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There are two ways in which the risk of a capital project can be described. This article outlines these two approaches: Sensitivity Analysis and Probability Analysis, and emphasises the connection between the two methods. The output of a computer model of the sensitivity of the project to underlying factors is used as input for a probability analysis. The methods are illustrated with a case study, the MM Co Ltd.

The robust appraisal of exploration drilling concepts is essential for establishing the economic viability of a prospective recovery field. This study evaluates the different concept selection methods that were considered for drilling operations at the Trym field in Norway. The construction of drilling rigs is a capital-intensive process, and it involves high levels of economic risk. These risks can be broadly categorised as aleatoric (i.e. those related to chance) and epistemic (i.e. those related to knowledge). Evaluating risks in the investment appraisal process tends to be a complicated process. Project risks are evaluated using Monte Carlo simulation (MCS) and are based on the fuzzy analytic hierarchy process (AHP). MCS provides a useful means of evaluating variabilities (i.e. aleatoric risks) in oil drilling operations. However, many of the economic risks in oil drilling processes are unanticipated, and, in some cases, are not readily expressible in quantitative values. The fuzzy AHP is therefore used to appraise the qualitatively defined indirect revenues comprising risks that affect future flexibilities, schedule certainty and health and safety performance. Both the Monte Carlo technique and the fuzzy AHP technique found that a cumulative revenue variation of up to 30% is possible in any of the considered drilling options. The fuzzy AHP technique estimates that the chances of profitability being less than NOK 1 billion over a five-year period is 0.5%, while the Monte Carlo technique estimates suggest a more conservative proportion of 10%. Overall, the fuzzy AHP technique is easy to use and flexible, and it demonstrates increased robustness and improved predictability.

The purpose of this paper is to deal with the maximum interest rate guarantees (MIRGs), and develop a methodology for setting the optimal value of the interest rate cap, namely the maximum interest rate above which the private investor will obtain reimbursement from the government, which balances the interests of the parties involved in the project.

The mechanism underlying the MIRG is modeled through real options. Monte Carlo simulation is employed as the option-pricing method. The resulting real option-based model is applied to the case of the “Camionale di Bari” toll road (Southern Italy).

The application provides some insights for the policy maker called to define the proper forms of guarantees. Furthermore, the results support the negotiation process, allowing the different actors to structure the guarantee in a way that satisfies all the parties and fairly allocates risks between them according to different operational and financial conditions.

The novelty of the contribution is triple. First, the authors advance the state of the art on government supports by focusing on the interest rate guarantee. Second, the authors enrich the existing studies on MIRG by proposing a quantitative model to set the guarantee in compliance with the public–private win-win principle. The developed real option-based model supports the decision maker in finding the optimal value of the interest rate cap, which is able to satisfy the interests of the parties involved in the project. Third, the authors consider not only the private sponsor and the government, as traditionally made by the models developed for other guarantees, but also the lender.

The purpose of this chapter is to serve as a basic guide to introduce the reader to different types of valuation techniques utilized when valuing new technologies. The goal is to familiarize the reader with the differing techniques along with some of the issues in utilizing them. The chapter begins with the foundation of corporate finance – the time value of money – and moves through brief discussions on discounted cash flow, decision tree analysis, Monte-Carlo analysis, and real option analysis. The chapter ends with a discussion emphasizing the need to place valuation into a larger context of firm control rights and ownership.

The present paper aims to introduce a new methodology taking risk behavior of decision maker into account to fine‐tune the value of a risky public‐private‐partnership (PPP) project and the corresponding cost of capital based on the target rate of return set by the project sponsor and the degree of project risks.

The proposed methodology combines the cumulative prospect theory (CPT) to characterize the risk preference of the project sponsor and the Monte Carlo simulation to assess the project riskiness. The methodology requires a pre‐set target rate of return that will define the relative gains and losses for a prospect theory project sponsor. The application was illustrated using a build/operate/transfer toll road project as a case study.

As the project sponsor sets a greater target return, the probability of the project not meeting the target is accordingly greater. Given that losses have greater impact than gains on the decision, other things being equal, a higher target return leads to a higher value correction. It has also been demonstrated that the corresponding project's cost of capital can be up‐ or downadjusted depending on the project's riskiness which may result in a reverse preference to favor a higher risk scenario.

The methodology uses the CPT parameters that need to be further confirmed and validated if applied to value large risky projects like PPP investments.

The proposed methodology offers a different approach to correctly value a risky PPP project by extending the application of the cumulative prospect theory that well explains the irrationality of human decision behavior under risk into a financial decision‐making process. It takes the full benefit of simulation to understand project risks and also assists financial decision‐making.

The liquefied natural gas (LNG) business comprises a number of economic activities with inherent risks. The purpose of this paper is to propose an integrated modelling approach, as part of the investment decision‐making process, for optimising economic returns from LNG whilst taking into account uncertainty in various key input parameters.

Inter‐linked cash flow and pricing models of the LNG chain were constructed. Net present value was maximised based on selection of netback pricing variables and level of investment shareholding. Constraints were placed on the minimum acceptable returns. The risk affinity of the decision maker was captured in the form of a chance‐constrained optimisation problem. A genetic algorithm was applied for numerical optimisation, in combination with Monte Carlo simulations to account for the stochastic nature of the problem.

Based on the results of a case study, the deterministic solution, having no consideration to uncertainty, was found to be both sub‐optimal and provided an unsatisfactory risk outcome. The stochastic approach yielded an optimal solution with due consideration to risk. Various scenarios show that the choice of the decision variables significantly impacts the trade‐off between risk and returns along the LNG chain to government and investor.

The suitability of the methodology to the operational phase of the LNG business which incorporates different elements of risk, such as market dynamics and logistics, is as yet untested.

This framework may be useful in the formulation of optimal commercial structure of firms, investment portfolio and gas/LNG pricing arrangements for host governments involved in the LNG business.

Extreme temperature and humidity are major causes of errors in industrial precision and in dimensional measurement. In addition, hot and/or humid workplaces have adverse effects on humans which include reduced work time due to more frequent and longer breaks, reduced production rates, and increased error rates. Managers considering investments in air conditioning their manufacturing facilities must weigh many uncertain benefits against more certain costs. In this paper, we identify the benefits resulting from air conditioning manufacturing facilities and develop a Monte Carlo computer simulation model to evaluate investments in air conditioning. The model uses projected incremental cash flows to compute net present value and internal rate of return. Simulation is used to take into account the uncertainty associated with projecting the benefits of air conditioning, deal with possible correlation among some benefits of air conditioning, and sensitize decision makers to the range of possible outcomes. The proposed model is programmed into user‐friendly menu‐driven software which is tested on actual cases and is illustrated in this paper.

Evaluating investments in new product development has been a much debated question over the last few years. Traditional methods are recognized as inadequate but there is no dominant methodology. Presents a case study, developed at Olivetti, in which new and old methodologies have been used together to analyse and evaluate an investment project in new product development. The suggested approach supports decision making better than scoring methods, and data obtained are more reliable than those provided by traditional financial methods.

Capital budgeting in a live environment is crucially influenced by exposure to risk. Argues that while there are many risk analysis techniques that could be used to assist with investment appraisal (for example the incorporation of risk premiums in discount rates, simulation, sensitivity analysis, etc.), it is not often recognized that the most widely used method – net present value (NPV) and nearly all of its variants – will often lead to incorrect conclusions when exposure to risk is not correctly incorporated. When risk is properly accounted for, surprising results emerge in evaluating project viability and sensitivity with respect to risk.

The quantitative assessment of the degree of risk associated with the direct acquisition of commercial property for investment purposes is practically non‐existent. There is almost always a total reliance on unquantified subjective feeling with no attempt to transform such a qualitative treatment into an analytically more acceptable and useful form. Whilst the investment capitalisation rate should, to an extent, reflect the investor's view of the future earnings capacity of a particular property, this yield rate is principally a function of general market sentiment and may not significantly allow for the inherent risk characteristics of an individual investment. This is especially the case at the prime end of the market where the pressure of funds competing to invest in a sector of particularly limited supply remains most severe.