance is based upon future cash flows which can be generated from a reservoir. Future cash flows are a function of hydrocarbon volumetric flow rates which a reservoir can produce, and the market conditions. Both of these functions of future cash flows are associated with uncertainties. There is uncertainty associated in estimates of future hydrocarbon flow rates due to uncertainty in geological model, limited availability and type of data, and the complexities involved in the reservoir modeling process. The second source of uncertainty associated with future cash flows come from changing oil prices, rate of return etc., which are all functions of market dynamics. Robust integration of these two sources of uncertainty, i.e. future hydrocarbon flow rates and market dynamics, in a model to predict cash flows from a reservoir is an essential part of risk assessment, but a difficult task. Current practices to assess a reservoir’s economic performance by using Deterministic Cash Flow (DCF) methods have been unsuccessful in their predictions because of lack in parametric capability to robustly and completely incorporate these both types of uncertainties. This thesis presents a procedure which accounts for uncertainty in hydrocarbon production forecasts due to incomplete geologic information, and a novel real options methodology to assess the project economics for upstream petroleum industry. The modeling approach entails determining future hydrocarbon production rates due to incomplete geologic information with and without secondary information. The price of hydrocarbons is modeled separately, and the costs to produce them are determined based on market dynamics. A real options methodology is used to assess the effective cash flows from the reservoir, and hence, to determine the project economics. This methodology associates realistic probabilities, which are quantified using the method’s parameters, with benefits and costs. The results from this methodology are compared against the results from DCF methodology to examine if the real options methodology can identify some hidden potential of a reservoir’s performance which DCF might not be able to uncover. This methodology is then applied to various case studies and strategies for planning and decision making.Item Decision impact of stochastic price models in the petroleum industry(2011-08) Hammond, Robert Kincaid; Bickel, J. Eric; Dyer, James S.; Smith, James E.Stochastic price models have proven material to decision making in the oil industry when accurate valuations are important, but little consideration is given to their impact on decisions based on relative project rankings. Traditional industry economic analysis methods do not usually consider uncertainty in oil price, although the real options literature has shown that this practice underestimates the value of projects that have flexibility. Monetary budget constraints are not always the limiting constraints in decision making; there may be other constraints that limit the number of projects a company can undertake. We consider building a portfolio of upstream petroleum development projects to determine the relevance of stochastic price models to a decision for which accurate valuations may not be important. The results provide guidelines to determine when a stochastic price model should be used in economic analysis of petroleum projects.Item Essays on real options and strategic interactions(2012-08) Dehghani Firouzabadi, Mohammad Hossein; Boyarchenko, Svetlana I.; Almazan, Andres; Stinchcombe, Maxwell B.; Tompaidis, Stathis; Wiseman, ThomasChapter 2 considers technology adoption under both technological and subsidy uncertainties. Uncertainty in subsidies for green technologies is considered as an example. Technological progress is exogenous and modeled as a jump process with a drift. The analytical solution is presented for cases when there is no subsidy uncertainty and when the subsidy changes once. The case when the subsidy follows a time invariant Markov process is analyzed numerically. The results show that improving the innovation process raises the investment thresholds. When technological jumps are small or rare, this improvement reduces the expected time before technology adoption. However, when technological jumps are large or abundant, this improvement may raise this expected time. Chapter 3 studies technology adoption in a duopoly where the unbiased technological change improves production efficiency. Technological progress is exogenous and modeled as a jump process with a drift. There is always a Markov perfect equilibrium in which the firm with more efficient technology never preempts its rival. Also, a class of equilibria may exist that lead to a smaller industry surplus. In these equilibria either of the firms may preempt its rival in a set of technology efficiency values. The first investment does not necessarily happen at the boundary of this set due to the discrete nature of the technology progress. The set shrinks and eventually disappears when the difference between firms’ efficiencies increases. Chapter 4 studies the behavior of two firms after a new investment opportunity arises. Firms either invest immediately or wait until market uncertainty is resolved. Two types of separating equilibrium are possible when sunk costs are private information. In the first type the firm with lower cost invests first. In the second type the firm with higher cost invests first leading to a smaller industry surplus. The results indicate that the second type is possible only for strictly negatively correlated sunk costs. Numerical analysis illustrates that when first mover advantage is large, the firm that delays the investment should be almost certain about its rival’s sunk cost. When market risk increases, the equilibria can exist when the firm is less certain.Item Facility planning and value of information : a case study of deepwater reservoir compartmentalization(2010-08) Ramachandran, Hariharan, 1986-; Jablonowski, Christopher J.; Lasdon, Leon S.This thesis investigates how estimates of reservoir compartmentalization impact facility sizing decisions and the degree to which inaccurate estimates destroy project value. An uncertainty analysis workflow is proposed and an asset development optimization model is specified to simulate the decision making process during concept selection. The model endogenizes drilling decisions and includes a real option to expand facility capacity after the uncertain variables are realized. The value of information analysis suggests that cost of erroneous estimates of reservoir compartmentalization is significant and can reduce asset value by more than 30%. We also find that the negative impacts are larger when the degree of compartmentalization is underestimated (too optimistic) than when it is overestimated (too pessimistic).Item Individuals' decisions and group behavior in financial economics(2012-05) Wilson, Michael Scott; Boyarchenko, Svetlana I.; Abrevaya, Jason; Starks, Laura; Stinchcombe, Maxwell; Wiseman, ThomasThis dissertation contains three chapters in financial economics that theoretically and empirically examine how individuals' investment decisions explain aggregate behavior. The first chapter examines how reputational herding between fund managers depends on the fee structure, fund manager evaluation metric, market efficiency, and density of talented fund managers. Results show there are more equilibria involving herding between fund managers when net fund balance growth depends on reputation of talent rather than fund return. These inefficient equilibria are removed when the ratio of the performance fee rate to management fee rate is larger than calculated thresholds that depend on market efficiency and the density of talented fund managers. In the absence of performance fees, lower predictability of investment returns and a higher density of talented fund managers increase the desire for fund managers to deviate from efficient equilibria. The model also shows having fund managers compete against each other induces herding when net fund balance growth depends on fund returns, but removes herding equilibria when net fund balance growth depends on reputation of talent. The second chapter determines what herding networks exist between institutional investors and how herding depends on stock market volatility, degree of portfolio changes, and stock size. Using quarterly holding data from 2000-2010, I find stronger herding networks between similar types of institutions compared to institutions in the same metropolitan area. Furthermore, the herding network between similar types of institutions exists across metropolitan areas. Results show institutions herd more when making major portfolio changes than when making minor portfolio changes. The difference in herding between the two types of portfolio changes is greatest for small cap stocks which exhibit the highest levels of herding under both types of portfolio changes. The relationship between market volatility and herding by institutions is also examined and found not to have a strong correlation using quarterly holdings data. The third chapter answers the question, "Can reasonable wind energy plant cost reductions or efficiency improvements precipitate immediate investment in wind energy in the absence of renewable energy Production Tax Credits?" I analyze a single entity considering an irreversible investment under uncertainty in wind power energy. The investor's decision to invest is dependent on investment cost, energy production efficiency, government policy, current price of electricity, and beliefs on future electricity prices. The results show that even with substantial cost reductions and efficiency improvements, Production Tax Credits are still needed to encourage immediate investment.Item Information structures and their effects on consumption decisions and prices(2013-05) Moreno González, Othón M.; Wiseman, Thomas E., 1974-This work analyzes the effects that different information structures on the demand side of the market have on consumption decisions and the way prices are determined. We develop three theoretical models to address this issue in a systematic way. First, we focus our attention on the consumers' awareness, or lack thereof, of substitute products in the market and the strategic interaction between firms competing in prices and costly advertising in such an environment. We find that prior information held by consumers can drastically change the advertising equilibrium predictions. In particular, we provide sufficient conditions for the existence of three types of equilibria, in addition to one previously found in the literature, and provide a necessary condition for a fourth type of equilibrium. Additionally, we show that the effect of the resulting advertising strategies on the expected transaction price is qualitatively significant, although ambiguous when compared to the case of a newly formed market. We can establish, however, that the transaction price is increasing in the size of the smaller firm's captive market. In the second chapter, we study the optimal timing to buy a durable good with an embedded option to resell it at some point in the future, as well as its reservation price, where the agent faces Knightian uncertainty about the process generating the market prices. The problem is modeled as a stopping problem with multiple priors in continuous time with infinite horizon. We find that the direction of the change in the buyer's reservation price depends on the particular parametrization of the model. Furthermore, the change in the buying threshold due to an increase in ambiguity is greater as the fraction of the market at which the agent can resell the good decreases, and the value of the embedded option is decreasing in the perceived level of ambiguity. Finally, we introduce Knightian uncertainty to a model of price search by letting the consumers be ambiguous regarding the industry's cost of production. We characterize the equilibria of this game for high and low levels of the search cost and show that firms extract abnormal profits for low realizations of the marginal cost. Furthermore, we show that, as the search cost goes to zero, the equilibrium of the game under the low cost regime does not converge to the Bertrand marginal-cost pricing. Instead firms follow a mixed-strategy that includes all prices between the high and low production costs.Item Multistage stochastic programming models for the portfolio optimization of oil projects(2011-08) Chen, Wei, 1974-; Dyer, James S.; Lasdon, Leon S., 1939-; Balakrishnan, Anantaram; Lake, Larry W.; Jablonowski, Christopher J.Exploration and production (E&P) involves the upstream activities from looking for promising reservoirs to extracting oil and selling it to downstream companies. E&P is the most profitable business in the oil industry. However, it is also the most capital-intensive and risky. Hence, the proper assessment of E&P projects with effective management of uncertainties is crucial to the success of any upstream business. This dissertation is concentrated on developing portfolio optimization models to manage E&P projects. The idea is not new, but it has been mostly restricted to the conceptual level due to the inherent complications to capture interactions among projects. We disentangle the complications by modeling the project portfolio optimization problem as multistage stochastic programs with mixed integer programming (MIP) techniques. Due to the disparate nature of uncertainties, we separately consider explored and unexplored oil fields. We model portfolios of real options and portfolios of decision trees for the two cases, respectively. The resulting project portfolio models provide rigorous and consistent treatments to optimally balance the total rewards and the overall risk. For explored oil fields, oil price fluctuations dominate the geologic risk. The field development process hence can be modeled and assessed as sequentially compounded options with our optimization based option pricing models. We can further model the portfolio of real options to solve the dynamic capital budgeting problem for oil projects. For unexplored oil fields, the geologic risk plays the dominating role to determine how a field is optimally explored and developed. We can model the E&P process as a decision tree in the form of an optimization model with MIP techniques. By applying the inventory-style budget constraints, we can pool multiple project-specific decision trees to get the multistage E&P project portfolio optimization (MEPPO) model. The resulting large scale MILP is efficiently solved by a decomposition-based primal heuristic algorithm. The MEPPO model requires a scenario tree to approximate the stochastic process of the geologic parameters. We apply statistical learning, Monte Carlo simulation, and scenario reduction methods to generate the scenario tree, in which prior beliefs can be progressively refined with new information.Item Multivariate real options valuation(2011-05) Wang, Tianyang; Dyer, James S.; Tompaidis, Efstathios; Muthuraman, Kumar; Bickel, J. E.; Butler, John C.; Garlappi, LorenzoThis dissertation research focuses on modeling and evaluating multivariate uncertainties and the dependency between the uncertainties. Managing risk and making strategic decisions under uncertainty is critically important for both individual and corporate success. In this dissertation research, we present two new methodologies, the implied binomial tree approach and the dependent decision tree approach, to modeling multivariate decision making problems with practical applications in real options valuation. First, we present the implied binomial tree approach to consolidate the representation of multiple sources of uncertainty into univariate uncertainty, while capturing the impact of these uncertainties on the project’s cash flows. This approach provides a nonparametric extension of the approaches in the literature by allowing the project value to follow a generalized diffusion process in which the volatility may vary with time and with the asset prices, therefore offering more modeling flexibility. This approach was motivated by the Implied Binomial Tree (IBT) approach that is widely used to value complex financial options. By constructing the implied recombining binomial tree in a way so as to be consistent with the simulated market information, we extended the finance-based IBT method for real options valuation — when the options are contingent on the value of one or more market related uncertainties that are not traded assets. Further, we present a general framework based on copulas for modeling dependent multivariate uncertainties through the use of a decision tree. The proposed dependent decision tree model allows multiple dependent uncertainties with arbitrary marginal distributions to be represented in a decision tree with a sequence of conditional probability distributions. This general framework could be naturally applied in decision analysis and real options valuations, as well as in more general applications of dependent probability trees. While this approach to modeling dependencies can be based on several popular copula families as we illustrate, we focus on the use of the normal copula and present an efficient computational method for multivariate decision and risk analysis that can be standardized for convenient application.Item Quantifying the impacts of regulatory delay on housing affordability and quality in Austin, Texas(2015-05) Shannon, Megan Elizabeth; Wegmann, Jake; Mitchell, TerryRegulatory delay during site plan review of multifamily projects in Austin has three primary impacts: 1) it generates unexpected development costs which increases housing prices over-time; 2) it stifles innovation and decreases quality of development; and 3) it promotes exurban growth. These impacts reduce affordability and quality of life for all Austinites and thwart the goals of the Imagine Austin comprehensive plan. As regulatory delays have increased remarkably since 2009, strong rent growth has compensated for this growing uncertainty throughout the Austin market. If regulatory delays are eliminated and developers receive approvals for multifamily projects within the 120 day mandate instead of the 223 day average, renters could see relief of 4-5% on their rent, or an average of $60 per month or $720 annually in Central Austin. Interviews with 14 Austin-area residential developers confirm these delays, costs, and impacts on their projects. On average it takes 3.5 additional months to receive site plan approvals in Austin in addition to the code mandated four month cycle. Austin's peer cities fare differently. The average delay in Denver, Colorado is three weeks, and is just several days in Raleigh, North Carolina. Whereas land use regulations theoretically generate positive externalities, delays in administering those regulations generate no benefits to the community. During this unforeseen 3.5 months, developers accrue unexpected costs such as legal fees, and developer overhead which includes the opportunity costs of not pursuing other deals. Construction costs increase during delays, and developers must continue to pay for land options and carry costs. In the short-term, developers pay for these unexpected costs out-of-pocket, and by reducing construction costs, which can result in lower quality materials or amenities. Unexpected costs roll into the project's overall budget, resulting in more expensive development projects. More expensive projects require higher rents in order to maintain the development team's expected yield on cost. Further, interviews with urban designers and civil engineers reveal that regulatory delay stifles private sector innovation in the built environment. Developer interviews and case studies suggest that regulatory delay promotes exurban growth instead of urban infill in the Austin metropolitan area.Item A review of the methods of economic analysis of nuclear power plants(2011-05) Cavender, Brittainy Anne; Popova, Elmira; Hess, StephenNuclear power plants across the United States are reaching the end of their current operating licenses, forcing decision makers to think about the way forward. As they consider the best alternatives for dealing with aging nuclear plants, it is becoming increasingly important to have an accurate method for calculating the long-term costs of nuclear power plants. This report begins by investigating the methodologies currently used in these calculations. They focus on the uncertainty associated with deregulated electricity markets and can be broken down into two main categories: discounted cash flow and real options analysis. Next the report discusses the limitations of the current methodologies, focusing specifically on those aspects of evaluation that are currently eclipsed by electricity market uncertainty. Finally the report offers recommendations for addressing these limitations and creating a stronger analytical framework for calculating the lifetime cost of nuclear power plants.