基于分数阶矩和分片Wasserstein距离的鲁棒风险度量优化模型OA北大核心CHSSCDCSSCICSTPCD

Robust risk measurement optimization is a class of important issue in the stochastic risk measurement.The cap-ture of uncertain distribution information depends on imperfect assumptions and estimates,and the reflection of the tail of distribu-tion directly affects the accuracy of distribution prediction.The error of the tail model can cause serious consequences in actual risk management decision-making.This paper constructs a robust risk measurement optimization model in a way that is robust to the error of the tail model.In the case of constructing the model uncertainty set,a distribution estimation method based on Wasser-stein metric is proposed to overcome the limitation that the existing parameter distributions cannot reflect the real distribution tail behavior.In view of the ability of fractional moment to accurately describe the tail information of distribution,a robust risk mea-surement model with fractional moment constraints is established based on the analysis of distribution estimation.Furthermore,a piecewise robust risk measurement model with piecewise Wasserstein metric constraints is constructed based on fiber bundles the-ory to optimize the tail model error caused by the fact that Wasserstein metric ignores the distribution geometry.Finally,the simu-lation analysis of normative data is performed,and the results show that the model can accurately quantify the risk of sudden ex-treme loss.