Decision support for firm performance by real options analytics

This paper develops a real options decision support tool for raising the performance of the firm. It shows how entrepreneurs can use our intuitive tool quickly to assess the nature and type of action required for improved performance. This exploits our estimated econometric relationship between precipitators of entrepreneurial opportunities, time until exercise, and firm performance. Our 3D chromaticity plots show how staging investments, investment time, and firm performance support entrepreneurial decisions to embed, or to expedite, investments. Speedy entrepreneurial action is securely supported with this tool, without expertise in econometric estimation or in formulae for real options valuation

Using Surveys to Compare the Public’s and Decisionmakers’ Preferences for Urban Regeneration: The Venice Arsenale

In this paper, we illustrate how surveys can be used to elicit the preferences of the public and of policymakers and city officials for regeneration projects at urban sites. Our methodology uses rating exercises, coupled with conjoint-choice stated preferences for the general public and with ranking exercises for the public officials and other stakeholders, and is then applied to investigate alternative reuses of the Venice Arsenale, Italy, and their economic, environmental and social impacts. One interesting feature of the conjoint choice questions for members of the public is that the responses to these questions can be used to estimate the social benefits of regeneration projects, i.e., how much people are willing to pay for these urban transformations. Another advantage of our approach is that it can be used seek and foster broader public participation into urban decisionmaking processes.Land Use, Decision-Making, Cleanup, Sustainable Development, Local Economic Development, Choice Experiments

Application of the Real Options in Engineering Design and Decision Making: Focus on Mine Design and Planning at Operational Level

Flexibility and adaptability are essential for long-term corporate success, and real options (RO) is the preferred tool for analysis. This research argues that uncertainty is a source of value as the opportunities that it presents can be leveraged by having a flexible system. As a contribution to knowledge, a relationship between the beta and flexibility index was derived, RO identification framework for mine operational decision-making was proposed and predictive data analytics was utilised to create managerial flexibility

A fuzzy model for sensitivity analysis in real options

This paper adopts a promising concept of uncertainty, incorporating both stochastic processes and fuzzy theory to capture the somewhat vague and imprecise ideas the manager has about the future expected cash flows, the profitability of the project, the costs of the project and many other variables involved in an investment decision. Thus, uncertainty in real option valuation can be faced introducing fuzziness in the fundamental items of the classical approach. In particular, three examples of real options are examined and the computational experiments are performed. It is shown that fuzziness can play the role of a sensitivity analysis of the real option value with respect to the key decisional variables

Valuing portfolios of interdependent real options using influence diagrams and simulation-and-regression: A multi-stage stochastic integer programming approach

Although real options generally occur within portfolios, most valuation approaches based on either option pricing or decision analysis alone focus on single well-defined options. In this paper we present a new approach for modelling and approximating the value of portfolios of interdependent real options using both influence diagrams and simulation-and-regression. The key feature of this approach is that it translates the interdependencies between real options into a set of constraints and then directly models the dynamics of all underlying uncertainties using (Markovian) stochastic processes. These are then integrated in a portfolio optimisation problem which is formulated as a multi-stage stochastic integer program. Applying a simulation and parametric regression approach to approximate the value of this optimisation problem, we present a transparent valuation algorithm that explicitly takes into account vector-valued exercise decisions and the state variable’s multidimensional resource component. The approach is therefore applicable to a wide range of complex investment projects with both inherent interdependent flexibilities and many underlying uncertainties. The approach is illustrated by evaluating a complex natural resource investment that features both a large portfolio of interdependent real options and four stochastic factors. We analyse the way in which the approximated value of the portfolio and its individual options are affected by the initial copper price as well as by the degrees of production cost and copper price uncertainty

Valuing infrastructure investments as portfolios of interdependent real options

The value of infrastructure investments is frequently influenced by enormous uncertainty surrounding both exogenous and endogenous factors. At the same time, however, their value is generally driven by much flexibility - i.e. options - with respect to design, financing, construction and operation. Real options analysis aims to pro-actively manage risks by valuing the flexibilities inherent in uncertain investments. Although real options generally occur within portfolios whose value is affected by both exogenous and endogenous uncertainty, most existing valuation approaches focus on single (i.e. individual) options and consider only exogenous uncertainty. In this thesis, we introduce an approach for modelling and approximating the value of portfolios of interdependent real options under exogenous uncertainty, using both influence diagrams and simulation-and-regression. The key features of this approach are that it translates the interdependencies between real options into linear constraints and then integrates these in a portfolio optimisation problem, formulated as a multi-stage stochastic integer programme. To approximate the value of this optimisation problem we present a transparent valuation algorithm based on simulation and parametric regression that explicitly takes into account the state variable's multidimensional resource component. We operationalise this approach using three numerical examples of increasing complexity: an American put option in a simple single-factor setting; a natural resource investment with a switching option in a one-factor setting; and the same investment in a three-factor setting. Subsequently, we demonstrate the ability of the proposed approach to evaluate a complex natural resource investment that features both a large portfolio of interdependent real options and four underlying uncertainties. We show how our approach can be used to investigate the way in which the value of that portfolio and its individual real options are affected by the underlying operating margin and the degrees of different uncertainties. Lastly, we extend this approach to include endogenous, decision- and state-dependent uncertainties. We present an efficient valuation algorithm that is more transparent than those used in existing approaches; by exploiting the problem structure it explicitly accounts for the path dependencies of the state variables. The applicability of the extended approach to complex investment projects is illustrated by valuing an urban infrastructure investment. We show the way in which the optimal value of the portfolio and its single, well-defined options are affected by the initial operating revenues, and by the degrees of exogenous and endogenous uncertainty.Open Acces

A probabilistic numerical method for optimal multiple switching problem and application to investments in electricity generation

In this paper, we present a probabilistic numerical algorithm combining dynamic programming, Monte Carlo simulations and local basis regressions to solve non-stationary optimal multiple switching problems in infinite horizon. We provide the rate of convergence of the method in terms of the time step used to discretize the problem, of the size of the local hypercubes involved in the regressions, and of the truncating time horizon. To make the method viable for problems in high dimension and long time horizon, we extend a memory reduction method to the general Euler scheme, so that, when performing the numerical resolution, the storage of the Monte Carlo simulation paths is not needed. Then, we apply this algorithm to a model of optimal investment in power plants. This model takes into account electricity demand, cointegrated fuel prices, carbon price and random outages of power plants. It computes the optimal level of investment in each generation technology, considered as a whole, w.r.t. the electricity spot price. This electricity price is itself built according to a new extended structural model. In particular, it is a function of several factors, among which the installed capacities. The evolution of the optimal generation mix is illustrated on a realistic numerical problem in dimension eight, i.e. with two different technologies and six random factors