Annals of Actuarial Science最新文献_第10页

Evaluation of equity-linked products in the presence of policyholder surrender option using risk-control strategies 使用风险控制策略评估存在保单持有人放弃期权的股票挂钩产品

IF 1.7

Annals of Actuarial Science Pub Date : 2021-03-08 DOI: 10.1017/S1748499521000051

Patrice Gaillardetz, S. Hachem, Mehran Moghtadai

引用次数: 1

Extracting information from textual descriptions for actuarial applications 从精算应用程序的文本描述中提取信息

IF 1.7

Annals of Actuarial Science Pub Date : 2021-03-02 DOI: 10.1017/S1748499521000026

S. Manski, Kaixu Yang, Gee Y. Lee, T. Maiti

{"title":"Extracting information from textual descriptions for actuarial applications","authors":"S. Manski, Kaixu Yang, Gee Y. Lee, T. Maiti","doi":"10.1017/S1748499521000026","DOIUrl":"https://doi.org/10.1017/S1748499521000026","url":null,"abstract":"Abstract Initial insurance losses are often reported with a textual description of the claim. The claims manager must determine the adequate case reserve for each known claim. In this paper, we present a framework for predicting the amount of loss given a textual description of the claim using a large number of words found in the descriptions. Prior work has focused on classifying insurance claims based on keywords selected by a human expert, whereas in this paper the focus is on loss amount prediction with automatic word selection. In order to transform words into numeric vectors, we use word cosine similarities and word embedding matrices. When we consider all unique words found in the training dataset and impose a generalised additive model to the resulting explanatory variables, the resulting design matrix is high dimensional. For this reason, we use a group lasso penalty to reduce the number of coefficients in the model. The scalable, analytical framework proposed provides for a parsimonious and interpretable model. Finally, we discuss the implications of the analysis, including how the framework may be used by an insurance company and how the interpretation of the covariates can lead to significant policy change. The code can be found in the TAGAM R package (github.com/scottmanski/TAGAM).","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S1748499521000026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45183496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Modelling random vectors of dependent risks with different elliptical components 具有不同椭圆分量的依赖风险随机向量建模

IF 1.7

Annals of Actuarial Science Pub Date : 2021-02-22 DOI: 10.1017/S1748499521000038

Z. Landsman, T. Shushi

{"title":"Modelling random vectors of dependent risks with different elliptical components","authors":"Z. Landsman, T. Shushi","doi":"10.1017/S1748499521000038","DOIUrl":"https://doi.org/10.1017/S1748499521000038","url":null,"abstract":"Abstract In Finance and Actuarial Science, the multivariate elliptical family of distributions is a famous and well-used model for continuous risks. However, it has an essential shortcoming: all its univariate marginal distributions are the same, up to location and scale transformations. For example, all marginals of the multivariate Student’s t-distribution, an important member of the elliptical family, have the same number of degrees of freedom. We introduce a new approach to generate a multivariate distribution whose marginals are elliptical random variables, while in general, each of the risks has different elliptical distribution, which is important when dealing with insurance and financial data. The proposal is an alternative to the elliptical copula distribution where, in many cases, it is very difficult to calculate its risk measures and risk capital allocation. We study the main characteristics of the proposed model: characteristic and density functions, expectations, covariance matrices and expectation of the linear regression vector. We calculate important risk measures for the introduced distributions, such as the value at risk and tail value at risk, and the risk capital allocation of the aggregated risks.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S1748499521000038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47168492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Functional disability with systematic trends and uncertainty: a comparison between China and the US 具有系统趋势和不确定性的功能性残疾：中美比较

IF 1.7

Annals of Actuarial Science Pub Date : 2021-02-15 DOI: 10.2139/SSRN.3785743

Yu-Hsiang Fu, M. Sherris, Mengyi Xu

{"title":"Functional disability with systematic trends and uncertainty: a comparison between China and the US","authors":"Yu-Hsiang Fu, M. Sherris, Mengyi Xu","doi":"10.2139/SSRN.3785743","DOIUrl":"https://doi.org/10.2139/SSRN.3785743","url":null,"abstract":"Abstract China and the US are two contrasting countries in terms of functional disability and long-term care. China is experiencing declining family support for long-term care and developing private long-term care insurance. The US has a more developed public aged care system and private long-term care insurance market than China. Changes in the demand for long-term care are driven by the levels, trends and uncertainty in mortality and functional disability. To understand the future potential demand for long-term care, we compare mortality and functional disability experiences in China and the US, using a multi-state latent factor intensity model with time trends and systematic uncertainty in transition rates. We estimate the model with the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and the US Health and Retirement Study (HRS) data. The estimation results show that if trends continue, both countries will experience longevity improvement with morbidity compression and a declining proportion of the older population with functional disability. Although the elderly Chinese have a shorter estimated life expectancy, they are expected to spend a smaller proportion of their future lifetime functionally disabled than the elderly Americans. Systematic uncertainty is shown to be significant in future trends in disability rates and our model estimates higher uncertainty in trends for the Chinese elderly, especially for urban residents.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42904368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Mortality models incorporating long memory for life table estimation: a comprehensive analysis 结合长记忆的寿命表估计死亡率模型:综合分析

IF 1.7

Annals of Actuarial Science Pub Date : 2021-02-02 DOI: 10.1017/S1748499521000014

Hongxuan Yan, G. Peters, J. Chan

{"title":"Mortality models incorporating long memory for life table estimation: a comprehensive analysis","authors":"Hongxuan Yan, G. Peters, J. Chan","doi":"10.1017/S1748499521000014","DOIUrl":"https://doi.org/10.1017/S1748499521000014","url":null,"abstract":"Abstract Mortality projection and forecasting of life expectancy are two important aspects of the study of demography and life insurance modelling. We demonstrate in this work the existence of long memory in mortality data. Furthermore, models incorporating long memory structure provide a new approach to enhance mortality forecasts in terms of accuracy and reliability, which can improve the understanding of mortality. Novel mortality models are developed by extending the Lee–Carter (LC) model for death counts to incorporate a long memory time series structure. To link our extensions to existing actuarial work, we detail the relationship between the classical models of death counts developed under a Generalised Linear Model (GLM) formulation and the extensions we propose that are developed under an extension to the GLM framework known in time series literature as the Generalised Linear Autoregressive Moving Average (GLARMA) regression models. Bayesian inference is applied to estimate the model parameters. The Deviance Information Criterion (DIC) is evaluated to select between different LC model extensions of our proposed models in terms of both in-sample fits and out-of-sample forecasts performance. Furthermore, we compare our new models against existing models structures proposed in the literature when applied to the analysis of death count data sets from 16 countries divided according to genders and age groups. Estimates of mortality rates are applied to calculate life expectancies when constructing life tables. By comparing different life expectancy estimates, results show the LC model without the long memory component may provide underestimates of life expectancy, while the long memory model structure extensions reduce this effect. In summary, it is valuable to investigate how the long memory feature in mortality influences life expectancies in the construction of life tables.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S1748499521000014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47228434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Dynamic importance allocated nested simulation for variable annuity risk measurement 可变年金风险度量的动态重要性分配嵌套模拟

IF 1.7

Annals of Actuarial Science Pub Date : 2020-11-27 DOI: 10.2139/ssrn.3738777

Ou Dang, M. Feng, M. Hardy

{"title":"Dynamic importance allocated nested simulation for variable annuity risk measurement","authors":"Ou Dang, M. Feng, M. Hardy","doi":"10.2139/ssrn.3738777","DOIUrl":"https://doi.org/10.2139/ssrn.3738777","url":null,"abstract":"Abstract Estimating tail risk measures for portfolios of complex variable annuities is an important enterprise risk management task which usually requires nested simulation. In the nested simulation, the outer simulation stage involves projecting scenarios of key risk factors under the real-world measure, while the inner simulations are used to value pay-offs under guarantees of varying complexity, under a risk-neutral measure. In this paper, we propose and analyse an efficient simulation approach that dynamically allocates the inner simulations to the specific outer scenarios that are most likely to generate larger losses. These scenarios are identified using a proxy calculation that is used only to rank the outer scenarios, not to estimate the tail risk measure directly. As the proxy ranking will not generally provide a perfect match to the true ranking of outer scenarios, we calculate a measure based on the concomitant of order statistics to test whether further tail scenarios are required to ensure, with given confidence, that the true tail scenarios are captured. This procedure, which we call the dynamic importance allocated nested simulation approach, automatically adjusts for the relationship between the proxy calculations and the true valuations and also signals when the proxy is not sufficiently accurate.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2020-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48116453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A spatial machine learning model for analysing customers’ lapse behaviour in life insurance 用于分析寿险客户失效行为的空间机器学习模型

IF 1.7

Annals of Actuarial Science Pub Date : 2020-11-10 DOI: 10.1017/S1748499520000329

Sen Hu, A. O'Hagan, James Sweeney, Mohammadhossein Ghahramani

{"title":"A spatial machine learning model for analysing customers’ lapse behaviour in life insurance","authors":"Sen Hu, A. O'Hagan, James Sweeney, Mohammadhossein Ghahramani","doi":"10.1017/S1748499520000329","DOIUrl":"https://doi.org/10.1017/S1748499520000329","url":null,"abstract":"Abstract Spatial analysis ranges from simple univariate descriptive statistics to complex multivariate analyses and is typically used to investigate spatial patterns or to identify spatially linked consumer behaviours in insurance. This paper investigates if the incorporation of publicly available spatially linked demographic census data at population level is useful in modelling customers’ lapse behaviour (i.e. stopping payment of premiums) in life insurance policies, based on data provided by an insurance company in Ireland. From the insurance company’s perspective, identifying and assessing such lapsing risks in advance permit engagement to prevent such incidents, saving money by re-evaluating customer acquisition channels and improving capital reserve calculation and preparation. Incorporating spatial analysis in lapse modelling is expected to improve lapse prediction. Therefore, a hybrid approach to lapse prediction is proposed – spatial clustering using census data is used to reveal the underlying spatial structure of customers of the Irish life insurer, in conjunction with traditional statistical models for lapse prediction based on the company data. The primary contribution of this work is to consider the spatial characteristics of customers for life insurance lapse behaviour, via the integration of reliable government provided census demographics, which has not been considered previously in actuarial literature. Company decision-makers can use the insights gleaned from this analysis to identify customer subsets to target with personalized promotions to reduce lapse rates, and to reduce overall company risk.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S1748499520000329","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47046648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Clustering driving styles via image processing 基于图像处理的驾驶风格聚类

IF 1.7

Annals of Actuarial Science Pub Date : 2020-10-27 DOI: 10.1017/S1748499520000317

Rui Zhu, M. Wüthrich

引用次数: 5

Imprecise credibility theory 不精确可信度理论

IF 1.7

Annals of Actuarial Science Pub Date : 2020-10-23 DOI: 10.1017/S1748499521000117

Liang Hong, Ryan Martin

引用次数: 0

Mortality forecasting using a Lexis-based state-space model 使用基于Lexis的状态空间模型进行死亡率预测

IF 1.7

Annals of Actuarial Science Pub Date : 2020-09-11 DOI: 10.1017/S1748499520000275

Patrik Andersson, M. Lindholm

引用次数: 2