时 间：2026年11月3日（周二）13：00 -14：00
地 点：普陀校区理科大楼A1714室
报告人：徐玉红 苏州大学研究员
邀请人：钱林义 华东师范大学统计学院
摘 要：
We propose a general CoVaR framework that extends the traditional CoVaR by incorporating diverse expert views and information, such as asset moment characteristics, quantile insights, and perspectives on the relative loss distribution between two assets. To integrate these expert views effectively while minimizing deviations from the prior distribution, we employ the entropy pooling method to derive the posterior distribution, which in turn enables us to compute the general CoVaR. Analytical expressions of the general CoVaR and its spillover measure, Delta-CoVaR, are derived under the bivariate normal setting, revealing linear dependence on posterior expectations and nonlinear sensitivity to higher-order statistics such as variances, quantiles, and correlations. Empirical evidence from the U.S. banking sector during the Federal Reserve’s rate hikes demonstrates that the general CoVaR, when specified with appropriate distributional or quantile views, significantly improves predictive accuracy and stability relative to traditional CoVaR. The proposed framework offering a flexible and rigorous tool for systemic risk assessment, stress testing, and decision-making under uncertainty.
报告人简介：
徐玉红，苏州大学正高级研究员，博士生导师，仲英青年学者。师从彭实戈院士。先后在法国西布列塔尼大学(博士后)、新加坡国立大学、香港大学、香港理工等学校访问。长期从事数理金融和金融人工智能方面的研究，研究结果在金融数学期刊《Mathematical Finance》、管理科学综合类期刊《Management Science》等杂志发表。先后任中国运筹学会金融工程与风险管理分会常务理事、中国优选法统筹法与经济数学研究会量化金融与保险分会理事、全国工业统计研究会金融科技与大数据分会理事、中国工业与应用数学协会金融数学与工程和精算保险分会常务委员、金融数学与数据处理年会发起人。先后主持国家自然科学基金3项、江苏省自然科学基金2项、上海市开放课题1项。