A fuzzy approach for R&amp;D compound option valuation

Biancardi, Marta; Villani, Giovanni

doi:10.1016/j.fss.2016.10.013

Cited by 16 publications

(16 citation statements)

References 16 publications

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“…Combining the stochastic and fuzzy methods, Biancardi and Villani (2017) valued the R&D compound option. Tolga (2017) used Type-2 fuzzy logic to real option valuation.…”

Section: Pricing Derivatives In a Fuzzy Environmentmentioning

confidence: 99%

Stochastic approach to model spot price and value forward contracts on energy markets under uncertainty

Pawłowski

Nowak

2021

J Ambient Intell Human Comput

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The paper deals with a model of electricity spot prices. The proposed dynamics of electricity spot prices is driven by a mean reverting diffusion with jumps having hyperexponential distribution. The analytical formula for the forward contract’s price is derived in a crisp case. Inasmuch as the model parameters are considered to be evaluated imprecisely, their fuzzy counterparts are introduced. With usage of the fuzzy arithmetic, the analytical expression for the forward contract’s price is derived. Several numerical examples highlighting attributes of the fuzzy forward electricity prices are brought out.

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“…Combining the stochastic and fuzzy methods, Biancardi and Villani (2017) valued the R&D compound option. Tolga (2017) used Type-2 fuzzy logic to real option valuation.…”

Section: Pricing Derivatives In a Fuzzy Environmentmentioning

confidence: 99%

Stochastic approach to model spot price and value forward contracts on energy markets under uncertainty

Pawłowski

Nowak

2021

J Ambient Intell Human Comput

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“…Heng, Chen, and Tan (2014) stated that modeling compound options by Geske (1979) and the theory by Agliardi and Agliardi (2013), which generalizes Geske's theorem in time-dependent volatility and interest rate, could be applied in land expropriation practice in a fuzzy environment. Meanwhile, Biancardi and Villani (2017) modeled the compound American Exchange options with a fuzzy approach in their study. They declared that this option approach, which is suitable for sequential structure of R&D projects, could be used in a fuzzy environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Intuitionistic Fuzzy Real-Options Theory and its Application to Solar Energy Investment Projects

Ersen

Taş

Kahraman

2018

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Conventional methods used in project evaluation have a static nature, which cause decision makers to examine investment projects within a narrow scope. As an alternative, real-options theory allows decision makers to use the concepts of uncertainty and managerial flexibility, which classical methods do not deal with, in valuing investment projects. Carlsson and Fuller's fuzzy real-options theory has been a solution for situations where cash flow and cost variables used in the model cannot be expressed by using classical numbers but by using fuzzy numbers. The idea of using intuitionistic fuzzy numbers in real options theory can expand the narrow scope more effectively than the idea of using classical real-options theory, thereby providing an opportunity to evaluate investment projects from a wider perspective. In this study, intuitionistic fuzzy real-option valuation model has been developed and applied to the evaluation of solar energy investment projects. Studies have shown that solar energy investments that do not seem profitable today in Turkey can perform profitably in the future. Moreover, the historical volatility model differs from the expected valuable models, whereas the optimum investment year between the models formed by the uncertainty inductions from the expected values exhibit minimal difference.

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“…Hassanzadeh et al (2012) adopted fuzzy pay-off method for R&D project evaluation and developed a fuzzy R&D project portfolio model. Biancardi and Villani (2017) presented a fuzzy approach to value R&D investments. Hesarsorkh et al (2021) formulated a robust possibilistic optimization approach for pharmaceutical R&D project portfolio selection and scheduling under uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Pharmaceutical R &D portfolio optimization with minimum borrowed capital based on fuzzy set theory

Zhang

Liu²

2022

Soft Comput

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Due to long lead times, uncertain outcomes and lack of enough historical data, pharmaceutical research and development (R&D) portfolio selection is a often very complex decision issue. The aim of this paper is to investigate pharmaceutical R&D portfolio selection with unavailable and unreliable project information, where the borrowed capital is allowed. Based on fuzzy set theory, we propose two pharmaceutical R&D portfolio optimization models with minimum borrowed capital by taking into account corporate strategy in developing new products, scarcity of resources, lack of investment budget and cardinality constraint. In the two proposed models, the pharmaceutical R&D company is assumed to achieve the objectives of maximizing terminal wealth and minimizing the cumulative borrowed capital over the whole investment horizon. Then, we transform the two proposed bi-objective models into the corresponding single-objective models by using the weighted sum approach and employ the modified artificial bee colony (ABC) algorithm to solve the transformed models. Finally, we provide a numerical example to illustrate the application of the proposed models.

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A fuzzy approach for R&D compound option valuation

Cited by 16 publications

References 16 publications

Stochastic approach to model spot price and value forward contracts on energy markets under uncertainty

Stochastic approach to model spot price and value forward contracts on energy markets under uncertainty

Intuitionistic Fuzzy Real-Options Theory and its Application to Solar Energy Investment Projects

Pharmaceutical R &D portfolio optimization with minimum borrowed capital based on fuzzy set theory

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