报告题目一：Application of Population Sampling to the Valuation of Large Heterogeneous Insurance Portfolios
摘要：Insurance policies are mostly individual specific, as policyholders possess different risk characteristics and choose different policy options to meet their needs. Thus, the probability distributions that underlie the insurance claims are policy specific and complex, as opposed to the common parametric distributions. As a result, calculating the quantities of interest (e.g. premiums, liabilities, etc.) can only be done via stochastic simulations individually. Moreover, in reality an insurance portfolio contains hundreds of thousands of policies. Hence, we are dealing with a very large and highly heterogeneous insurance portfolio. To obtain any quantity of interest via simulation across the entire portfolio is extremely time consuming and is very often prohibitive. In this talk, we present an effective simulation scheme that only involves the simulation of a very small number of the policies in the portfolio. The scheme involves the use of a population sampling technique so that the entire portfolio is well represented by a very small number of (representative) policies. A tailor-made simulation algorithm is applied to those representative policies. We then identify a policy specific quantity or a summary statistic that describes the information from a policy (policy attributes and claims) and use a surrogate model to link the quantity/statistic to the quantity of interest we intend to calculate. The surrogate model is estimated using the representative policies and is used for extrapolation (to calculate the quantity for the rest of the policies). We illustrate this simulation scheme with two very different examples. One is the valuation and hedging of a variable annuity (VA) portfolio and the other is on the calculation of the Bayesian premiums of an auto insurance portfolio.
报告人：X. Sheldon Lin
X. Sheldon Lin, ASA, ACIA, is a Professor of Actuarial Science at the University of Toronto and serves as an Editor for Insurance: Mathematics and Economics. His recent research is on data-driven nonlinear regression modelling for insurance ratemaking and risk management of large insurance portfolios. The research aims to develop new and implementable methodology and technologies for insurance.
报告题目二：Mitigating Financial Impact of Pandemics: A Collaborative Public-Private Pandemic Bond Approach
摘要：This talk introduces a novel pandemic bond, a result of public-private sector partnership, designed to mitigate the economic impact of pandemics on life insurers and other institutions vulnerable to pandemic risks. The bond, issued by the private sector, bases its payoff on pandemic data released by public institutions, such as the World Health Organization. We propose a new stochastic Susceptible-Infected-Recovered-Deceased (SIRD) model to estimate pandemic transmission processes, applying it to US Covid-19 data and simulating future pandemic scenarios. Our numerical analyses indicate that the stochastic SIRD model reliably estimates excess death rates and that the proposed pandemic bond serves as a valuable instrument for managing the financial consequences of pandemics on life insurers.
李泓，加拿大圭尔夫大学Gordon S. Lang商学院教授，北美精算师，主要研究方向为保险数据分析与风险管理（包括长寿风险，气候风险，金融风险等）。他在世界一流精算、金融与经济期刊，例如Journal of Risk and Insurance, Insurance: Economics and Mathematics, Journal of Banking and Finance, Journal of Economic Behavior and Organization以及Demography上发表超过20篇论文。
邀 请 人：池义春