A Fuzzy Pay-off Method for Real Option Valuation

Real Options analysis offers interesting insights on the value of assets and on the profitability of investments, which has made real options a growing field of academic research and practical application. Real option valuation is, however, often found to be difficult to understand and to implement due to the quite complex mathematics involved. Recent advances in modeling and analysis methods have made real option valuation easier to understand and to implement. This paper presents a new method (fuzzy pay-off method) for real option valuation using fuzzy numbers that is based on findings from earlier real option valuation methods and from fuzzy real option valuation. The method is intuitive to understand and far less complicated than any previous real option valuation model to date. The paper also presents the use of number of different types of fuzzy numbers with the method and an application of the new method in an industry setting.Real Option Valuation; Fuzzy Real Options; Fuzzy Numbers

Review of modern numerical methods for a simple vanilla option pricing problem

Option pricing is a very attractive issue of financial engineering and optimization. The problem of determining the fair price of an option arises from the assumptions made under a given financial market model. The increasing complexity of these market assumptions contributes to the popularity of the numerical treatment of option valuation. Therefore, the pricing and hedging of plain vanilla options under the Black–Scholes model usually serve as a bench-mark for the development of new numerical pricing approaches and methods designed for advanced option pricing models. The objective of the paper is to present and compare the methodological concepts for the valuation of simple vanilla options using the relatively modern numerical techniques in this issue which arise from the discontinuous Galerkin method, the wavelet approach and the fuzzy transform technique. A theoretical comparison is accompanied by an empirical study based on the numerical verification of simple vanilla option prices. The resulting numerical schemes represent a particularly effective option pricing tool that enables some features of options that are depend-ent on the discretization of the computational domain as well as the order of the polynomial approximation to be captured better

COG-FPOM: adapted fuzzy pay-off method for real options valuation: application in the abandonment decision of petroleum producing fields

This paper presents the COG-FPOM, a model based on the Fuzzy Pay-Off Method (FPOM). The FPOM is a scenario-based real option valuation method that uses fuzzy numbers as possibility distributions. The paper shows an unexpected result generated by the original FPOM, in which the real option would have a negative value. It further analyses its reasons and suggests a way to overcome it, by using the center of gravity (COG) instead of the possibilistic mean to summarize a fuzzy number. The overall work is an ongoing project that aims to apply the presented model to support the abandonment decision of petroleum producing fields – an initial attempt together with its preliminary outcomes are shown here. Although not concluded, the COG-FPOM and its pilot results indicate a good potential for the sequence of the project

The use of fuzzy logic and expert systems for rating and pricing firms: a new perspective on valuation.

This paper presents an expert system aimed at evaluating firms and business units. It makes use of fuzzy logic and integrates financial, strategic, managerial aspects, processing both quantitative and qualitative information. Twenty-nine value drivers are explicitly taken into account and combined together via “if-then” rules to produce an output. The output is a real number in the interval [0,1], representing the value-creation power of the firm. The system may be used for rating, ranking and pricing firms as well as for assessing the impact of managers’ decisions on value creation and as a tool of corporate governance.Firm valuation, fuzzy logic, expert system, acquisition, rating, pricing

ISIPTA'07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications

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Modeling multi-criteria decision-making problems with applications in last mile delivery and school safety assessment

The last-mile delivery option has become a focal point of academic research and industrial development in recent years. Multiple factors such as increased demands on delivery flexibility, customer requirements, delivery urgency, and many others are enforcing to adopt this option. For fulfilling this paradigm shift in delivery and providing additional flexibility, drones can be considered as a viable option to use for last-mile delivery cases. Numerous drones are available in the market with varying capacities and functionalities, posing a significant challenge for decision-makers to select the most appropriate drone type for a specific application. For this purpose, this study proposes a comprehensive list of criteria that can be used to compare a set of available last-mile delivery drones. Additionally, we introduced a systematic multi-criterion, multi-personnel decision-making approach, referred to as the Interval Valued Inferential Fuzzy TOPSIS method. This method is robust and can handle the fuzziness in decision-making, thereby providing quality drone selection decisions under different applications. We then apply this method to a real-life test setting. Results suggest that smaller drones or quadcopters are considered viable to use in urban environments, while long-range drones are preferred for the last mile delivery needs in rural settings