未决赔款准备金评估的随机性Munich链梯法

精算实务界通常采用链梯法等确定性方法评估未决赔款准备金,这些评估方法存在一定缺陷,一方面不能有效考虑保险公司历史数据中所包含的已决赔款和已报案赔款数据信息,另一方面只能得到未决赔款准备金的均值估计,不能度量不确定性。为了克服这些缺陷,本文结合Mack模型假设和非参数Bootstrap重抽样方法,提出了未决赔款准备金评估的随机性Munich链梯法,并应用R软件对精算实务中的实例给出了数值分析。     

时变多元NBS Copula模型与美国股指期货市场可视化相依性分析

基于多元NBS(Normal Birnbaum-Saunders)分布构造了一种新的多元偏斜厚尾Copula,即多元NBS Copula,并进一步采用DCC(Dynamic Conditional Correlation)模型构造了时变NBS Copula模型。以美国道琼斯30指数期货、标准普尔500指数期货和纳斯达克100指数期货为例，可视化分析了收益率序列之间的各种相依特征，比较了DCC-NBS Copula模型与其他一些Copula模型在相依结构拟合上的效果差异。实证结果表明：美国三大股指期货之间的相依结构具有正相依性、厚尾相依性、非对称相依性和时变相依性，其中，NAGARCH模型可以较好地描述收益率序列的动态特征，椭圆Copula优于阿基米德Copula,非对称椭圆Copula优于对称椭圆Copula,厚尾椭圆Copula优于正态Copula,时变椭圆Copula优于静态椭圆Copula。综合来看，DCC-NBSCopula模型是所有模型中对相依结构的拟合效果最优的。     

基于藤Copula的多资产交换期权模拟定价

传统的多维Copula是用单个参数来度量多变量之间的相依关系,这限制了该类Copula在描述多变量之间相依结构.为了解决这一问题,提出了一种使用藤构造三维Copula的算法,用蒙特卡罗方法分别模拟传统的单参数三维Copula和藤构造的三维Copula,并给三资产的交换期权定价,发现藤构造的Copula在定价上与单参数多维Copula存在明显的差别,使用藤构造的Copula在描述相依结构时有较大弹性.     

未决赔款准备金评估的对数正态模型及其预测分布的Bootstrap实现

目前在我国精算实务中对未决赔款准备金评估的不确定性风险逐渐重视,对不确定性加以度量显得很有必要.在以往关于未决赔款准备金的不确定性研究中,大多集中于预测均方误差.从数值角度看,如果应用随机模拟的方法,能得到未决赔款准备金完整的预测分布,那么就可以由该分布得到各个分位数以及相关的分布度量,对准备金负债评估的准确性和充足性具有重要的参考价值.研究的对数正态模型是未决赔款准备金评估中的分布模型之一,它假设累计赔款单个进展因子服从对数正态分布,进而将参数Bootstrap方法和非参数Bootstrap方法应用于对数正态模型中,得到了未决赔款准备金的预测分布,并通过精算实务中的数值实例加以实证分析.数值实例由当前国际上日益流行的统计软件R加以实现.     

相依风险条件下的医疗费用预测模型及其应用

在医疗费用的预测中,通常假设门诊费用与住院费用相互独立,事实上它们之间往往存在一定的相依关系。它们的相依性可以通过Copula函数来描述。在已知医疗费用发生的条件下,假设门诊费用和住院费用分别服从伽玛分布、逆高斯分布、对数正态分布和GB2分布,可以建立门诊费用与住院费用相互依赖的Copula回归模型。本文将此模型应用于一组实际的医疗费用数据,结果表明,考虑相依关系的回归模型要优于独立假设下的医疗费用预测模型。     

基于极值统计和高维动态C藤Copula的股市行业集成风险计算

股市诸多行业风险之间存在着波动相依性,集成计量多维风险对投资决策意义重大。藤Copula是Copula函数高维化拓展的一个方向,其动态化是新的研究前沿。将极值理论的GPD模型和高维动态C藤Copula方法结合起来研究沪深300指数中地产、基建、银行和运输四个行业风险,能够有效描述尾部极值形态,突出关键变量的作用。再运用动态Pair-Copula分解,刻画高维行业风险变量间的动态关系,以仿真出动态集成风险变量VaR序列。VaR计算结果通过了回溯检验和稳定性测试,表明高维动态C藤Copula模型可以作为风险集成计量的一种新的有效方法。     

基于三维Copula函数的金融指数相关性分析

给出了三维Copula函数模型中未知参数的估计方法及最优三维Copula函数的选择方法,此构造方法对研究多变量之间的相依性提供了新途径.通过对上证指数、深圳成指及创业板指的历史数据进行实证分析,选出了最优三维Copula函数以描述三者之间的相关性,并分析三者之间的尾部相关性.     

基于广义线性模型的个体索赔RBNS准备金评估

基于个体索赔模型对准备金的评估已成为准备金评估研究的重要内容.本文基于广义线性模型,对个体索赔额及索赔数目建立责任准备金模型,给出未决赔款责任准备金的期望及方差.进而,根据样本数据对未知参数求解极大似然估计,并讨论了估计的强相合性和渐近正态性.并得到责任准备金的估计及其预测均方误差.最后,通过数值模拟的方法将本文得到的估计与链梯法进行比较,结果显示我们的估计明显优于链梯法估计.     

基于Copula模型的尾部相依性长记忆效应研究

在已有动态Copula模型基础上,提出可同时描述尾部相依性的非对称和长记忆特征的Copula模型.基于沪深股市数据,首次从尾部相依性的角度检验了沪深股市的长记忆效应.研究发现,沪深两市在重大利好或利空消息冲击时的相关性(即尾部相依性)都具有长记忆效应,极端事件对尾部相依性的影响比对未来收益和波动的影响更加持久.而且,样本外分析结果表明,相比已有Copula模型,具有长记忆性的Copula模型能更准确地预测未来1周至1年的市场间相关性.     

混合Copula模型在中国股市的应用

首先给出了描述相依结构的混合Copula模型,然后给出寻求混合Copula模型的EM算法,最后以中国股市的实际数据进行了实证分析,说明混合Copula模型是可以用来描述中国股市的相依结构.     

基于藤结构 Copula 模型的中国股市风格资产相依结构研究

首先采用AR（1）-GJR（1,1）-SkT（73,A）模型来刻画中国股市风格资产（大盘成长、大盘价值、中盘成长、中盘价值、小盘成长、小盘价值）的边缘分布,接着结合各边缘分布的残差,引入C—VineCop—ula和r）IVineCopula模型来描述这六种风格资产之间的相依结构,然后对基于CVineCopula和I）IVineCopula模型的拟合效果进行综合比较．研究结果表明：中国股市各风格资产之间的相依性存在结构性差异,最适合用I）IVineCopula模型来刻画各风格资产之间的相依结构．同类型的风格资产之间的相依程度比不同类型风格资产之间的相依程度要高;在同一类型的风格资产中,资产规模差距越大的风格资产之间的相依系数就越小．无条件的风格资产收益系列之间的相关性要显著大于有条件的风格资产收益系列之间的相关性;最后根据研究结论提出了降低风格资产组合风险的资产配置建议．     

GAMLSS模型在车险定价中的应用

在系统梳理国内外非寿险产品费率厘定方法的基础上,详细介绍了GAMLSS模型,证明了在位置参数和尺度参数的预测中均引入随机效应的GAMLSS模型可更有效地解释纵向数据中个体间的异质性.最后将GAMLSS模型应用于一组纵向车辆保险数据,计算了先验保费、后验保费、后验风险保费和奖惩因子.实证结果表明,GAMLSS模型不仅可为非寿险产品的定价提供依据,而且使风险分类更加稳定、合理.     

藤Copula模型与多资产投资组合VaR预测

投资组合风险管理往往涉及多个资产,在传统的二元Copula函数面临"维度诅咒"问题及多元Copula函数刻画多变量联合分布时其精确性和灵活性存在各种局限性的情况下,引入藤Copula刻画多个资产收益的联合分布,基于不同的Pair-Copula类别构建藤Copula,运用蒙特卡罗模拟方法计算多资产投资组合的VaR,通过Kupiec和Christoffersen返回检验方法测试藤Copula模型的VaR预测效果,并与传统方差-协方差风险管理方法做比较。实证分析表明,传统的方差-协方差风险管理方法和基于正态Pair-Copula作为藤Copula构建模块的方法不能通过多资产投资组合的VaR预测返回检验;而基于student-t Copula、Clayton Copula具有尾部分布特征的Copula作为构建模块的藤Copula模型能够有效地用于多资产投资组合VaR预测,从而更好的用于指导实践。     

Assessing inflation risk in non-life insurance

Inflation risk is of high relevance in non-life insurers’ long-tail business and can have a major impact on claims reserving. In this paper, we empirically study claims inflation with focus on automobile liability insurance based on a data set provided by a large German non-life insurance company. The aim is to obtain empirical insight regarding the drivers of claims inflation risk and its impact on reserving. Toward this end, we use stepwise multiple regression analysis to identify relevant drivers based on economic indices related to health costs and consumer prices, amongst others. We further study the impact of (implicitly and explicitly) predicting calendar year inflation effects on claims reserves using stochastic inflation models. Our results show that drivers for claims inflation can considerably vary for different lines of business and emphasize the importance of explicitly dealing with (stochastic) claims inflation when calculating reserves.     

Severity modeling of extreme insurance claims for tariffication

Generalized linear models are common instruments for the pricing of non-life insurance contracts. They are used to estimate the expected frequency and severity of insurance claims. However, these models do not work adequately for extreme claim sizes. To accommodate for these extreme claim sizes, we develop the threshold severity model, that splits the claim size distribution in areas below and above a given threshold. More specifically, the extreme insurance claims above the threshold are modeled in the sense of the peaks-over-threshold methodology from extreme value theory using the generalized Pareto distribution for the excess distribution, and the claims below the threshold are captured by a generalized linear model based on the truncated gamma distribution. Subsequently, we develop the corresponding concrete log-likelihood functions above and below the threshold. Moreover, in the presence of simulated extreme claim sizes following a log-normal as well as Burr Type XII distribution, we demonstrate the superiority of the threshold severity model compared to the commonly used generalized linear model based on the gamma distribution.     

On the recursive evaluation of a certain multivariate compound distribution

In this paper, we extend to a multivariate setting the bivariate model A introduced by Jin and Ren in 2014 (Recursions and fast Fourier transforms for a new bivariate aggregate claims model, Scandinavian Actuarial Journal 8) to model insurance aggregate claims in the case when different types of claims simultaneously affect an insurance portfolio. We obtain an exact recursive formula for the probability function of the multivariate compound distribution corresponding to this model under the assumption that the conditional multivariate counting distribution (conditioned by the total number of claims) is multinomial. Our formula extends the corresponding one from Jin and Ren.     

An optimization approach to adaptive multi-dimensional capital management

Firms should keep capital to offer sufficient protection against the risks they are facing. In the insurance context methods have been developed to determine the minimum capital level required, but less so in the context of firms with multiple business lines including allocation. The individual capital reserve of each line can be represented by means of classical models, such as the conventional Cramér–Lundberg model, but the challenge lies in soundly modelling the correlations between the business lines. We propose a simple yet versatile approach that allows for dependence by introducing a common environmental factor. We present a novel Bayesian approach to calibrate the latent environmental state distribution based on observations concerning the claim processes. The calibration approach is adjusted for an environmental factor that changes over time. The convergence of the calibration procedure towards the true environmental state is deduced. We then point out how to determine the optimal initial capital of the different business lines under specific constraints on the ruin probability of subsets of business lines. Upon combining the above findings, we have developed an easy-to-implement approach to capital risk management in a multi-dimensional insurance risk model.     

Optimal control and dependence modeling of insurance portfolios with Lévy dynamics

In this paper we are interested in optimizing proportional reinsurance and investment policies in a multidimensional Lévy-driven insurance model. The criterion is that of maximizing exponential utility. Solving the classical Hamilton-Jacobi-Bellman equation yields that the optimal retention level keeps a constant amount of claims regardless of time and the company’s wealth level.A special feature of our construction is to allow for dependencies of the risk reserves in different business lines. Dependence is modeled via an Archimedean Lévy copula. We derive a sufficient and necessary condition for an Archimedean Lévy generator to create a multidimensional positive Lévy copula in arbitrary dimension.Based on these results we identify structure conditions for the generator and the Lévy measure of an Archimedean Lévy copula under which an insurance company reinsures a larger fraction of claims from one business line than from another.     

自回归相依结构下累积索赔的矩及应用

在不同时期的索赔具有自回归相依结构的条件下,给出了累积索赔的前两阶矩,并进一步讨论了在保费计算中的应用.数值算例揭示了相依参数对风险矩以及保险费的影响,表明了保险公司适时开发新业务、淘汰旧业务的必要性.     

Uniform asymptotics for the ruin probabilities in a bidimensional renewal risk model with strongly subexponential claims

This paper considers a bidimensional continuous-time renewal risk model of insurance business with different claim-number processes and strongly subexponential claims. For the finite-time ruin probability defined as the probability for the aggregate surplus process to break down the horizontal line at the level zero within a given time, an uniform asymptotic formula is established, which provides new insights into the solvency ability of the insurance company.