The valuation of multidimensional American real options using the LSM simulation method

In this paper we show how a multidimensional American real option may be solved using the LSM simulation method originally proposed by Longstaff and Schwartz [2001, The Review of the Financial Studies 14(1): 113–147] for valuing a financial option and how this method can be used in a complex setting. We extend a well-known natural resource real option model, initially solved using finite difference methods, to include a more realistic three-factor stochastic process for commodity prices, more in line with current research. Numerical results show that the procedure may be successfully used for multidimensional models, expanding the applicability of the real options approach.     

A fuzzy linguistic ontology payoff method for aerospace real options valuation

In this paper, we present a fuzzy linguistic ontology payoff method for the valuation of real options in the aerospace industry. Using real data, we apply the fuzzy linguistic approach to determine the credibility measures and the credibilistic expected value for the fuzzy real options valuation payoff method. This approach is used to obtain a multi-scenario modeling process by envisioning three scenarios: optimistic, most likely, and pessimistic. In addition, our experience with the scenario estimates is premised on results in an operating profit forecast. This forecast corresponds to a plausible outcome within the aerospace licensing maintenance, repair, and overhaul market and provides a decision-making tool. This tool can be utilized for determining real options for project valuation of aerospace licensing revenues based on unit costs, recurring costs, and quantity of units sold.     

Using jump-diffusion modeling for valuing real options in infrastructure projects

In infrastructure financed projects, in order to attract private investors, host governments often provide some guarantees. This paper develops a value model of minimum revenue guarantee with multiple-exercise real options under the impact of the emergency incident. The model is applied to infrastructure financed projects using the minimum revenue guarantee under simulation. The simulation results indicate that, before quantifying the value of the minimum revenue guarantee, it is necessary to forecast the jump degree and intensity of the emergency incident, as well as prevent and control risks arising from such emergencies. Otherwise, underestimation of the guarantee value will occur and the government will have to bear huge debt in this condition. We also analyze the dependence of the guaranteed value on the minimum guaranteed revenue level, initial revenue and number of exercise rights. For various conditions, the diagrams of the guaranteed value are also presented.     

Pricing real abandonment options on several R&;D investment projects

Much previous research has shown that the R&D investments can be evaluated by real growth options approach. But few studies have been done on real abandonment options for R&D projects which may not succeed. The contribution of this paper is not only to derive a more general closed-form solution for evaluating real abandonment options, but to put backup project consideration into our model for reality. We show that both Black-Scholes’s and Stulz’s models are special cases of our model under some specifications of parameters. From the simulation results, we explore that the higher the percentage of recovering salvage value, the more investment projects should be carried out. We hope that the results in this study could provide a useful reference for the manager, to make better decisions regarding backup projects.     

An evolutionary real options framework for the design and management of projects and systems with complex real options and exercising conditions

To address the issue of decision support for designing and managing flexible projects and systems in the face of uncertainties, this paper integrates real options valuation, decision analysis techniques, Monte Carlo simulations and evolutionary algorithms in an evolutionary real options framework. The proposed evolutionary real options framework searches for an optimized portfolio of real options and makes adaptive plans to cope with uncertainties as the future unfolds. Exemplified through a test case, the evolutionary framework not only compares favorably with traditional fixed design approaches but also delivers considerable improvements over prevailing real options practices.     

The valuation of RFID investment using fuzzy real option

Net present value (NPV) and return on investment (ROI) are commonly used to evaluate investment in new technologies. Sometimes, however, measuring the value of investment in new IT becomes very difficult due to its wide scope of application coupled with embedded options in its adoption. Therefore, comprehensive but easily understandable methodologies are needed to solve the complicated problems resulting from the complexity of new technologies. This paper employs a real option analysis to evaluate RFID adoption in the supply chain. Real options analysis should be a better way to evaluate a disruptive technology like RFID. However, the pure (probabilistic) real option rule characterizes the present value of expected cash flows and the expected costs by a single number, which is not realistic in many cases. To solve the problem, this paper considers the real option rule in a more realistic setting, namely, when the present values of expected cash flows and expected costs are estimated by trapezoidal fuzzy numbers. Specifically, it drew out their means and variance and presented a method of calculating fuzzy real options through numerical value examples of RFID investment assuming the current value of expected cash flow and investment costs using trapezoid fuzzy number fuzzy real options. Since advanced information technology such as RFID has very high risk and options such as change, extension, delay and withdrawal, etc., investment valuation using the real options technique should be done, and in the process, in a more realistic and practical approach, the fuzzy real options model presented in this study is judged to be useful.     

A real options approach for evaluation and justification of a hospital information system

Nowadays healthcare organizations globally recognize the importance of investing in information technologies to improve the quality of care delivery and reduce costs. The key drivers of healthcare sector such as continuously improving healthcare standards and insurance systems have introduced new requirements for hospitals, which in return provided a solid ground for decision-makers to consider implementing hospital information systems that are customized and improved versions of enterprise resource planning (ERP) systems designed according to the needs of the healthcare sector. The conventional discounted cash flow methods ignore the value of managerial and strategic flexibility inherent in these investments, which is crucial for justification of the investment decision. This study introduces a real options-based methodology which overcomes the limitations of traditional valuation methods and enables decision-makers to value an ERP system investment incorporating multiple options. The option valuation model developed in this study extends the binomial lattice framework to model a hospital information system (HIS) investment opportunity with compound options. The potential application of the proposed model is illustrated through evaluation of a real-world HIS investment.     

High-accuracy finite-difference methods for the valuation of options

A finite-difference scheme often employed for the valuation of options from the Black–Scholes equation is the Crank–Nicolson (CN) scheme. The CN scheme is second order in both time and asset. For a rapid valuation with a reasonable resolution of the option price curve, it requires extremely small steps in both time and asset. In this paper, we present high-accuracy finite-difference methods for the Black–Scholes equation in which we employ the fourth-order L-stable Simpson-type (LSIMP) time integration schemes developed earlier and the well-known Numerov method for discretization in the asset direction. The resulting schemes, called LSIMP–NUM, are fourth order in both time and asset. The LSIMP–NUM schemes obtained can provide a rapid, stable and accurate resolution of option prices, allowing for relatively large steps in both time and asset. We compare the computational efficiency of the LSIMP–NUM schemes with the CN and Douglas schemes by considering valuation of European options and American options via the linear complementarity approach.     

New procedure for valuing patents under imprecise information with a consensual dynamics model and a real options framework

In this paper we present a new procedure for patent selection and ranking that uses a new dynamic consensus model and a new variant of the Matching Method real options framework. The procedure utilizes opinions from multiple experts in the creation of a stream of consensual yearly cash-flows for each patent. The cash-flow stream is used as an input in continuous-time real option valuation of each patent. Continuous time analysis of the patents allows for optimizing the timing of dropping patents from the portfolio. The procedure is relatively easy to apply in a practical setting, and is consistent with financial theory. Using the procedure is illustrated with a numerical example. Ranking result sensitivity to parameter value selection is studied separately.     

Fuzzy decision making model for selection of real time location systems

Real Time Location Systems (RTLS) have gained importance in contemporary world since they allow real time positioning of assets, people and workflows. They can be used in different sectors to increase work efficiency and quality in areas of application. The selection of the appropriate RTLS technology becomes a major decision problem since it has a multi-criteria structure which includes both qualitative and quantitative factors. In this study, a decision making model is developed for selection of the appropriate RTLS technology for companies operating in different sectors. Three main criteria are determined by existing literature and with the help of the experts, namely economic, technical and implementation factors. The Fuzzy Analytic Hierarchy Process (FAHP) method is proposed to select the appropriate RTLS technology. Also sensitivity analysis is performed according to the incremental rates of main criteria. The model is applied to a hospital in Turkey considering three types of RTLS systems which are given as IR–RF hybrid, UHF RFID and Active RFID. Since the scores of the hybrid system for economic, implementation and technical factors are higher comparing the other technologies, it is selected as the best alternative.     

Simulation-based evaluation of defuzzification-based approaches to fuzzy multiattribute decision making

This paper presents a simulation-based study to evaluate the performance of 12 defuzzification-based approaches for solving the general fuzzy multiattribute decision-making (MADM) problem requiring cardinal ranking of decision alternatives. These approaches are generated based on six defuzzification methods in conjunction with the simple additive weighting (SAW) method and the technique for order preference by similarity to the ideal solution method. The consistency and effectiveness of these approaches are examined in terms of four new objective performance measures, which are based on five evaluation indexes. The simulation result shows that the approaches, which are capable of using all the available information on fuzzy numbers effectively in the defuzzification process, produce more consistent ranking outcomes. In particular, the SAW method with the degree of dominance defuzzification is proved to be the overall best performed approach, which is followed by the SAW method with the area center defuzzification. These findings are of practical significance in real-world settings where the selection of the defuzzification-based approaches is required in solving the general fuzzy MADM problems under specific decision contexts.     

Fuzzy model tuning using simulated annealing

This paper presents the use of simulated annealing metaheuristic for tuning Mamdani type fuzzy models. Structure of the Mamdani fuzzy model is learned from input–output data pairs using Wang and Mendel’s method and fuzzy c-means clustering algorithm. Then, parameters of the fuzzy system are tuned through simulated annealing. In this paper, we perform experiments to examine effects of (a) initial solution generated by Wang and Mendel’s method and fuzzy c-means clustering method, (b) membership function update procedure, (c) probability parameter for the calculation of the initial temperature, (d) temperature update coefficient used for cooling schedule, and (e) randomness level in the disturbance mechanism used in simulated annealing algorithm on the tuning of Mamdani type fuzzy models. Experiments are performed with Mackey–Glass chaotic time series. The results indicate that Wang and Mendel’s method provides better starting configuration for simulated annealing compared to fuzzy c-means clustering method, and for the membership function update parameter, MFChangeRate ∈  (0, 1], and the probability parameter for the calculation of the initial temperature, P₀ ∈  (0, 1), values close to zero produced better results.     

Multi-objective optimization of water distribution systems based on a real options approach

This paper describes a multi-objective optimization model including Real Options concepts for the design and operation of water distribution networks. This approach is explained through a case study with some possible expansion areas defined to fit different future scenarios. A multi-objective decision model with conflicting objectives is detailed. Also, environmental impacts are considered that take into account not only the life cycle carbon emissions of the different materials used during the construction of the networks but also the emissions related to energy consumption during operation. These impacts are translated by giving a cost to each tonne of carbon dioxide emitted. This work presents a new multi-objective simulated annealing algorithm linked to a hydraulic simulator to verify the hydraulic constraints, and the results are represented as points on the Pareto front. The results show that the approach can deal explicitly with conflicting objectives, with environmental impacts and with future uncertainty.     

Adaptive control of a pressure-controlled artificial ventilator: a simulator-based evaluation using real COPD patient data

The paper discusses the application of a direct adaptive controller to a pressure controlled artificial ventilation problem. In pressure controlled ventilators, the manipulated variable is the maximum flow applied to the patient during the active phase (inspiration), and the regulated variable is the peak pressure at end-inspiration. This simulation case study focuses on patients diagnosed with Chronic Obstructive Pulmonary Disease (COPD), which require artificial/mechanical ventilation. An adaptive PID controller ensures peak pressures below critical values, by manipulating the flow delivered by the ventilator. The simulation study is performed on fractional-order models of the respiratory impedance identified from lung function data obtained from 21 COPD patients. Additional simulation studies show the robustness of the controller in presence of varying model parameters from the respiratory impedance of the patient. Possibilities to implement the control strategy as an online adaptive algorithm are also explored. The results show that the design of the control is suitable for this kind of application and provides useful insight on realistic scenarios.     

Multiscale methods for the valuation of American options with stochastic volatility

This paper deals with the efficient valuation of American options. We adopt Heston's approach for a model of stochastic volatility, leading to a generalized Black–Scholes equation called Heston's equation. Together with appropriate boundary conditions, this can be formulated as a parabolic boundary value problem with a free boundary, the optimal exercise price of the option. For its efficient numerical solution, we employ, among other multiscale methods, a monotone multigrid method based on linear finite elements in space and display corresponding numerical experiments.     

Performance evaluation of a multithreaded RTS using a synchronous reactive model

Synchronous reactive modelling provides an optimal framework for the modular decomposition of programs that engage in complex patterns of deterministic interaction, such as many real-time and communication entities. This paper presents an approach which includes performance modelling techniques in the synchronous reactive modelling method supported by ESTEREL. It defines a methodology based on timing and probabilistic quantitative constructs that complete the synchronous reactive models. A monitoring mechanism allows the computation of performance results during the simulation. This methodology is applied to study a multithreaded runtime system for a distributed functional programming language. Performance metrics are computed and validated with experimental results.     

普适环境中基于模糊理论的信任评估模型

信任管理是当前普适环境中信息安全研究的热点。为了解决主观信任的模糊性和主体之间信任关系的动态性,在模糊数学中的模糊理论的基础上,提出了一个用户信任度的评估模型。该模型是将模糊理论应用到信任关系的不确定性和动态性上,对影响信任主体的主要因素进行综合评估,得出一个总的信任度来确定是否可以信任主体以便进行交互。该模型可以全面地评估用户的信任度,很大程度上保证了普适行为的安全可靠,为处理普适计算的信任管理的不确定研究提供了一个新的方法。     

Autonomic query parallelization using non-dedicated computers: an evaluation of adaptivity options

Writing parallel programs that can take advantage of non-dedicated processors is much more difficult than writing such programs for networks of dedicated processors. In a non-dedicated environment such programs must use autonomic techniques to respond to the unpredictable load fluctuations that prevail in the computational environment. In adaptive query processing (AQP), several techniques have been proposed for dynamically redistributing processor load assignments throughout a computation to take account of varying resource capabilities, but we know of no previous study that compares their performance. This paper presents a simulation-based evaluation of these autonomic parallelization techniques in a uniform environment and compares how well they improve the performance of the computation. Four published strategies are compared with a new algorithm that seeks to overcome some weaknesses identified in the existing approaches. In addition, we explore the use of techniques from online algorithms to provide a firm foundation for determining when to adapt in two of the existing algorithms. The evaluations identify situations in which each strategy may be used effectively and in which it should be avoided.     

Fuzzy regression model of R&D project evaluation

Engineering and technology play an important role in strengthening the competitive power of a company and in surviving a severe competition in the world. About 70% of the total R&D investment in Japan comes from the private sector. It is the most important to decide which research projects have to be adopted for a future research out of proposals from divisions and sections in a company. The objective of this paper is to analyze the results of experts’ evaluation in selecting submitted proposals for R&D and to model the experts’ evaluation. This paper analyzes a research and development of a certain manufacturing company in a heavy metallurgy industry.We employed a principal component model, dual scaling, AHP and fuzzy regression analysis to analyze the results that experts evaluated proposed research projects for single or plural of fiscal years. The experts’ evaluation was pursued on the basis of (1) the objective of a research project, (2) its background, (3) its research contents, (4) the expected effect, (5) the possibility of obtaining patents, (6) project schedule, (7) developing cost, etc. The obtained model results in the same selection of projects as the experts did.     

Fuzzy system reliability evaluation using time-dependent intuitionistic fuzzy set

In general, fuzzy sets are used to analyse the system reliability. In this article, the concept of fuzzy set is extended by the idea of intuitionistic fuzzy set (IFS) and a new general procedure is proposed to construct the membership and non-membership functions of the fuzzy reliability using time-dependent IFS. Here, failure rate function of the system is represented by a triangular intuitionistic fuzzy number (IFN). Also, using proposed approach, membership and non-membership functions of fuzzy reliability of series and parallel systems are constructed, where the failure rate of each component is taken as a time-dependent triangular IFN. The major advantage of using IFS over fuzzy sets is that IFS separate the positive and negative evidences for membership of an element in the set. Numerical examples are given to illustrate the proposed approach.