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2018年中国人民大学风险管理与精算两岸学术研讨会
时间:2018-05-07

时间:2018510日,地点明德主楼1031

会议议程

9:00—9:20 开幕式  欢迎致辞

9:20-—11:00 专题报告

Information Asymmetry in Medical Insurance: An Empirical Study of One Life Insurance Company in Taiwan

报告时间:9:201000

报告人:陈耀东,教授,台湾铭传大学 风险管理与保险学系

摘要 Based on the data from one anonymous but renowned life insurance company in Taiwan, this paper focuses on a big data analysis using R programming language while adhering to the requisite conditions for the validity of using regression model to test the existence of moral hazard in medical insurance. The log-linear regression model is fitted with the data set and it is shown: There is a significantly non-linear positive relationship between the compensations and the insurance coverage, indicating that the issue of moral hazard does exist among those claimants under discussion; the older those claimants were insured, the more they claimed; women claimed more medical compensation than men; those paying premiums quarterly claimed the highest.

Techniques to Analyze and Forecast Mortality

报告时间: 10:00-1040

报告人:Han Li, Assistant ProfessorBusiness & Economics Department , Macquarie University

摘要The increasing amount of attention paid on longevity risk and funding for old age has created the need for precise mortality models and accurate mortality forecasts. In this talk we are going to talk about some modern econometrics and statistical techniques to better capture mortality patterns and improve the accuracy of future mortality projections. These techniques will be applied tomortality data from a wide range of developed countries including the Great Britain, the United States, Australia, Netherlands, Japan, France and Spain over the post-war period 1950–2009. Contributions have been made to the existing literature with focus given to the forecasting perspective of models and to the analysis of cohort effects.

Claims Frequency Modeling Using Telematics Car Driving Data

报告时间: 1040-11:20

报告人:高光远,讲师,中国人民大学统计学院

摘要:We investigate the predictive power of covariates extracted from telematics car driving data using the speed-acceleration (v-a) heatmaps of Gao and Wüthrich (2017) for claims frequency modeling. These telematics covariates include the K-means classification, the principal components, and the bottleneck activations from a bottleneck neural network. It turns out that the first principal component and the bottleneck activations give a better out-of-sample prediction for claims frequencies than other traditional pricing factors such as driver`s age. For this reason we recommend the use of these telematics covariates for car insurance pricing.

11:20-12:00  讨论

12:00-13:00  午餐