Abstract
This paper describes methodology of dealing with financial modeling under uncertainty with risk and vagueness aspects. An approach to modeling risk by the Conditional Value at Risk methodology under imprecise and soft Conditions is solved. It is supposed that the input data and problem conditions are difficult to determine as real number or as some precise distribution function. Thus, vagueness is modeled through the fuzzy numbers of linear T-number type. The combination of risk and vagueness is solved by fuzzy-stochastic methodology. Illustrative example is introduced.
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Tang, Sf., He, Yy. (2013). Conditional Value at Risk Methodology under Fuzzy-Stochastic Approach. In: Huang, DS., Bevilacqua, V., Figueroa, J.C., Premaratne, P. (eds) Intelligent Computing Theories. ICIC 2013. Lecture Notes in Computer Science, vol 7995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39479-9_20
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DOI: https://doi.org/10.1007/978-3-642-39479-9_20
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