Optimal Decision-Making under Uncertainty - Application to Power Transmission Investments

Economists define investment as the act of incurring immediate costs with the expectation of future returns. An investment project, as every asset has a value. For successfully investing in and managing these assets is crucial not only recognizing what the value is but also the sources of this value. Most investment decisions share three characteristics in different degrees. First, investments are partially or totally irreversible. Roughly speaking, the initial investment cost is at least partially sunk; i.e. it is impossible to recover all the expenditures if the decision-maker changes his mind. Second, there is uncertainty in the revenues from the investment, and therefore, risk associated with this. Third, all decision-making has some leeway about the timing of the investment. It is possible to defer the decision making to get more information about the future. These three features interact to determine the optimal decisions of investors on a given investment project. Transmission utilities are faced with investment projects, which hold these three characteristics: irreversibility, uncertainty and the choice of timing. In this context, an efficient decision making process is, therefore, based on managing the uncertainties and understanding the relationships between risks and opportunities in order to achieve a well-timed investment execution. Therefore, strategic flexibility for seizing opportunities and cutting losses contingent upon the market evolution is of huge value. Strategic flexibility is a risk management method that is gaining ongoing research attention as it enables properly managing major uncertainties, which are unsolved at the time of making decisions. Hence, valuing added flexibility in transmission investment portfolios, for instance, by investing in power electronic-based controller meanwhile transmission line projects are deferred, is necessary to make optimal network upgrading. Nevertheless, expressing the value of flexibility in economic terms is not a trivial task and requires new, sophisticated valuing tools, since the traditional investment theory has not recognized the important implications of the interaction between the three aforementioned investment features. Any attempt to quantify investment flexibility almost naturally leads to the concept of Real Options (RO). The RO technique provides a well-founded framework –based on the theory of financial options, and consequently, stochastic dynamic programming- to assess strategic investments under uncertainty. In the first RO applications, valuation was normally confined to the investment options that can be easily assimilated to financial options, for which solutions are well-known and readily available. Nevertheless, an investor confront with a diverse set of opportunities. From this point of view, investment projects can be seen as a portfolio of options, where its value is driven by several stochastic variables. The introduction of multiple interacting options into real options models highly increases the problem complexity, making traditional numerical approaches impracticable. However in the recent years, simulation procedures for solving multiple American options have been successfully proposed. One of the most promising approaches is the Least Square Monte Carlo (LSM) method proposed by Longstaff and Schwartz in 2001. LSM method is based on stochastic chronological simulation and uses least squares linear regression to determine the optimal stopping time (optimal path) in the decision making process. This chapter lays out a general background about key concepts -uncertainty and risk- and the most usual risk management techniques in transmission investment are provided. Then, the concept of strategic flexibility is introduced in order to set its ability for dealing with the uncertainties involved in the investment problem. In addition, new criteria and advantages of ROV approach compared with classical probabilistic choice are presented, by exposing a LSM-based method for decomposing and evaluating the complex real option problem involved in flexible transmission investments under uncertainties. The proposed methodology is applied in a study case which evaluates an interconnection reinforcement on the European interconnected power system, by showing how the valuation of flexibility is a key task for making efficient and well-timed investments in the transmission network. The impact of two network upgrades on the system-wide welfare is analyzed. These upgrades are the development of a new interconnected line and the installation of a power electronic-based controller. Both upgrades represent measures to strengthen the German-Dutch interconnections due to the fact that these are among the most important corridors within the Central Western European (CWE) region. Hence, an interconnection project, which is currently under study, is compared to flexible investment in order to shed some light on the influence of the strategic flexibility on the optimal decision-making process. The research is focused on assessing the impact of different wind power in-feed scenarios in detail as well as the uncertainty of the demand growth, generation cost evolution and the installed wind capacity on the decision-making process. The presented approach might serve as a basis for a decision-making tool for regulatory agencies in order to quantify the necessity for network upgrades.Fil: Blanco, Gerardo. Universidad Nacional de Asunción; ParaguayFil: Olsina, Fernando Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentin

Real Option Valuation of a Portfolio of Oil Projects

Various methodologies exist for valuing companies and their projects. We address the problem of valuing a portfolio of projects within companies that have infrequent, large and volatile cash flows. Examples of this type of company exist in oil exploration and development and we will use this example to illustrate our analysis throughout the thesis. The theoretical interest in this problem lies in modeling the sources of risk in the projects and their different interactions within each project. Initially we look at the advantages of real options analysis and compare this approach with more traditional valuation methods, highlighting strengths and weaknesses ofeach approach in the light ofthe thesis problem. We give the background to the stages in an oil exploration and development project and identify the main common sources of risk, for example commodity prices. We discuss the appropriate representation for oil prices; in short, do oil prices behave more like equities or more like interest rates? The appropriate representation is used to model oil price as a source ofrisk. A real option valuation model based on market uncertainty (in the form of oil price risk) and geological uncertainty (reserve volume uncertainty) is presented and tested for two different oil projects. Finally, a methodology to measure the inter-relationship between oil price and other sources of risk such as interest rates is proposed using copula methods.Imperial Users onl

Investors and skewness preference in option portfolios / BEBR No. 816

Bibliography: p. 21-22

Investment Model Uncertainty and Fair Pricing

Modern investment theory takes it for granted that a Security Market Line (SML) is as certain as its "corresponding" Capital Market Line. (CML). However, it can be easily demonstrated that this is not the case. Knightian non-probabilistic, information gap uncertainty exists in the security markets, as the bivariate "Galton's Error" and its concomitant information gap proves (Journal of Banking & Finance, 23, 1999, 1793-1829). In fact, an SML graph needs (at least) two parallel horizontal beta axes, implying that a particular mean security return corresponds with a limited Knightian uncertainty range of betas, although it does correspond with only one market portfolio risk volatility. This implies that a security' risk premium is uncertain and that a Knightian uncertainty range of SMLs and of fair pricing exists. This paper both updates the empirical evidence and graphically traces the financial market consequences of this model uncertainty for modern investment theory. First, any investment knowledge about the securities risk remains uncertain. Investment valuations carry with them epistemological ("modeling") risk in addition to the Markowitz-Sharpe market risk. Second, since idiosyncratic, or firm-specific, risk is limited-uncertain, the real option value of a firm is also limited-uncertain This explains the simultaneous coexistence of different analyst valuations of investment projects, particular firms or industries, included a category "undecided." Third, we can now distinguish between "buy", "sell" and "hold" trading orders based on an empirically determined collection of SMLs, based this Knightian modeling risk. The coexistence of such simultaneous value signals for the same security is necessary for the existence of a market for that security! Without epistemological investment uncertainty, no ongoing markets for securities could exist. In the absence of transaction costs and other inefficiencies, Knightian uncertainty is the necessary energy for market trading, since it creates potential or perceived arbitrage (= trading) opportunities, but it is also necessary for investors to hold securities. Knightian uncertainty provides a possible reason why the SEC can't obtain consensus on what constitutes "fair pricing." The paper also shows that Malkiel's recommended CML-based investments are extremely conservative and non-robust.capital market line, security market line, beta, investments, decision-making, Knightian uncertainty, robustness, information-gap, Galton's Error, real option value

Risk preference discrepancy : a prospect relativity account of the discrepancy between risk preferences in laboratory gambles and real world investments

In this article, we presented evidence that people are more risk averse when investing in financial products in the real world than when they make risky choices between gambles in laboratory experiments. In order to provide an account for this discrepancy, we conducted experiments, which showed that the range of offered investment funds that vary in their riskreward characteristics had a significant effect on the distribution of hypothetical funds to those products. We also showed that people are able to use the context provided by the choice set in order the make relative riskiness judgments for investment products. This context dependent relativistic nature of risk preferences is proposed as a plausible explanation of the risk preference discrepancy between laboratory experiments and real-world investments. We also discuss other possible theoretical interpretations of the discrepancy

An analysis of spending behaviour under liquidity constraints with an application to financial hedging

Imperial Users onl

Investing in Energy Conversion Technologies - An Optimum Vintage Portfolio Selection Approach

The methods by which fuels can be converted into electricity all belong to different “technology families”: the “gas-fired-turbine-family”, the “coal-fired-turbine-family”, etc. Each family consists of different generations of similar technologies, as in a vintage model. Within a family, the latest generation embodies the most recent level and type of knowledge, becoming more and more outdated as new generations arrive. Producers face the problem how to compose their portfolio of families to minimize risk-adjusted costs of investment and production under a given demand constraint. Risk emanates from a number of uncertainties, such as volatile fuel prices and uncertain (prospects of) technological change. The paper presents a model capturing these features by integrating elements from financial Optimum Portfolio Theory (OPT) in a vintage capital investment framework. We find that the cumulative nature of embodied technical change gives rise to investment responses to (changes in) uncertainty that are in between the ‘standard’ results of OPT and Real Option Theory.economics of technology ;

Applying the real options theory for identifying flexibility in project delivery of health organisations

Healthcare is influenced by many uncertainties. Uncertainties affecting health organisations also influence real estate since this facilitates the primary process. Within real estate management, decisions have to be made today while there is little knowledge about the future. Therefore, flexibility is needed in the process of designing, constructing and operating real estate. A case study has been done to gain insight about how health organisations deal with flexibility. The real options approach is used to show what types of flexibility have been used, and that uncertainty can also generate opportunities. Of the five types of flexibility, only in two types real options were identified in the case study. These were stage, abandon, defer and scale within process flexibility and the options growth and switch within product flexibility. This is partly a result of the fact that the project in the case study is not further advanced than the preliminary design phase. Nevertheless it can be concluded that project managers already act as using real options. Consciously using this concept might create even more real options to be used in project management

A Real Options Perspective On R&D Portfolio Diversification

This paper shows that the conditionality of investment decisions in R&D has a critical impact on portfolio risk, and implies that traditional diversification strategies should be reevaluated when a portfolio is constructed. Real option theory argues that research projects have conditional or option-like risk and return properties, and are different from unconditional projects. Although the risk of a portfolio always depends on the correlation between projects, a portfolio of conditional R&D projects with real option characteristics has a fundamentally different risk than a portfolio of unconditional projects. When conditional R&D projects are negatively correlated, diversification only slightly reduces portfolio risk. When projects are positively correlated, however, diversification proves more effective than conventional tools predict.real options;portfolio analysis;research & development