Insurance Mathematics & Economics最新文献_第10页

Auto insurance fraud detection: Leveraging cost sensitive and insensitive algorithms for comprehensive analysis 汽车保险欺诈检测：利用成本敏感和不敏感算法进行综合分析

IF 1.9 2区经济学

Insurance Mathematics & Economics Pub Date : 2025-05-01 Epub Date: 2025-02-09 DOI: 10.1016/j.insmatheco.2025.02.001

Meryem Yankol Schalck

引用次数: 0

Identifying scenarios for the own risk and Solvency assessment of insurance companies 确定保险公司自身风险和偿付能力评估的情景

IF 1.9 2区经济学

Insurance Mathematics & Economics Pub Date : 2025-05-01 Epub Date: 2025-02-06 DOI: 10.1016/j.insmatheco.2025.01.009

Philipp Aigner

{"title":"Identifying scenarios for the own risk and Solvency assessment of insurance companies","authors":"Philipp Aigner","doi":"10.1016/j.insmatheco.2025.01.009","DOIUrl":"10.1016/j.insmatheco.2025.01.009","url":null,"abstract":"<div><div>Most insurers in the European Union determine their regulatory capital requirements based on the standard formula of Solvency II. However, there is evidence that the standard formula inaccurately reflects insurers' risk situation and may provide misleading steering incentives. In the second pillar, Solvency II requires insurers to perform a so-called “Own Risk and Solvency Assessment” (ORSA). In their ORSA, insurers must establish their own risk measurement approaches, including those based on scenarios, in order to derive suitable risk assessments and address shortcomings of the standard formula. The idea of this paper is to identify scenarios in such a way that the standard formula in connection with the ORSA provides a reliable basis for risk management decisions. Using an innovative method for scenario identification, our approach allows for a simple but precise assessment of marginal and even non-marginal portfolio changes. We numerically evaluate the proposed approach in the context of market risk employing an internal model from the academic literature and the Solvency Capital Requirement (SCR) calculation under Solvency II.</div></div>","PeriodicalId":54974,"journal":{"name":"Insurance Mathematics & Economics","volume":"122 ","pages":"Pages 30-43"},"PeriodicalIF":1.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143386423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Insurance contract for electric vehicle charging stations: A Stackelberg game-theoretic approach 电动汽车充电站保险合同：一个Stackelberg博弈论方法

IF 1.9 2区经济学

Insurance Mathematics & Economics Pub Date : 2025-05-01 Epub Date: 2025-02-17 DOI: 10.1016/j.insmatheco.2025.02.002

Yuanmin Jin , Zhuo Jin , Jiaqin Wei

引用次数: 0

Mean-variance optimization for participating life insurance contracts 参与式寿险合同的均值方差优化

IF 1.9 2区经济学

Insurance Mathematics & Economics Pub Date : 2025-05-01 Epub Date: 2025-03-17 DOI: 10.1016/j.insmatheco.2025.03.005

Felix Fießinger, Mitja Stadje

引用次数: 0

Length of stay in residential aged care: Patterns and determinants from a population-based cohort study 老年护理住院时间：基于人群的队列研究的模式和决定因素

IF 1.9 2区经济学

Insurance Mathematics & Economics Pub Date : 2025-05-01 Epub Date: 2025-03-24 DOI: 10.1016/j.insmatheco.2025.03.006

Mengyi Xu , Gaoyun Yan

引用次数: 0

Almost stochastic dominance: Magnitude constraints on risk aversion 几乎随机优势：风险规避的大小约束

IF 1.9 2区经济学

Insurance Mathematics & Economics Pub Date : 2025-05-01 Epub Date: 2025-02-19 DOI: 10.1016/j.insmatheco.2025.02.003

Liqun Liu , Jack Meyer

引用次数: 0

Robust indifference valuation of catastrophe bonds 巨灾债券的稳健无差异估值

IF 1.9 2区经济学

Insurance Mathematics & Economics Pub Date : 2025-05-01 Epub Date: 2025-01-30 DOI: 10.1016/j.insmatheco.2025.01.008

Haibo Liu

引用次数: 0

Self-protection under Nth-degree risk increase of random unit cost 随机单位成本n度风险增加下的自我保护

IF 1.9 2区经济学

Insurance Mathematics & Economics Pub Date : 2025-05-01 Epub Date: 2025-02-28 DOI: 10.1016/j.insmatheco.2025.02.004

Yongjin Yin, Shengwang Meng

{"title":"Self-protection under Nth-degree risk increase of random unit cost","authors":"Yongjin Yin, Shengwang Meng","doi":"10.1016/j.insmatheco.2025.02.004","DOIUrl":"10.1016/j.insmatheco.2025.02.004","url":null,"abstract":"<div><div>Cost risk, as a type of multiplicative risk, should be given more attention in decision-making issues. Crainich and Menegatti (2021) have studied the effects of introducing random unit cost in self-protection under the four standard self-protection model frameworks. They focus on the case where the unit cost of effort in self-protection changes from certainty (<em>denoted as</em> <span><math><mi>c</mi></math></span>) to randomness (<em>denoted as</em> <span><math><mover><mi>c</mi><mo>˜</mo></mover></math></span>) with <span><math><mrow><mi>E</mi><mo>[</mo><mover><mi>c</mi><mo>˜</mo></mover><mo>]</mo><mo>=</mo><mi>c</mi></mrow></math></span>, which represents second-degree risk increase in Ekern (1980). In this paper, we generalize the concept of second-degree risk increase to <em>N</em>th-degree risk increase and provide sufficient conditions for increasing or decreasing effort in self-protection, which are closely related to the parity of the order of the risk change and decision-maker's higher-order risk attitudes. We use the multiplicative effect and apportionment effect to explain the decision-maker's preference conditions.</div></div>","PeriodicalId":54974,"journal":{"name":"Insurance Mathematics & Economics","volume":"122 ","pages":"Pages 137-142"},"PeriodicalIF":1.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient evaluation of risk allocations 风险分配的有效评估

IF 1.9 2区经济学

Insurance Mathematics & Economics Pub Date : 2025-05-01 Epub Date: 2025-02-27 DOI: 10.1016/j.insmatheco.2025.02.006

Christopher Blier-Wong , Hélène Cossette , Etienne Marceau

{"title":"Efficient evaluation of risk allocations","authors":"Christopher Blier-Wong , Hélène Cossette , Etienne Marceau","doi":"10.1016/j.insmatheco.2025.02.006","DOIUrl":"10.1016/j.insmatheco.2025.02.006","url":null,"abstract":"<div><div>Expectations of marginals conditional on the total risk of a portfolio are crucial in risk-sharing and allocation. However, computing these conditional expectations may be challenging, especially in critical cases where the marginal risks have compound distributions or when the risks are dependent. We introduce a generating function method to compute these conditional expectations. We provide efficient algorithms to compute the conditional expectations of marginals given the total risk for a portfolio of risks with lattice-type support. We show that the ordinary generating function of unconditional expected allocations is a function of the multivariate probability generating function of the portfolio. The generating function method allows us to develop recursive and transform-based techniques to compute the unconditional expected allocations. We illustrate our method to large-scale risk-sharing and risk allocation problems, including cases where the marginal risks have compound distributions, where the portfolio is composed of dependent risks, and where the risks have heavy tails, leading in some cases to computational gains of several orders of magnitude. Our approach is useful for risk-sharing in peer-to-peer insurance and risk allocation based on Euler's rule.</div></div>","PeriodicalId":54974,"journal":{"name":"Insurance Mathematics & Economics","volume":"122 ","pages":"Pages 119-136"},"PeriodicalIF":1.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A generalized tail mean-variance model for optimal capital allocation 最优资本配置的广义尾均值-方差模型

IF 1.9 2区经济学

Insurance Mathematics & Economics Pub Date : 2025-05-01 Epub Date: 2025-03-07 DOI: 10.1016/j.insmatheco.2025.03.003

Yang Yang , Guojing Wang , Jing Yao , Hengyue Xie

{"title":"A generalized tail mean-variance model for optimal capital allocation","authors":"Yang Yang , Guojing Wang , Jing Yao , Hengyue Xie","doi":"10.1016/j.insmatheco.2025.03.003","DOIUrl":"10.1016/j.insmatheco.2025.03.003","url":null,"abstract":"<div><div>Capital allocation is a core task in financial and actuarial risk management. Some well-known capital allocation principles, such as the “Euler principle” and the “haircut principle”, have been widely used in the banking and insurance industry. The partitions of allocated capital not only serve as a buffer against potential losses but also provide certain risk pricing and performance measurement to the underlying risks. <span><span>Dhaene et al. (2012)</span></span> proposed a unified distance-minimizing capital allocation framework. Their objective function in the optimization only considers the magnitude of the loss function but not the variability. In this paper, we propose a general tail mean-variance (GTMV) model, which employs the Bregman divergences to construct distance-minimizing functions, and takes both the magnitude and the variability into account. We prove the existence and uniqueness of the optimal allocation and provide the general system of equations that characterizes the optimal solution. In this context, we further introduce the Mahalanobis tail mean-variance (MTMV) model and provide explicit distribution-free optimal allocation formulas, which cover many existing results as special cases. In particular, we derive the parametric analytical solutions for multivariate generalized hyperbolic distributed risks. For multivariate log-generalized hyperbolic distributed non-negative risks, we use the convex approximation method to obtain explicit solutions. We present two numerical examples showing the good performance of our optimal capital allocation rules. The first one analyzes the market risk of S&P 500 industry sector indices. We show that our optimal capital allocation framework is applicable to various scenario analyses and provides a performance measure for the indices and the financial market. The other example is based on insurance claims from an Australian insurance company, showing our approximate formulas are both robust and accurate.</div></div>","PeriodicalId":54974,"journal":{"name":"Insurance Mathematics & Economics","volume":"122 ","pages":"Pages 157-179"},"PeriodicalIF":1.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0