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GEMAct: a Python package for non-life (re)insurance modeling GEMAct：用于非寿险（再）保险建模的 Python 软件包
IF 1.7
Annals of Actuarial Science Pub Date : 2024-02-14 DOI: 10.1017/s1748499524000022
Gabriele Pittarello, Edoardo Luini, Manfred Marvin Marchione
{"title":"GEMAct: a Python package for non-life (re)insurance modeling","authors":"Gabriele Pittarello, Edoardo Luini, Manfred Marvin Marchione","doi":"10.1017/s1748499524000022","DOIUrl":"https://doi.org/10.1017/s1748499524000022","url":null,"abstract":"This paper introduces gemact, a Python package for actuarial modeling based on the collective risk model. The library supports applications to risk costing and risk transfer, loss aggregation, and loss reserving. We add new probability distributions to those available in scipy, including the (a, b, 0) and (a, b, 1) discrete distributions, copulas of the Archimedean family, the Gaussian, the Student t and the Fundamental copulas. We provide an implementation of the AEP algorithm for calculating the cumulative distribution function of the sum of dependent, nonnegative random variables, given their dependency structure specified with a copula. The theoretical framework is introduced at the beginning of each section to give the reader with a sufficient understanding of the underlying actuarial models.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"221 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139757709","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
The discrete-time arbitrage-free Nelson-Siegel model: a closed-form solution and applications to mixed funds representation 离散时间无套利的 Nelson-Siegel 模型：封闭式解法及混合基金表示法的应用
IF 1.7
Annals of Actuarial Science Pub Date : 2024-02-12 DOI: 10.1017/s1748499524000010
Ramin Eghbalzadeh, Frédéric Godin, Patrice Gaillardetz
{"title":"The discrete-time arbitrage-free Nelson-Siegel model: a closed-form solution and applications to mixed funds representation","authors":"Ramin Eghbalzadeh, Frédéric Godin, Patrice Gaillardetz","doi":"10.1017/s1748499524000010","DOIUrl":"https://doi.org/10.1017/s1748499524000010","url":null,"abstract":"A closed-form solution for zero-coupon bonds is obtained for a version of the discrete-time arbitrage-free Nelson-Siegel model. An estimation procedure relying on a Kalman filter is provided. The model is shown to produce adequate fit when applied to historical Canadian spot rate data and to improve distributional predictive performance over benchmarks. An adaptation of the mixed fund return model from Augustyniak <jats:italic>et al</jats:italic>. ((2021). <jats:italic>ASTIN Bulletin: The Journal of the IAA</jats:italic>, <jats:italic>51</jats:italic>(1), 131–159.) is also provided to include the discrete-time arbitrage-free Nelson-Siegel model as one of its building blocks.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"324 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139757858","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
On clustering levels of a hierarchical categorical risk factor 关于分层分类风险因素的聚类水平
IF 1.7
Annals of Actuarial Science Pub Date : 2024-02-01 DOI: 10.1017/s1748499523000283
Bavo D.C. Campo, Katrien Antonio
{"title":"On clustering levels of a hierarchical categorical risk factor","authors":"Bavo D.C. Campo, Katrien Antonio","doi":"10.1017/s1748499523000283","DOIUrl":"https://doi.org/10.1017/s1748499523000283","url":null,"abstract":"<p>Handling nominal covariates with a large number of categories is challenging for both statistical and machine learning techniques. This problem is further exacerbated when the nominal variable has a hierarchical structure. We commonly rely on methods such as the random effects approach to incorporate these covariates in a predictive model. Nonetheless, in certain situations, even the random effects approach may encounter estimation problems. We propose the data-driven Partitioning Hierarchical Risk-factors Adaptive Top-down algorithm to reduce the hierarchically structured risk factor to its essence, by grouping similar categories at each level of the hierarchy. We work top-down and engineer several features to characterize the profile of the categories at a specific level in the hierarchy. In our workers’ compensation case study, we characterize the risk profile of an industry via its observed damage rates and claim frequencies. In addition, we use embeddings to encode the textual description of the economic activity of the insured company. These features are then used as input in a clustering algorithm to group similar categories. Our method substantially reduces the number of categories and results in a grouping that is generalizable to out-of-sample data. Moreover, we obtain a better differentiation between high-risk and low-risk companies.</p>","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"120 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139657706","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
Boosted Poisson regression trees: a guide to the BT package in R 提升泊松回归树：R 中 BT 软件包的使用指南
IF 1.7
Annals of Actuarial Science Pub Date : 2024-01-15 DOI: 10.1017/s174849952300026x
Gireg Willame, Julien Trufin, Michel Denuit
{"title":"Boosted Poisson regression trees: a guide to the BT package in R","authors":"Gireg Willame, Julien Trufin, Michel Denuit","doi":"10.1017/s174849952300026x","DOIUrl":"https://doi.org/10.1017/s174849952300026x","url":null,"abstract":"<p>Thanks to its outstanding performances, boosting has rapidly gained wide acceptance among actuaries. Wüthrich and Buser (Data Analytics for Non-Life Insurance Pricing. Lecture notes available at SSRN. http://dx.doi.org/10.2139/ssrn.2870308, 2019) established that boosting can be conducted directly on the response under Poisson deviance loss function and log-link, by adapting the weights at each step. This is particularly useful to analyze low counts (typically, numbers of reported claims at policy level in personal lines). Huyghe et al. (Boosting cost-complexity pruned trees on Tweedie responses: The ABT machine for insurance ratemaking. Scandinavian Actuarial Journal. https://doi.org/10.1080/03461238.2023.2258135, 2022) adopted this approach to propose a new boosting machine with cost-complexity pruned trees. In this approach, trees included in the score progressively reduce to the root-node one, in an adaptive way. This paper reviews these results and presents the new <span>BT</span> package in <span>R</span> contributed by Willame (Boosting Trees Algorithm. https://cran.r-project.org/package=BT; https://github.com/GiregWillame/BT, 2022), which is designed to implement this approach for insurance studies. A numerical illustration demonstrates the relevance of the new tool for insurance pricing.</p>","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"16 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139469134","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
Epidemic modelling and actuarial applications for pandemic insurance: a case study of Victoria, Australia 大流行病保险的流行病建模和精算应用：澳大利亚维多利亚州的案例研究
IF 1.7
Annals of Actuarial Science Pub Date : 2024-01-09 DOI: 10.1017/s1748499523000246
Chang Zhai, Ping Chen, Zhuo Jin, Tak Kuen Siu
{"title":"Epidemic modelling and actuarial applications for pandemic insurance: a case study of Victoria, Australia","authors":"Chang Zhai, Ping Chen, Zhuo Jin, Tak Kuen Siu","doi":"10.1017/s1748499523000246","DOIUrl":"https://doi.org/10.1017/s1748499523000246","url":null,"abstract":"With the recent outbreak of COVID-19, evaluating the epidemic risk appears to be a pressing issue of global concern and one of the major challenges recently. In the fight against pandemics, the ability to understand, model, and forecast the transmission dynamics of infectious diseases plays a crucial role. This paper provides an overview of foundational compartment models and introduces the Susceptible-Exposed-Infected-Containing-3-Substates-Recovered-Dead model to study the dynamics of COVID-19. A meticulous data calibration procedure is employed to study the evolution trend of an actual pandemic using real-world data from Victoria, Australia. Additionally, the paper discusses innovative applications of epidemic models to the insurance industry, which are currently under investigation. Through the use of the newly developed analytically tractable model, insurance companies are able to determine fair premium levels during an outbreak. Moreover, the paper provides practical guidance for insurance companies by examining the variation in reserve levels over time.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"34 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139411981","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
Nonparametric intercept regularization for insurance claim frequency regression models 保险索赔频率回归模型的非参数截距正则化
IF 1.7
Annals of Actuarial Science Pub Date : 2024-01-05 DOI: 10.1017/s1748499523000271
Gee Y. Lee, Himchan Jeong
{"title":"Nonparametric intercept regularization for insurance claim frequency regression models","authors":"Gee Y. Lee, Himchan Jeong","doi":"10.1017/s1748499523000271","DOIUrl":"https://doi.org/10.1017/s1748499523000271","url":null,"abstract":"In a subgroup analysis for an actuarial problem, the goal is for the investigator to classify the policyholders into unique groups, where the claims experience within each group are made as homogenous as possible. In this paper, we illustrate how the alternating direction method of multipliers (ADMM) approach for subgroup analysis can be modified so that it can be more easily incorporated into an insurance claims analysis. We present an approach to penalize adjacent coefficients only and show how the algorithm can be implemented for fast estimation of the parameters. We present three different cases of the model, depending on the level of dependence among the different coverage groups within the data. In addition, we provide an interpretation of the credibility problem using both random effects and fixed effects, where the fixed effects approach corresponds to the ADMM approach to subgroup analysis, while the random effects approach represents the classic Bayesian approach. In an empirical study, we demonstrate how these approaches can be applied to real data using the Wisconsin Local Government Property Insurance Fund data. Our results show that the presented approach to subgroup analysis could provide a classification of the policyholders that improves the prediction accuracy of the claim frequencies in case other classifying variables are unavailable in the data.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"36 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139373310","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
Modeling and management of cyber risk: a cross-disciplinary review 网络风险建模与管理：跨学科审查
IF 1.7
Annals of Actuarial Science Pub Date : 2024-01-04 DOI: 10.1017/s1748499523000258
Rong He, Zhuo Jin, Johnny Siu-Hang Li
{"title":"Modeling and management of cyber risk: a cross-disciplinary review","authors":"Rong He, Zhuo Jin, Johnny Siu-Hang Li","doi":"10.1017/s1748499523000258","DOIUrl":"https://doi.org/10.1017/s1748499523000258","url":null,"abstract":"This paper provides a review of cyber risk research accomplished in different disciplines, with a primary goal to aid researchers in the field of insurance and actuarial science in identifying potential research gaps as well as leveraging useful models and techniques that have been considered in the literature. We highlight the recent advancements in cyber risk prediction, modeling, management, and insurance achieved in different domains including computer engineering, actuarial science, and business studies. The surveyed works are classified according to their respective modeling approaches, allowing readers to more easily compare the technical aspects of the surveyed works and spot out research gaps based on the research tools of their liking. We conclude this paper with a summary of possible research directions that are identified from the review.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139373242","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
Error propagation and attribution in simulation-based capital models 基于仿真的资本模型中的错误传播和归因
IF 1.7
Annals of Actuarial Science Pub Date : 2023-11-28 DOI: 10.1017/s1748499523000210
Daniel J. Crispin
{"title":"Error propagation and attribution in simulation-based capital models","authors":"Daniel J. Crispin","doi":"10.1017/s1748499523000210","DOIUrl":"https://doi.org/10.1017/s1748499523000210","url":null,"abstract":"Calculation of loss scenarios is a fundamental requirement of simulation-based capital models and these are commonly approximated. Within a life insurance setting, a loss scenario may involve an asset-liability optimization. When cashflows and asset values are dependent on only a small number of risk factor components, low-dimensional approximations may be used as inputs into the optimization and resulting in loss approximation. By considering these loss approximations as perturbations of linear optimization problems, approximation errors in loss scenarios can be bounded to first order and attributed to specific proxies. This attribution creates a mechanism for approximation improvements and for the eventual elimination of approximation errors in capital estimates through targeted exact computation. The results are demonstrated through a stylized worked example and corresponding numerical study. Advances in error analysis of proxy models enhance confidence in capital estimates. Beyond error analysis, the presented methods can be applied to general sensitivity analysis and the calculation of risk.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"23 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138509562","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
Capital requirement modeling for market and non-life premium risk in a dynamic insurance portfolio 动态保险组合中市场和非寿险保费风险的资本需求模型
Annals of Actuarial Science Pub Date : 2023-10-31 DOI: 10.1017/s1748499523000234
Stefano Cotticelli, Nino Savelli
{"title":"Capital requirement modeling for market and non-life premium risk in a dynamic insurance portfolio","authors":"Stefano Cotticelli, Nino Savelli","doi":"10.1017/s1748499523000234","DOIUrl":"https://doi.org/10.1017/s1748499523000234","url":null,"abstract":"Abstract For some time now, Solvency II requires that insurance companies calculate minimum capital requirements to face the risk of insolvency, either in accordance with the Standard Formula or using a full or partial Internal Model. An Internal Model must be based on a market-consistent valuation of assets and liabilities at a 1-year time span, where a real-world probabilistic structure is used for the first year of projection. In this paper, we describe the major risks of a non-life insurance company, i.e. the non-life underwriting risk and market risk, and their interactions, focusing on the non-life premium risk, equity risk, and interest rate risk. This analysis is made using some well-known stochastic models in the financial-actuarial literature and practical insurance business, i.e. the Collective Risk Model for non-life premium risk, the Geometric Brownian Motion for equity risk, and a real-world version of the G2++ Model for interest rate risk, where parameters are calibrated on current and real market data. Finally, we illustrate a case study on a single-line and a multi-line insurance company in order to see how the risk drivers behave in both a stand-alone and an aggregate framework.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"163 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135871969","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
A changing climate for actuarial science 精算科学的气候变化
Annals of Actuarial Science Pub Date : 2023-10-24 DOI: 10.1017/s1748499523000222
Mathieu Boudreault, Iain Clacher, Johnny Siu-Hang Li, Catherine Pigott, Rui Zhou
{"title":"A changing climate for actuarial science","authors":"Mathieu Boudreault, Iain Clacher, Johnny Siu-Hang Li, Catherine Pigott, Rui Zhou","doi":"10.1017/s1748499523000222","DOIUrl":"https://doi.org/10.1017/s1748499523000222","url":null,"abstract":"An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"31 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135220382","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
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