ASTIN Bulletin: The Journal of the IAA最新文献

Impact of correlation between interest rates and mortality rates on the valuation of various life insurance products 利率与死亡率之间的相关性对各种人寿保险产品估值的影响

ASTIN Bulletin: The Journal of the IAA Pub Date : 2024-09-09 DOI: 10.1017/asb.2024.20

Griselda Deelstra, Pierre Devolder, Benjamin Roelants du Vivier

{"title":"Impact of correlation between interest rates and mortality rates on the valuation of various life insurance products","authors":"Griselda Deelstra, Pierre Devolder, Benjamin Roelants du Vivier","doi":"10.1017/asb.2024.20","DOIUrl":"https://doi.org/10.1017/asb.2024.20","url":null,"abstract":"In this paper, we question the traditional independence assumption between mortality risk and financial risk and model the correlation between these two risks, estimating its impact on the price of different life insurance products. The interest rate and the mortality intensity are modelled as two correlated Hull and White models in an affine set-up. We introduce two building blocks, namely the zero-coupon survival bond and the mortality density, calculate them in closed form and perform an investigation about their dependence on the correlation between mortality and financial risk, both with theoretical results and numerical analysis. We study the impact of correlation also for more structured insurance products, such as pure endowment, annuity, term insurance, whole life insurance and mixed endowment. We show that in some cases, the inclusion of correlation can lead to a severe underestimation or overestimation of the best estimate. Finally, we illustrate that the results obtained using a traditional affine diffusive set-up can be generalised to affine jump diffusion by computing the price of the zero-coupon survival bond in the presence of jumps.","PeriodicalId":501189,"journal":{"name":"ASTIN Bulletin: The Journal of the IAA","volume":"113 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180786","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

Generic framework for a coherent integration of experience and exposure rating in reinsurance 再保险中经验与风险评级统一整合的通用框架

ASTIN Bulletin: The Journal of the IAA Pub Date : 2024-08-27 DOI: 10.1017/asb.2024.17

Stefan Bernegger

{"title":"Generic framework for a coherent integration of experience and exposure rating in reinsurance","authors":"Stefan Bernegger","doi":"10.1017/asb.2024.17","DOIUrl":"https://doi.org/10.1017/asb.2024.17","url":null,"abstract":"This article introduces a comprehensive framework that effectively combines experience rating and exposure rating approaches in reinsurance for both short-tail and long-tail businesses. The generic framework applies to all nonlife lines of business and products emphasizing nonproportional treaty business. The approach is based on three pillars that enable a coherent usage of all available information. The first pillar comprises an exposure-based generative model that emulates the generative process leading to the observed claims experience. The second pillar encompasses a standardized reduction procedure that maps each high-dimensional claim object to a few weakly coupled reduced random variables. The third pillar comprises calibrating the generative model with retrospective Bayesian inference. The derived calibration parameters are fed back into the generative model, and the reinsurance contracts covering future cover periods are rated by projecting the calibrated generative model to the cover period and applying the future contract terms.","PeriodicalId":501189,"journal":{"name":"ASTIN Bulletin: The Journal of the IAA","volume":"23 11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180787","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

Strategic underreporting and optimal deductible insurance 战略性少报和最佳免赔额保险

ASTIN Bulletin: The Journal of the IAA Pub Date : 2024-04-18 DOI: 10.1017/asb.2024.14

Jingyi Cao, Dongchen Li, Virginia R. Young, Bin Zou

{"title":"Strategic underreporting and optimal deductible insurance","authors":"Jingyi Cao, Dongchen Li, Virginia R. Young, Bin Zou","doi":"10.1017/asb.2024.14","DOIUrl":"https://doi.org/10.1017/asb.2024.14","url":null,"abstract":"<p>This paper proposes a theoretical insurance model to explain well-documented loss underreporting and to study how strategic underreporting affects insurance demand. We consider a utility-maximizing insured who purchases a deductible insurance contract and follows a barrier strategy to decide whether she should report a loss. The insurer adopts a bonus-malus system with two rate classes, and the insured will move to or stay in the more expensive class if she reports a loss. First, we fix the insurance contract (deductibles) and obtain the equilibrium reporting strategy in semi-closed form. A key result is that the equilibrium barriers in both rate classes are strictly greater than the corresponding deductibles, provided that the insured economically prefers the less expensive rate class, thereby offering a theoretical explanation to underreporting. Second, we study an optimal deductible insurance problem in which the insured strategically underreports losses to maximize her utility. We find that the equilibrium deductibles are strictly positive, suggesting that full insurance, often assumed in related literature, is <span>not</span> optimal. Moreover, in equilibrium, the insured underreports a positive amount of her loss. Finally, we examine how underreporting affects the insurer’s expected profit.</p>","PeriodicalId":501189,"journal":{"name":"ASTIN Bulletin: The Journal of the IAA","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140609597","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

Multidimensional credibility: A new approach based on joint distribution function 多维可信度：基于联合分布函数的新方法

ASTIN Bulletin: The Journal of the IAA Pub Date : 2024-04-12 DOI: 10.1017/asb.2024.13

Limin Wen, Wei Liu, Yiying Zhang

{"title":"Multidimensional credibility: A new approach based on joint distribution function","authors":"Limin Wen, Wei Liu, Yiying Zhang","doi":"10.1017/asb.2024.13","DOIUrl":"https://doi.org/10.1017/asb.2024.13","url":null,"abstract":"In the traditional multidimensional credibility models developed by Jewell ((1973) Operations Research Center, pp. 73–77.), the estimation of the hypothetical mean vector involves complex matrix manipulations, which can be challenging to implement in practice. Additionally, the estimation of hyperparameters becomes even more difficult in high-dimensional risk variable scenarios. To address these issues, this paper proposes a new multidimensional credibility model based on the conditional joint distribution function for predicting future premiums. First, we develop an estimator of the joint distribution function of a vector of claims using linear combinations of indicator functions based on past observations. By minimizing the integral of the expected quadratic distance function between the proposed estimator and the true joint distribution function, we obtain the optimal linear Bayesian estimator of the joint distribution function. Using the plug-in method, we obtain an explicit formula for the multidimensional credibility estimator of the hypothetical mean vector. In contrast to the traditional multidimensional credibility approach, our newly proposed estimator does not involve a matrix as the credibility factor, but rather a scalar. This scalar is composed of both population information and sample information, and it still maintains the essential property of increasingness with respect to the sample size. Furthermore, the new estimator based on the joint distribution function can be naturally extended and applied to estimate the process covariance matrix and risk premiums under various premium principles. We further illustrate the performance of the new estimator by comparing it with the traditional multidimensional credibility model using bivariate exponential-gamma and multivariate normal distributions. Finally, we present two real examples to demonstrate the findings of our study.","PeriodicalId":501189,"journal":{"name":"ASTIN Bulletin: The Journal of the IAA","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140567128","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

Machine Learning with High-Cardinality Categorical Features in Actuarial Applications 精算应用中的高心率分类特征机器学习

ASTIN Bulletin: The Journal of the IAA Pub Date : 2024-04-11 DOI: 10.1017/asb.2024.7

Benjamin Avanzi, Greg Taylor, Melantha Wang, Bernard Wong

{"title":"Machine Learning with High-Cardinality Categorical Features in Actuarial Applications","authors":"Benjamin Avanzi, Greg Taylor, Melantha Wang, Bernard Wong","doi":"10.1017/asb.2024.7","DOIUrl":"https://doi.org/10.1017/asb.2024.7","url":null,"abstract":"High-cardinality categorical features are pervasive in actuarial data (e.g., occupation in commercial property insurance). Standard categorical encoding methods like one-hot encoding are inadequate in these settings.In this work, we present a novel Generalised Linear Mixed Model Neural Network (“GLMMNet”) approach to the modelling of high-cardinality categorical features. The GLMMNet integrates a generalised linear mixed model in a deep learning framework, offering the predictive power of neural networks and the transparency of random effects estimates, the latter of which cannot be obtained from the entity embedding models. Further, its flexibility to deal with any distribution in the exponential dispersion (ED) family makes it widely applicable to many actuarial contexts and beyond. In order to facilitate the application of GLMMNet to large datasets, we use variational inference to estimate its parameters—both traditional mean field and versions utilising textual information underlying the high-cardinality categorical features.We illustrate and compare the GLMMNet against existing approaches in a range of simulation experiments as well as in a real-life insurance case study. A notable feature for both our simulation experiment and the real-life case study is a comparatively low signal-to-noise ratio, which is a feature common in actuarial applications. We find that the GLMMNet often outperforms or at least performs comparably with an entity-embedded neural network in these settings, while providing the additional benefit of transparency, which is particularly valuable in practical applications.Importantly, while our model was motivated by actuarial applications, it can have wider applicability. The GLMMNet would suit any applications that involve high-cardinality categorical variables and where the response cannot be sufficiently modelled by a Gaussian distribution, especially where the inherent noisiness of the data is relatively high.","PeriodicalId":501189,"journal":{"name":"ASTIN Bulletin: The Journal of the IAA","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140567308","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

Signature-based validation of real-world economic scenarios 基于签名的真实世界经济情景验证

ASTIN Bulletin: The Journal of the IAA Pub Date : 2024-04-04 DOI: 10.1017/asb.2024.12

Hervé Andrès, Alexandre Boumezoued, Benjamin Jourdain

引用次数: 0

Mack’s estimator motivated by large exposure asymptotics in a compound poisson setting 复合泊松背景下以大暴露渐近为动机的马克估计器

ASTIN Bulletin: The Journal of the IAA Pub Date : 2024-03-25 DOI: 10.1017/asb.2024.11

Nils Engler, Filip Lindskog

引用次数: 0

A representation-learning approach for insurance pricing with images 利用图像进行保险定价的表征学习方法

ASTIN Bulletin: The Journal of the IAA Pub Date : 2024-03-15 DOI: 10.1017/asb.2024.9

Christopher Blier-Wong, Luc Lamontagne, Etienne Marceau

引用次数: 0

A Markov multiple state model for epidemic and insurance modelling 用于流行病和保险建模的马尔可夫多状态模型

ASTIN Bulletin: The Journal of the IAA Pub Date : 2024-03-14 DOI: 10.1017/asb.2024.8

Minh-Hoang Tran

引用次数: 0

Integration of traditional and telematics data for efficient insurance claims prediction 整合传统数据和远程信息处理技术数据，实现高效的保险理赔预测

ASTIN Bulletin: The Journal of the IAA Pub Date : 2024-02-15 DOI: 10.1017/asb.2024.6

Hashan Peiris, Himchan Jeong, Jae-Kwang Kim, Hangsuck Lee

引用次数: 0