Credibilistic Fuzzy Risk Measure And Optimization Models

Risk is future's uncertain loss. Randomness and fuzziness are two basic types of uncertainties in financial market. The traditional risk analysis optimization models in a stochastic environment was established as one of the most useful tools of probability. However, people are always with subjective judgment when they will invest. The research object of this thesis is fuzzy risk analysis. The quantitative analysis method is adopted in this thesis and the credibility theory is introduced to study the risk assessment technology.This thesis gives a systematic exposition of its theoretical foundation, components, risk opti-mization models based on the extensive reading of relevant literature at home and abroad. In the mean time, this paper makes research into the risk models, and carries out some experimental and analytical study.The article is structured as follows:Firstly, we have established the coherent fuzzy risk mea-surement axiom system based on credibility theory. The coherent fuzzy risk measure satisfies such as positive homogeneity, monotonicity, translation invariance and subadditivity under inde-pendence condition. In this axiom system, the definitions and properties of some credibilistic fuzzy risk measures have been demonstrated. Secondly, we establishes some risk optimization models with fuzzy income and design a hybrid intelligent algorithm, which include fuzzy simu-lations, genetic algorithm and neural network; Finally, we provide some numerical examples for the proposed models and employ the hybrid intelligent algorithm to solve them.