报告一
报告题目：On the optimality of deductibles with heterogeneous beliefs
报告人：池义春教授，中央财经大学中国精算研究院
报告时间地点：12月19日周一上午9:00-10:00统计楼103
报告摘要：
Abstract: It is known from Arrow's theorem that a risk averse insured, who wants to maximize the expected utility of its nal wealth, will choose a deductible insurance policy when the insurance premium depends only upon the actuarial value of the coverage. It is noteworthy that Arrow's result is based on the assumption of the same probabilistic belief about the underlying loss for the insured and the insurer. Unfortunately, this assumption does not often hold in insurance practice, and both parties usually have dierent subjective beliefs because they possess asymmetric information. This paper attempts to extend Arrow's theorem of the deductible to the case of heterogeneous beliefs. In contrast to the literature, we try to exclude the insurance contracts with expost moral hazard from the analysis. In particular, both parties are asked to pay more for a larger realization of the loss, just like Huberman et al. (1983). It is shown that,ceteris paribus, the deductible insurance is optimal for a risk averse insured if and only if the insurer is more optimistic about the positive loss than the insured in the sense of monotone hazard rate order. This result can partly explain the popularity of deductible insurance in the market where the insurer with the diversication be net and subject matter expertise is relatively optimistic. Finally, we derive the optimal deductible level explicitly for expected value premium principle, and investigate how it is a ected by the changes of the insured's risk aversion, the insurance price and belief heterogeneity.
报告二
报告题目：On the occupation times in a delayed Sparre Andersen risk model with exponential claims
报告人：Dr.Xueyuan Wu,Faculty of Business and Economics, The University of Melbourne
报告时间地点：12月20日周二上午9:00-10:00统计楼103
报告摘要：
In this paper, we study the joint Laplace transform of the occupation times in disjoint intervals until ruin in a delayed Sparre Andersen risk model with general inter-claim times and exponential claims. We extend the transformation method in the literature and apply the theoretical fluctuation techniques to derive an explicit expression of the joint Laplace transform under consideration. Further, with the presence of a constant dividend barrier, we derive explicit expressions for the Laplace transforms of the time of ruin and the non-dividend paying duration, namely the total length of non-dividend paying periods prior to ruin. This quantity is of practical interest but has not been studied in the literature to date. Within this paper, all of the Laplace transforms are expressed in terms of scale functions associated with the given spectrally negative Levy process. Numerical examples are also provided at the end of this paper regarding the Laplace transform of the non-dividend paying duration to illustrate how the distribution of this occupation time behaves in response to varying parameters and the impact of delay on the occupation times comparing with an ordinary Sparre Andersen risk model.
报告三
报告题目：Integrated Species Distribution Models: Combining presence-background data and site-occupancy data
报告人：Dr. Yan Wang, School Sciences, RMIT University
报告时间地点：12月20日周二上午10:00-11:00统计楼103
报告摘要：
Two main sources of data for species distribution models (SDMs) are site-occupancy (SO) from planned surveys, and presence-background (PB) data from opportunistic surveys and other sources. SO surveys give high quality data about presences and absences of the species in a particular area. However, due to their high cost, they often cover a smaller area relative to PB data, and are usually not representative of the geographic range of a species. In contrast, PB data is plentiful, covers a larger area, but is less reliable due to the lack of information on species absences, and is usually characterised by biased sampling. Here we present a new approach for species distribution modelling that integrates these two data types. The Integrated SDM's performance was evaluated using both simulated and real data, and compared to approaches using PB or SO data alone. It was found to be superior, improving the predictions of species spatial distributions, even when SO data is sparse and collected in a limited area.