Valuation of R&D projects of new energy vehicles based on generalized mixed sub-fractional Brownian motion under fuzzy environment
The industry and investors are closely monitoring the valuation of research and development (R&D) projects related to new energy vehicles (NEVs) as their technology advances rapidly. However, standard techniques of valuation often fail to describe the full value of R&D initiatives in an uncertain market environment, due to the significant technical hazards and uncertain results associated with these projects. To address this issue, this study creatively introduces a generalized mixed sub-fractional Brownian motion (GMSFBM) based valuation method for compound real options in the NEV R&D projects. In order to more accurately characterize the technical and financial risks at every stage of the R&D process, this paper combines stochastic processes with compound real option theory. Significantly, to derive the assessment model for NEV R&D projects, a fuzzy partial differential equation (FPDE) of the five-fold compound real option model is constructed using Itô's lemma. Moreover, asymmetric trapezoidal fuzzy parameters are also introduced by the valuation model to characterize the uncertainty of the NEV R&D projects. Finally, numerical experiments are conducted to validate the effectiveness and practicality of the GMSFBM model in the appraisement of the NEV R&D projects, providing new quantitative analysis tools and methods for project decision-making. • A GMSFBM valuation method for compound real options in the NEV R&D projects has been introduced. • A FPDE of the five-fold compound real option model is constructed. • Asymmetric trapezoidal fuzzy parameters are also introduced by the valuation model. • The effectiveness of the GMSFBM model for the NEV R&D projects is verified from numerical simulation.
Authors
- Guitian He (ORCID: https://orcid.org/0000-0002-9086-443X)
- Wenjie Liang (ORCID: https://orcid.org/0009-0000-3468-1930)
- Weiting Zhang (ORCID: https://orcid.org/0000-0002-7473-2234)
- Maokang Luo (ORCID: https://orcid.org/0000-0003-2793-6012)
Institutions
- Guangxi University (CN)
- Sichuan University (CN)
Publication Details
- Journal
- Applied Mathematics and Computation
- Published
- 2025-05-28
- DOI
- https://doi.org/10.1016/j.amc.2025.129559
- Primary Topic
- Fuzzy Systems and Optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Guangxi Zhuang Autonomous Region
- National Natural Science Foundation of China
- Natural Science Foundation of Guangxi Province