Pub Date : 2021-05-04DOI: 10.1080/10920277.2021.1886948
Maochao Xu, Yiying Zhang
Data breaches cause millions of dollars in financial losses each year. The insurance industry has been exploring the ways to transfer such extreme risk. In this work, we investigate data breach catastrophe (CAT) bonds via developing a multiperiod pricing model. It is found that the nonstationary extreme value model can capture the statistical pattern of the monthly maximum of data breach size very well and, in particular, a positive time trend is discovered. For the financial risks, data-driven time series approaches are proposed to model the complex patterns exhibited by the financial data, which are different from those in the literature. Simulation studies are performed to determine the bond prices and cash flows. Our results show that the data breach CAT bond can be an attractive financial product and an effective instrument for transferring the extreme data breach risk.
{"title":"Data Breach CAT Bonds: Modeling and Pricing","authors":"Maochao Xu, Yiying Zhang","doi":"10.1080/10920277.2021.1886948","DOIUrl":"https://doi.org/10.1080/10920277.2021.1886948","url":null,"abstract":"Data breaches cause millions of dollars in financial losses each year. The insurance industry has been exploring the ways to transfer such extreme risk. In this work, we investigate data breach catastrophe (CAT) bonds via developing a multiperiod pricing model. It is found that the nonstationary extreme value model can capture the statistical pattern of the monthly maximum of data breach size very well and, in particular, a positive time trend is discovered. For the financial risks, data-driven time series approaches are proposed to model the complex patterns exhibited by the financial data, which are different from those in the literature. Simulation studies are performed to determine the bond prices and cash flows. Our results show that the data breach CAT bond can be an attractive financial product and an effective instrument for transferring the extreme data breach risk.","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":"25 1","pages":"543 - 561"},"PeriodicalIF":1.4,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10920277.2021.1886948","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46250931","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 : 2021-04-15DOI: 10.1080/10920277.2021.1874421
Séverine Arnold, V. Glushko
This article applies cointegration analysis and vector error correction models to model the short- and long-run relationships between cause-specific mortality rates. We work with the data from five developed countries (the United States, Japan, France, England and Wales, and Australia) and split the mortality rates into five main causes of death (infectious and parasitic, cancer, circulatory diseases, respiratory diseases, and external causes). We successively adopt short- and long-term perspectives, and analyze how each cause-specific mortality rate impacts and reacts to the shocks received from the rest of the causes. We observe that the cause-specific mortality rates are closely linked to each other, apart from the external causes that show an entirely independent behavior and hence could be considered as truly exogenous. We summarize our findings with the aim to help practitioners set more informed assumptions concerning the future development of mortality.
{"title":"Short- and Long-Term Dynamics of Cause-Specific Mortality Rates Using Cointegration Analysis","authors":"Séverine Arnold, V. Glushko","doi":"10.1080/10920277.2021.1874421","DOIUrl":"https://doi.org/10.1080/10920277.2021.1874421","url":null,"abstract":"This article applies cointegration analysis and vector error correction models to model the short- and long-run relationships between cause-specific mortality rates. We work with the data from five developed countries (the United States, Japan, France, England and Wales, and Australia) and split the mortality rates into five main causes of death (infectious and parasitic, cancer, circulatory diseases, respiratory diseases, and external causes). We successively adopt short- and long-term perspectives, and analyze how each cause-specific mortality rate impacts and reacts to the shocks received from the rest of the causes. We observe that the cause-specific mortality rates are closely linked to each other, apart from the external causes that show an entirely independent behavior and hence could be considered as truly exogenous. We summarize our findings with the aim to help practitioners set more informed assumptions concerning the future development of mortality.","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":"26 1","pages":"161 - 183"},"PeriodicalIF":1.4,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10920277.2021.1874421","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44554246","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 : 2021-03-17DOI: 10.1080/10920277.2022.2082986
Boheng Su, Sharon Tennyson
This article tests for regulation-induced adverse selection in the Massachusetts automobile insurance market during the 1990–2004 period of fix-and-establish rate regulation. We demonstrate the application of the test for adverse selection in Finkelstein and Poterba (Journal of Risk and Insurance 81 (4):709–34, 2014) to a regulated insurance market using group-level panel data on purchase amounts and loss costs. Differences between rates that incorporate state-mandated restrictions and those based on actuarial estimates provide a proxy for the unused observables needed to implement the test. Consistent with regulation-induced adverse selection, proxy values indicating higher unpriced risk are statistically significant and positively related to both insurance purchases and loss costs.
本文对美国马萨诸塞州汽车保险市场在1990-2004年固定固定费率管制期间的监管诱导逆向选择进行了检验。我们将Finkelstein和Poterba (Journal of Risk and Insurance 81(4):709 - 34,2014)的逆向选择测试应用于一个受监管的保险市场，使用集团层面的购买金额和损失成本面板数据。包含州强制限制的费率与基于精算估计的费率之间的差异为实施测试所需的未使用的可观察值提供了代理。与监管诱导的逆向选择一致，表明较高未定价风险的代理值在统计上显著，且与保险购买和损失成本呈正相关。
{"title":"Price Subsidies and the Demand for Automobile Insurance","authors":"Boheng Su, Sharon Tennyson","doi":"10.1080/10920277.2022.2082986","DOIUrl":"https://doi.org/10.1080/10920277.2022.2082986","url":null,"abstract":"This article tests for regulation-induced adverse selection in the Massachusetts automobile insurance market during the 1990–2004 period of fix-and-establish rate regulation. We demonstrate the application of the test for adverse selection in Finkelstein and Poterba (Journal of Risk and Insurance 81 (4):709–34, 2014) to a regulated insurance market using group-level panel data on purchase amounts and loss costs. Differences between rates that incorporate state-mandated restrictions and those based on actuarial estimates provide a proxy for the unused observables needed to implement the test. Consistent with regulation-induced adverse selection, proxy values indicating higher unpriced risk are statistically significant and positively related to both insurance purchases and loss costs.","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":"27 1","pages":"341 - 354"},"PeriodicalIF":1.4,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46134324","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 : 2021-03-12DOI: 10.1080/10920277.2022.2099426
Tsz Chai Fung, G. Tzougas, M. Wüthrich
The aim of this article is to present a mixture composite regression model for claim severity modeling. Claim severity modeling poses several challenges such as multimodality, tail-heaviness, and systematic effects in data. We tackle this modeling problem by studying a mixture composite regression model for simultaneous modeling of attritional and large claims and for considering systematic effects in both the mixture components as well as the mixing probabilities. For model fitting, we present a group-fused regularization approach that allows us to select the explanatory variables that significantly impact the mixing probabilities and the different mixture components, respectively. We develop an asymptotic theory for this regularized estimation approach, and fitting is performed using a novel generalized expectation-maximization algorithm. We exemplify our approach on a real motor insurance dataset.
{"title":"Mixture Composite Regression Models with Multi-type Feature Selection","authors":"Tsz Chai Fung, G. Tzougas, M. Wüthrich","doi":"10.1080/10920277.2022.2099426","DOIUrl":"https://doi.org/10.1080/10920277.2022.2099426","url":null,"abstract":"The aim of this article is to present a mixture composite regression model for claim severity modeling. Claim severity modeling poses several challenges such as multimodality, tail-heaviness, and systematic effects in data. We tackle this modeling problem by studying a mixture composite regression model for simultaneous modeling of attritional and large claims and for considering systematic effects in both the mixture components as well as the mixing probabilities. For model fitting, we present a group-fused regularization approach that allows us to select the explanatory variables that significantly impact the mixing probabilities and the different mixture components, respectively. We develop an asymptotic theory for this regularized estimation approach, and fitting is performed using a novel generalized expectation-maximization algorithm. We exemplify our approach on a real motor insurance dataset.","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":"27 1","pages":"396 - 428"},"PeriodicalIF":1.4,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47941457","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 : 2021-02-25DOI: 10.1080/10920277.2020.1856687
Yaming Yang, Shuanming Li
Modeling dependence structure among various risks, especially the measure of tail dependence and the aggregation of risks, is crucial for risk management. In this article, we present an extension to the traditional one-parameter Archimedean copulas by integrating the log-gamma-generated (LGG) margins. This class of novel multivariate distribution can better capture the tail dependence. The distortion effect on the classic one-parameter Archimedean copulas is well exhibited and the analytical expression of the sum of bivariate margins is proposed. The model provides a flexible way to capture tail risks and aggregate portfolio losses. Sufficient conditions for constructing a legitimate d-dimensional LGG Archimedean copula as well as the simulation framework are also proposed. Furthermore, two applications of this model are presented using concrete insurance datasets.
{"title":"On a Family of Log-Gamma-Generated Archimedean Copulas","authors":"Yaming Yang, Shuanming Li","doi":"10.1080/10920277.2020.1856687","DOIUrl":"https://doi.org/10.1080/10920277.2020.1856687","url":null,"abstract":"Modeling dependence structure among various risks, especially the measure of tail dependence and the aggregation of risks, is crucial for risk management. In this article, we present an extension to the traditional one-parameter Archimedean copulas by integrating the log-gamma-generated (LGG) margins. This class of novel multivariate distribution can better capture the tail dependence. The distortion effect on the classic one-parameter Archimedean copulas is well exhibited and the analytical expression of the sum of bivariate margins is proposed. The model provides a flexible way to capture tail risks and aggregate portfolio losses. Sufficient conditions for constructing a legitimate d-dimensional LGG Archimedean copula as well as the simulation framework are also proposed. Furthermore, two applications of this model are presented using concrete insurance datasets.","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":"26 1","pages":"123 - 142"},"PeriodicalIF":1.4,"publicationDate":"2021-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10920277.2020.1856687","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49415183","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 : 2021-02-23DOI: 10.1080/10920277.2020.1855199
M. Denuit, C. Robert
Denuit (2019, 2020a) demonstrated that conditional mean risk sharing introduced by Denuit and Dhaene (2012) is the appropriate theoretical tool to share losses in collaborative peer-to-peer insurance schemes. Denuit and Robert (2020a, 2020b, 2021) studied this risk sharing mechanism and established several attractive properties including linear approximations when total losses or the number of participants get large. It is also shown there that the conditional expectation defining the conditional mean risk sharing is asymptotically increasing in the total loss (under mild technical assumptions). This ensures that the risk exchange is Pareto-optimal and that all participants have an interest to keep total losses as small as possible. In this article, we design a flexible system where entry prices can be made attractive compared to the premium of a regular, commercial insurance contract and participants are awarded cash-backs in case of favorable experience while being protected by a stop-loss treaty in the opposite case. Members can also be grouped according to some meaningful criteria, resulting in a hierarchical decomposition of the community. The particular case where realized losses are allocated in proportion to the pure premiums is studied.
{"title":"Collaborative Insurance with Stop-Loss Protection and Team Partitioning","authors":"M. Denuit, C. Robert","doi":"10.1080/10920277.2020.1855199","DOIUrl":"https://doi.org/10.1080/10920277.2020.1855199","url":null,"abstract":"Denuit (2019, 2020a) demonstrated that conditional mean risk sharing introduced by Denuit and Dhaene (2012) is the appropriate theoretical tool to share losses in collaborative peer-to-peer insurance schemes. Denuit and Robert (2020a, 2020b, 2021) studied this risk sharing mechanism and established several attractive properties including linear approximations when total losses or the number of participants get large. It is also shown there that the conditional expectation defining the conditional mean risk sharing is asymptotically increasing in the total loss (under mild technical assumptions). This ensures that the risk exchange is Pareto-optimal and that all participants have an interest to keep total losses as small as possible. In this article, we design a flexible system where entry prices can be made attractive compared to the premium of a regular, commercial insurance contract and participants are awarded cash-backs in case of favorable experience while being protected by a stop-loss treaty in the opposite case. Members can also be grouped according to some meaningful criteria, resulting in a hierarchical decomposition of the community. The particular case where realized losses are allocated in proportion to the pure premiums is studied.","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":"26 1","pages":"143 - 160"},"PeriodicalIF":1.4,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10920277.2020.1855199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47463162","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 : 2021-02-19DOI: 10.1080/10920277.2020.1853574
Di Wang, W. Chan
There have been significant developments in using extrapolative stochastic models for mortality forecasting (forward projection) in the literature. However, little attention has been devoted to mortality backcasting (backward projection). This article proposes a simple mortality backcasting framework that can be used in practice. Research and analysis of English demography in the 17th and 18th centuries have suffered from a lack of mortality data. We attempt to alleviate this problem by developing a technique that runs backward in time and produces estimates of mortality data before the time at which such data became available. After confirming the time reversibility of the mortality data, we compare the backcasting performance of some commonly used stochastic mortality models for the England and Wales data. The original Lee–Carter model is selected for backcasting purpose of this dataset. Finally, we examine the longevity of British artists between the 17th and the 20th centuries using the backcasted population mortality as benchmarks. The results show that artists living in Britain from 1600 to the mid 1800s had life expectancies similar to those of the general population, with a marked increase in longevity after the Industrial Revolution.
{"title":"Backcasting Mortality in England and Wales, 1600–1840","authors":"Di Wang, W. Chan","doi":"10.1080/10920277.2020.1853574","DOIUrl":"https://doi.org/10.1080/10920277.2020.1853574","url":null,"abstract":"There have been significant developments in using extrapolative stochastic models for mortality forecasting (forward projection) in the literature. However, little attention has been devoted to mortality backcasting (backward projection). This article proposes a simple mortality backcasting framework that can be used in practice. Research and analysis of English demography in the 17th and 18th centuries have suffered from a lack of mortality data. We attempt to alleviate this problem by developing a technique that runs backward in time and produces estimates of mortality data before the time at which such data became available. After confirming the time reversibility of the mortality data, we compare the backcasting performance of some commonly used stochastic mortality models for the England and Wales data. The original Lee–Carter model is selected for backcasting purpose of this dataset. Finally, we examine the longevity of British artists between the 17th and the 20th centuries using the backcasted population mortality as benchmarks. The results show that artists living in Britain from 1600 to the mid 1800s had life expectancies similar to those of the general population, with a marked increase in longevity after the Industrial Revolution.","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":"26 1","pages":"102 - 122"},"PeriodicalIF":1.4,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10920277.2020.1853574","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48430451","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 : 2021-02-18DOI: 10.1080/10920277.2019.1662316
P. Lyu, A. D. De Waegenaere, B. Melenberg
All-cause mortality is driven by various types of cause-specific mortality. Projecting all-cause mortality based on cause-of-death mortality allows one to understand the drivers of the recent changes in all-cause mortality. However, the existing literature has argued that all-cause mortality projections based on cause-specific mortality experience have a number of serious drawbacks, including the inferior cause-of-death mortality data and the complex dependence structure between causes of death. In this article, we use the recent World Health Organization causes-of-death data to address this issue in a multipopulation context. We construct a new model in the spirit of N. Li and Lee (2005) but in terms of cause-specific mortality. A new two-step beta convergence test is used to capture the cause-specific mortality dynamics between different countries and between different causes. We show that the all-cause mortality estimations produced by the new model perform in the sample similarly to the estimations by the Lee-Carter and Li-Lee all-cause mortality models. However, in contrast to results from earlier studies, we find that the all-cause mortality projections of the new model have better out-of-sample performance in a long forecast horizon. Moreover, for the case of The Netherlands, an approximately 1-year higher remaining life expectancy projection for a 67-year-old Dutch male in a 30-year forecast horizon is obtained by this new model, compared to the all-cause Li-Lee mortality model.
{"title":"A Multi-population Approach to Forecasting All-Cause Mortality Using Cause-of-Death Mortality Data","authors":"P. Lyu, A. D. De Waegenaere, B. Melenberg","doi":"10.1080/10920277.2019.1662316","DOIUrl":"https://doi.org/10.1080/10920277.2019.1662316","url":null,"abstract":"All-cause mortality is driven by various types of cause-specific mortality. Projecting all-cause mortality based on cause-of-death mortality allows one to understand the drivers of the recent changes in all-cause mortality. However, the existing literature has argued that all-cause mortality projections based on cause-specific mortality experience have a number of serious drawbacks, including the inferior cause-of-death mortality data and the complex dependence structure between causes of death. In this article, we use the recent World Health Organization causes-of-death data to address this issue in a multipopulation context. We construct a new model in the spirit of N. Li and Lee (2005) but in terms of cause-specific mortality. A new two-step beta convergence test is used to capture the cause-specific mortality dynamics between different countries and between different causes. We show that the all-cause mortality estimations produced by the new model perform in the sample similarly to the estimations by the Lee-Carter and Li-Lee all-cause mortality models. However, in contrast to results from earlier studies, we find that the all-cause mortality projections of the new model have better out-of-sample performance in a long forecast horizon. Moreover, for the case of The Netherlands, an approximately 1-year higher remaining life expectancy projection for a 67-year-old Dutch male in a 30-year forecast horizon is obtained by this new model, compared to the all-cause Li-Lee mortality model.","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":"25 1","pages":"S421 - S456"},"PeriodicalIF":1.4,"publicationDate":"2021-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10920277.2019.1662316","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48538440","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 : 2021-02-18DOI: 10.1080/10920277.2019.1651658
A. Cairns, Ghali El Boukfaoui
This article considers the assessment of longevity basis risk in the context of a general index-based hedge. We develop a detailed framework for measuring the impact of a hedge on regulatory or economic capital that takes population basis risk explicitly into account. The framework is set up in a way that accommodates a variety of regulatory regimes such as Solvency II as well as local actuarial practice, attempting, therefore, to bridge the gap between academia and practice. This is followed by a detailed analysis of the capital relief resulting from a hedge that uses a call spread as the hedging instrument. We find that the impact of population basis risk on capital relief (expressed in terms of a “haircut” relative to the case with no population basis risk) depends strongly on the exhaustion point of the hedge instrument. In particular, in a Solvency II setting, if the exhaustion point lies well below the 99.5% Value-at-Risk, population basis risk has a negligible impact and the haircut is zero.
{"title":"Basis Risk in Index-Based Longevity Hedges: A Guide for Longevity Hedgers","authors":"A. Cairns, Ghali El Boukfaoui","doi":"10.1080/10920277.2019.1651658","DOIUrl":"https://doi.org/10.1080/10920277.2019.1651658","url":null,"abstract":"This article considers the assessment of longevity basis risk in the context of a general index-based hedge. We develop a detailed framework for measuring the impact of a hedge on regulatory or economic capital that takes population basis risk explicitly into account. The framework is set up in a way that accommodates a variety of regulatory regimes such as Solvency II as well as local actuarial practice, attempting, therefore, to bridge the gap between academia and practice. This is followed by a detailed analysis of the capital relief resulting from a hedge that uses a call spread as the hedging instrument. We find that the impact of population basis risk on capital relief (expressed in terms of a “haircut” relative to the case with no population basis risk) depends strongly on the exhaustion point of the hedge instrument. In particular, in a Solvency II setting, if the exhaustion point lies well below the 99.5% Value-at-Risk, population basis risk has a negligible impact and the haircut is zero.","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":"25 1","pages":"S97 - S118"},"PeriodicalIF":1.4,"publicationDate":"2021-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10920277.2019.1651658","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43855735","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 : 2021-01-27DOI: 10.1080/10920277.2020.1826975
Patrice Gaillardetz, Emmanuel Osei Mireku
The impact of model risk when hedging equity-linked products and other investment guarantees is significant. We propose a model to determine the worst-case value of an equity-linked product through partial hedging. Risk control strategies based on conditional Value at Risk measures are used. The model integrates both mortality and financial risk associated with these products to find the worst-case value. We adopt robust optimization techniques to compute an optimal hedging strategy. To demonstrate versatility of the framework, numerical examples of point-to-point equity-indexed annuities are presented in multinomial lattice dynamics. We compare robustness of the model to super-replicating and quadratic hedging strategies by computing their capital requirements.
{"title":"Worst-Case Valuation of Equity-Linked Products Using Risk-Minimizing Strategies","authors":"Patrice Gaillardetz, Emmanuel Osei Mireku","doi":"10.1080/10920277.2020.1826975","DOIUrl":"https://doi.org/10.1080/10920277.2020.1826975","url":null,"abstract":"The impact of model risk when hedging equity-linked products and other investment guarantees is significant. We propose a model to determine the worst-case value of an equity-linked product through partial hedging. Risk control strategies based on conditional Value at Risk measures are used. The model integrates both mortality and financial risk associated with these products to find the worst-case value. We adopt robust optimization techniques to compute an optimal hedging strategy. To demonstrate versatility of the framework, numerical examples of point-to-point equity-indexed annuities are presented in multinomial lattice dynamics. We compare robustness of the model to super-replicating and quadratic hedging strategies by computing their capital requirements.","PeriodicalId":46812,"journal":{"name":"North American Actuarial Journal","volume":"26 1","pages":"64 - 81"},"PeriodicalIF":1.4,"publicationDate":"2021-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10920277.2020.1826975","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45555149","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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