Pub Date : 2024-02-02DOI: 10.1080/10920277.2023.2285977
Qiao Jiang, Tianxiang Shi
This study develops a hidden Markov model (HMM)-based clustering framework to predict auto insurance losses using driving characteristics extracted from telematics data. Through a simulation experi...
{"title":"Auto Insurance Pricing Using Telematics Data: Application of a Hidden Markov Model","authors":"Qiao Jiang, Tianxiang Shi","doi":"10.1080/10920277.2023.2285977","DOIUrl":"https://doi.org/10.1080/10920277.2023.2285977","url":null,"abstract":"This study develops a hidden Markov model (HMM)-based clustering framework to predict auto insurance losses using driving characteristics extracted from telematics data. Through a simulation experi...","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":"10 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139751734","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}
Pub Date : 2024-01-30DOI: 10.1080/10920277.2023.2259445
Le Chang, Guangyuan Gao, Yanlin Shi
In the actuarial literature, many existing stochastic claims-reserving methods ignore the excessive effects of outliers. In practice, however, these outlying observations may occur in the upper tri...
{"title":"Claims Reserving with a Robust Generalized Additive Model","authors":"Le Chang, Guangyuan Gao, Yanlin Shi","doi":"10.1080/10920277.2023.2259445","DOIUrl":"https://doi.org/10.1080/10920277.2023.2259445","url":null,"abstract":"In the actuarial literature, many existing stochastic claims-reserving methods ignore the excessive effects of outliers. In practice, however, these outlying observations may occur in the upper tri...","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":"176 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139752024","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}
Pub Date : 2023-12-18DOI: 10.1080/10920277.2023.2254836
David Baños, Å. H. Sande, Carlo Sgarra
The guaranteed minimum maturity benefit (GMMB) is quite a popular feature embedded in several unit-linked policies offered by insurance companies. The value of this benefit depends on several proce...
{"title":"Guaranteed Minimum Maturity Benefits in a Self-Exciting Stochastic Mortality Model: Pricing, Estimation and Calibration","authors":"David Baños, Å. H. Sande, Carlo Sgarra","doi":"10.1080/10920277.2023.2254836","DOIUrl":"https://doi.org/10.1080/10920277.2023.2254836","url":null,"abstract":"The guaranteed minimum maturity benefit (GMMB) is quite a popular feature embedded in several unit-linked policies offered by insurance companies. The value of this benefit depends on several proce...","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":"73 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138823482","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}
Pub Date : 2023-12-18DOI: 10.1080/10920277.2023.2256818
Shimeng Huang, Ken Seng Tan, Jinggong Zhang, Wenjun Zhu
The COVID-19 pandemic has had a with severe human toll and catastrophic economic losses and has also heightened the need for more effective solutions for managing epidemic-related risks. This artic...
{"title":"Epidemic Financing Facilities: Pandemic Bonds and Endemic Swaps","authors":"Shimeng Huang, Ken Seng Tan, Jinggong Zhang, Wenjun Zhu","doi":"10.1080/10920277.2023.2256818","DOIUrl":"https://doi.org/10.1080/10920277.2023.2256818","url":null,"abstract":"The COVID-19 pandemic has had a with severe human toll and catastrophic economic losses and has also heightened the need for more effective solutions for managing epidemic-related risks. This artic...","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139029339","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}
Pub Date : 2023-12-18DOI: 10.1080/10920277.2023.2281471
Andrew Stocking, Ian Duncan, Nhan Huynh
Diagnosis coding in administrative data is often incomplete and introduces inaccurate assessments of patients’ health outcomes. The underdiagnosis of chronic conditions reduces the ability to corre...
{"title":"Estimating Underdiagnosis of Patients in Chronically Ill Populations","authors":"Andrew Stocking, Ian Duncan, Nhan Huynh","doi":"10.1080/10920277.2023.2281471","DOIUrl":"https://doi.org/10.1080/10920277.2023.2281471","url":null,"abstract":"Diagnosis coding in administrative data is often incomplete and introduces inaccurate assessments of patients’ health outcomes. The underdiagnosis of chronic conditions reduces the ability to corre...","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":"7 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138717557","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}
Pub Date : 2023-10-19DOI: 10.1080/10920277.2023.2249524
Yuanying Guan, Zhanyi Jiao, Ruodu Wang
AbstractThe celebrated Expected Shortfall (ES, also known as tail Value at Risk or conditional Value at Risk) optimization formula implies that ES at a fixed probability level is the minimum of a linear real function plus a scaled mean excess function. We establish a reverse ES optimization formula that says that a mean excess function at any fixed threshold is the maximum of an ES curve minus a linear function. Despite being a simple result, this formula reveals elegant symmetries between the mean excess function and the ES curve, as well as their optimizers. The reverse ES optimization formula is closely related to the Fenchel-Legendre transforms, and our formulas are generalized from ES to optimized certainty equivalents, a popular class of convex risk measures. We analyze worst-case values of the mean excess function under two popular settings of model uncertainty to illustrate the usefulness of the reverse ES optimization formula, and this is further demonstrated with an application using insurance datasets. Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingRuodu Wang acknowledges financial support from the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-03823, RGPAS-2018-522590).
{"title":"A Reverse ES (CVaR) Optimization Formula","authors":"Yuanying Guan, Zhanyi Jiao, Ruodu Wang","doi":"10.1080/10920277.2023.2249524","DOIUrl":"https://doi.org/10.1080/10920277.2023.2249524","url":null,"abstract":"AbstractThe celebrated Expected Shortfall (ES, also known as tail Value at Risk or conditional Value at Risk) optimization formula implies that ES at a fixed probability level is the minimum of a linear real function plus a scaled mean excess function. We establish a reverse ES optimization formula that says that a mean excess function at any fixed threshold is the maximum of an ES curve minus a linear function. Despite being a simple result, this formula reveals elegant symmetries between the mean excess function and the ES curve, as well as their optimizers. The reverse ES optimization formula is closely related to the Fenchel-Legendre transforms, and our formulas are generalized from ES to optimized certainty equivalents, a popular class of convex risk measures. We analyze worst-case values of the mean excess function under two popular settings of model uncertainty to illustrate the usefulness of the reverse ES optimization formula, and this is further demonstrated with an application using insurance datasets. Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingRuodu Wang acknowledges financial support from the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-03823, RGPAS-2018-522590).","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135729093","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}
Pub Date : 2023-10-13DOI: 10.1080/10920277.2023.2236669
Chong-Rui Zhu
AbstractThis article investigates a dividend optimization problem with a positive creeping-associated terminal value at ruin for spectrally negative Lévy processes. We consider an insurance company whose surplus process evolves according to a spectrally negative Lévy process with a Gaussian part and a finite Lévy measure. Its objective function relates to dividend payments until ruin and a creeping-associated terminal value at ruin. The positive creeping-associated terminal value represents the salvage value or the creeping reward when creeping happens. Owing to formulas from fluctuation theory, the objective considered is represented explicitly. Under certain restrictions on the terminal value and the surplus process, we show that the threshold strategy should be the optimal one over an admissible class with bounded dividend rates. ACKNOWLEDGMENTI am truly grateful to the two anonymous referees for giving their valuable guiding comments on this work.Disclosure StatementNo potential conflict of interest was reported by the author.
{"title":"Optimality of Threshold Strategies for Spectrally Negative Lévy Processes and a Positive Terminal Value at Creeping Ruin","authors":"Chong-Rui Zhu","doi":"10.1080/10920277.2023.2236669","DOIUrl":"https://doi.org/10.1080/10920277.2023.2236669","url":null,"abstract":"AbstractThis article investigates a dividend optimization problem with a positive creeping-associated terminal value at ruin for spectrally negative Lévy processes. We consider an insurance company whose surplus process evolves according to a spectrally negative Lévy process with a Gaussian part and a finite Lévy measure. Its objective function relates to dividend payments until ruin and a creeping-associated terminal value at ruin. The positive creeping-associated terminal value represents the salvage value or the creeping reward when creeping happens. Owing to formulas from fluctuation theory, the objective considered is represented explicitly. Under certain restrictions on the terminal value and the surplus process, we show that the threshold strategy should be the optimal one over an admissible class with bounded dividend rates. ACKNOWLEDGMENTI am truly grateful to the two anonymous referees for giving their valuable guiding comments on this work.Disclosure StatementNo potential conflict of interest was reported by the author.","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135853142","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}
Pub Date : 2023-10-13DOI: 10.1080/10920277.2023.2238793
Francesco Ungolo, Len Patrick Dominic M. Garces, Michael Sherris, Yuxin Zhou
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.
{"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":"42 1","pages":"0"},"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}
Pub Date : 2023-09-08DOI: 10.1080/10920277.2023.2214603
Leon Chen, Steven W. Pottier
{"title":"Event Studies for Publicly Traded Insurers: An Investigation of the Bad-Model Problem","authors":"Leon Chen, Steven W. Pottier","doi":"10.1080/10920277.2023.2214603","DOIUrl":"https://doi.org/10.1080/10920277.2023.2214603","url":null,"abstract":"","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42008488","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}
Pub Date : 2023-08-07DOI: 10.1080/10920277.2023.2218897
Juan Sebastian Yanez, J. Boucher, M. Pigeon
{"title":"Modeling Payment Frequency for Loss Reserves Based on Dynamic Claim Scores","authors":"Juan Sebastian Yanez, J. Boucher, M. Pigeon","doi":"10.1080/10920277.2023.2218897","DOIUrl":"https://doi.org/10.1080/10920277.2023.2218897","url":null,"abstract":"","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48833865","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}
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