报告简介：During the last two decades, there's a significant increase in economic costs of natural disasters. A variety of parametric insurance has been developed to help the government to withstand the economic losses caused by nature disasters. Among which, earthquake parametric insurance in Yunnan province is a special case. It sets the magnitude of earthquake as the payout trigger. However, as a main limitation of parametric insurance, basis risk is inevitable. To reduce basis risk, a Bayesian spatial quantile regression model is proposed. Earthquake parametric insurance, including its indemnification standards and pricing, is then discussed based on the proposed model and methodologies. The impact of earthquake hazard, risk exposure and vulnerability on economical losses are analyzed and considered in the quantile regression model. Since risk exposure and vulnerability of the epicenter can not be observed and will be treated as latent variables in quantile regression model. Bayesian approaches are applied, and spatial correlation is considered to construct the prior distributions for the latent variables. As a highly seismic region, Yunnan province carried out earthquake parametric insurance since 2014. To obtain a better evaluation of earthquake risk a satisfactory parametric insurance in Yunnan province, the proposed model and methods are applied. With historical earthquake economic losses of Yunnan province from 1992 to 2019, the payments and premiums of 16 different regions in Yunnan Province are calculated. Results show that the basis risk is reduced significantly and the loss ratio is more reasonable compared to the current earthquake insurance.

报告人简介：李云仙，毕业于香港中文大学统计系，现为云南财经大学金融学院保险系教授、硕导。主持国家社会科学基金、国家自然科学基金以及省部级课题多项，参与国家级和省部级课题多项；参与建设省级创新团队、重点实验室，以及云南省防灾减灾智库；在数理统计与管理，Journal of Multivariate Analysis，Insurance: Mathematics and Economics，Empirical Economics等国内外杂志发表论文多篇。