North American Actuarial Journal最新文献_第3页

Estimation, Comparison, and Projection of Multifactor Age–Cohort Affine Mortality Models 多因素年龄队列仿射死亡率模型的估计、比较和预测

North American Actuarial Journal Pub Date : 2023-10-13 DOI: 10.1080/10920277.2023.2238793

Francesco Ungolo, Len Patrick Dominic M. Garces, Michael Sherris, Yuxin Zhou

{"title":"Estimation, Comparison, and Projection of Multifactor Age–Cohort Affine Mortality Models","authors":"Francesco Ungolo, Len Patrick Dominic M. Garces, Michael Sherris, Yuxin Zhou","doi":"10.1080/10920277.2023.2238793","DOIUrl":"https://doi.org/10.1080/10920277.2023.2238793","url":null,"abstract":"Affine mortality models, developed in continuous time, are well suited to longevity risk applications including pricing and capital management. A major advantage of this mortality modeling approach is the availability of closed-form cohort survival curves, consistent with the assumed time dynamics of mortality rates. This article makes new contributions to the estimation of multifactor continuous-time affine models including the canonical Blackburn-Sherris, the arbitrage-free Nelson-Siegel (AFNS), and the Cox-Ingersoll-Ross (CIR) mortality models. We discuss and address numerical issues with model estimation. We apply the estimation methods to age–cohort mortality data from five different countries, providing insights into the dynamics of mortality rates and the fitting performance of the models. We show how the use of maximum likelihood with the univariate Kalman filter turns out to be faster and more robust compared to traditional estimation methods that heavily use large matrix multiplication and inversion. We present graphical and numerical goodness-of-fit results and assess model robustness. We project cohort survival curves and assess the out-of-sample performance of the models for the five countries. We confirm previous results by showing that, across these countries, although the CIR mortality model fits the historical mortality data well, particularly at older ages, the canonical and AFNS affine mortality models provide better out-of-sample performance. We also show how these affine mortality models are robust with respect to the set of age–cohort data used for parameter estimation. R code is provided.","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135853633","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: 0

Event Studies for Publicly Traded Insurers: An Investigation of the Bad-Model Problem 上市保险公司的事件研究——对不良模型问题的调查

IF 1.4

North American Actuarial Journal Pub Date : 2023-09-08 DOI: 10.1080/10920277.2023.2214603

Leon Chen, Steven W. Pottier

引用次数: 0

Modeling Payment Frequency for Loss Reserves Based on Dynamic Claim Scores 基于动态索赔分数的损失准备金支付频率建模

IF 1.4

North American Actuarial Journal Pub Date : 2023-08-07 DOI: 10.1080/10920277.2023.2218897

Juan Sebastian Yanez, J. Boucher, M. Pigeon

引用次数: 0

A Unified Framework for Insurance Demand and Mortality Immunization 保险需求和死亡率免疫的统一框架

IF 1.4

North American Actuarial Journal Pub Date : 2023-07-26 DOI: 10.1080/10920277.2023.2218465

Hua Chen, Jin Gao, Wei Zhu

引用次数: 0

An Asymptotic Result on Catastrophe Insurance Losses 巨灾保险损失的一个渐近结果

IF 1.4

North American Actuarial Journal Pub Date : 2023-07-26 DOI: 10.1080/10920277.2023.2216764

Yiqing Chen, Jiajun Liu

引用次数: 0

Bowley Insurance with Expected Utility Maximization of the Policyholders 投保人期望效用最大化的鲍利保险

IF 1.4

North American Actuarial Journal Pub Date : 2023-07-19 DOI: 10.1080/10920277.2023.2213295

Tim J. Boonen, Wenjun Jiang

{"title":"Bowley Insurance with Expected Utility Maximization of the Policyholders","authors":"Tim J. Boonen, Wenjun Jiang","doi":"10.1080/10920277.2023.2213295","DOIUrl":"https://doi.org/10.1080/10920277.2023.2213295","url":null,"abstract":"<p>This article studies the Bowley solution for a sequential game within the expected utility framework. We assume that the policyholders are expected utility maximizers and there exists a representative policyholder who faces a fixed loss with given probability and no loss otherwise. This policyholder selects the optimal indemnity function in response to the pricing kernel set by the insurer. Knowing the policyholder’s choice of indemnity function, the insurer adjusts the pricing kernel to maximize its expected net profit. This pricing kernel is of our central interest in this article, and in our setting the pricing kernel can be evaluated via the safety loading factor in an expected value premium principle. For a wide class of utility functions, we show that the optimal safety loading factor increases with respect to both the policyholder’s risk aversion level and the probability of zero loss. We also show that the insurance contract corresponding to the Bowley solution is Pareto dominated in the sense that both parties’ interests can be further improved, which shows the inefficiency of the Bowley solution. Some numerical examples are presented to illustrate the main results, and it is shown that both the policyholder and insurer can strictly benefit from the Bowley solution.</p>","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138519477","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: 0

GAMLSS for Longitudinal Multivariate Claim Count Models 纵向多变量索赔计数模型的GAMLSS

IF 1.4

North American Actuarial Journal Pub Date : 2023-06-15 DOI: 10.1080/10920277.2023.2202707

R. Turcotte, J. Boucher

引用次数: 2

Pricing of Joint Life Long-Term Care Insurance Based on a Multistate Markov Model 基于多状态马尔可夫模型的联合终身长期护理保险定价

IF 1.4

North American Actuarial Journal Pub Date : 2023-06-15 DOI: 10.1080/10920277.2023.2208192

Qin Shang, Xueyang Wang, Xuezhi Qin, Xiaohui Zhao

引用次数: 0

Predictability and Financial Sufficiency of Health Insurance in Colombia: An Actuarial Analysis With a Bayesian Approach 哥伦比亚医疗保险的可预测性和财务充分性：基于贝叶斯方法的精算分析

IF 1.4

North American Actuarial Journal Pub Date : 2023-06-09 DOI: 10.1080/10920277.2023.2197475

Oscar Espinosa, Valeria Bejarano, Jeferson Ramos

引用次数: 0

Antidiscrimination Insurance Pricing: Regulations, Fairness Criteria, and Models 反歧视保险定价:法规、公平标准和模型

North American Actuarial Journal Pub Date : 2023-06-08 DOI: 10.1080/10920277.2023.2190528

Xi Xin, Fei Huang

{"title":"Antidiscrimination Insurance Pricing: Regulations, Fairness Criteria, and Models","authors":"Xi Xin, Fei Huang","doi":"10.1080/10920277.2023.2190528","DOIUrl":"https://doi.org/10.1080/10920277.2023.2190528","url":null,"abstract":"On the issue of insurance discrimination, a grey area in regulation has resulted from the growing use of big data analytics by insurance companies: direct discrimination is prohibited, but indirect discrimination using proxies or more complex and opaque algorithms is not clearly specified or assessed. This phenomenon has recently attracted the attention of insurance regulators all over the world. Meanwhile, various fairness criteria have been proposed and flourished in the machine learning literature with the rapid growth of artificial intelligence (AI) in the past decade and have mostly focused on classification decisions. In this article, we introduce some fairness criteria that are potentially applicable to insurance pricing as a regression problem to the actuarial field, match them with different levels of potential and existing antidiscrimination regulations, and implement them into a series of existing and newly proposed antidiscrimination insurance pricing models, using both generalized linear models (GLMs) and Extreme Gradient Boosting (XGBoost). Our empirical analysis compares the outcome of different models via the fairness–accuracy trade-off and shows their impact on adverse selection and solidarity.","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135215217","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