-
A representation-learning approach for insurance pricing with images ASTIN Bull. (IF 1.9) Pub Date : 2024-03-15 Christopher Blier-Wong, Luc Lamontagne, Etienne Marceau
Unstructured data are a promising new source of information that insurance companies may use to understand their risk portfolio better and improve the customer experience. However, these novel data sources are difficult to incorporate into existing ratemaking frameworks due to the size and format of the unstructured data. This paper proposes a framework to use street view imagery within a generalized
-
A Markov multiple state model for epidemic and insurance modelling ASTIN Bull. (IF 1.9) Pub Date : 2024-03-14 Minh-Hoang Tran
With recent epidemics such as COVID-19, H1N1 and SARS causing devastating financial loss to the economy, it is important that insurance companies plan for financial costs of epidemics. This article proposes a new methodology for epidemic and insurance modelling by combining the existing deterministic compartmental models and the Markov multiple state models to facilitate actuarial computations to design
-
Expressive mortality models through Gaussian process kernels ASTIN Bull. (IF 1.9) Pub Date : 2024-02-15 Jimmy Risk, Mike Ludkovski
We develop a flexible Gaussian process (GP) framework for learning the covariance structure of Age- and Year-specific mortality surfaces. Utilizing the additive and multiplicative structure of GP kernels, we design a genetic programming algorithm to search for the most expressive kernel for a given population. Our compositional search builds off the Age–Period–Cohort (APC) paradigm to construct a covariance
-
Integration of traditional and telematics data for efficient insurance claims prediction ASTIN Bull. (IF 1.9) Pub Date : 2024-02-15 Hashan Peiris, Himchan Jeong, Jae-Kwang Kim, Hangsuck Lee
While driver telematics has gained attention for risk classification in auto insurance, scarcity of observations with telematics features has been problematic, which could be owing to either privacy concerns or favorable selection compared to the data points with traditional features. To handle this issue, we apply a data integration technique based on calibration weights for usage-based insurance
-
Telematics combined actuarial neural networks for cross-sectional and longitudinal claim count data ASTIN Bull. (IF 1.9) Pub Date : 2024-02-14 Francis Duval, Jean-Philippe Boucher, Mathieu Pigeon
We present novel cross-sectional and longitudinal claim count models for vehicle insurance built upon the combinedd actuarial neural network (CANN) framework proposed by Wüthrich and Merz. The CANN approach combines a classical actuarial model, such as a generalized linear model, with a neural network. This blending of models results in a two-component model comprising a classical regression model
-
Fair valuations of insurance policies under multiple risk factors: A flexible lattice approach ASTIN Bull. (IF 1.9) Pub Date : 2024-02-12 Pierre Devolder, Emilio Russo, Alessandro Staino
We propose a flexible lattice model to evaluate the fair value of insurance contracts embedding both financial and actuarial risk factors. Flexibility relies on the ability of the model to manage different specifications of the correlated processes governing interest rate, mortality, and fund dynamics, thus allowing the insurer to make the most appropriate choices. The model is also able to handle
-
Optimal insurance with counterparty and additive background risk ASTIN Bull. (IF 1.9) Pub Date : 2024-02-02 Yanhong Chen
In this paper, we explore how to design the optimal insurance contracts when the insured faces insurable, counterparty, and additive background risk simultaneously. The target is to minimize the mean-variance of the insured’s loss. By utilizing the calculus of variations, an implicit characterization of the optimal ceded loss function is given. An explicit structure of the optimal ceded loss function
-
Microscopic traffic models, accidents, and insurance losses ASTIN Bull. (IF 1.9) Pub Date : 2024-01-10 Sojung Kim, Marcel Kleiber, Stefan Weber
The paper develops a methodology to enable microscopic models of transportation systems to be accessible for a statistical study of traffic accidents. Our approach is intended to permit an understanding not only of historical losses but also of incidents that may occur in altered, potential future systems. Through such a counterfactual analysis, it is possible, from an insurance, but also from an engineering
-
Taxation and policyholder behavior: the case of guaranteed minimum accumulation benefits ASTIN Bull. (IF 1.9) Pub Date : 2024-01-05 Jennifer Alonso-García, Michael Sherris, Samuel Thirurajah, Jonathan Ziveyi
This paper considers variable annuity (VA) contracts embedded with guaranteed minimum accumulation benefit (GMAB) riders when policyholder’s proceeds are taxed upon early surrender or maturity. These contracts promise the return of the premium paid by the policyholder, or a higher rolled-up value, at the end of the investment period. A partial differential equation valuation framework which exploits
-
Pricing and hedging of longevity basis risk through securitisation ASTIN Bull. (IF 1.9) Pub Date : 2023-12-27 Fadoua Zeddouk, Pierre Devolder
Pension funds and insurers face difficulties in hedging their longevity risk, which is the uncertainty of how long their clients will live. A possible solution could be using longevity-linked securities to transfer some of this risk to other parties. However, these securities may not match the actual mortality rates of the insurer’s clients, resulting in a potential loss due to basis risk. In this
-
Optimal performance of a tontine overlay subject to withdrawal constraints ASTIN Bull. (IF 1.9) Pub Date : 2023-11-17 Peter A. Forsyth, Kenneth R. Vetzal, Graham Westmacott
We consider the holder of an individual tontine retirement account, with maximum and minimum withdrawal amounts (per year) specified. The tontine account holder initiates the account at age 65 and earns mortality credits while alive, but forfeits all wealth in the account upon death. The holder wants to maximize total withdrawals and minimize expected shortfall at the end of the retirement horizon
-
Construction of rating systems using global sensitivity analysis: A numerical investigation ASTIN Bull. (IF 1.9) Pub Date : 2023-10-19 Arianna Vallarino, Giovanni Rabitti, Amir Khorrami Chokami
The ratemaking process is a key issue in insurance pricing. It consists in pooling together policyholders with similar risk profiles into rating classes and assigning the same premium for policyholders in the same class. In actuarial practice, rating systems are typically not based on all risk factors but rather only some of factors are selected to construct the rating classes. The objective of this
-
Optimal VIX-linked structure for the target benefit pension plan ASTIN Bull. (IF 1.9) Pub Date : 2023-10-18 Lv Chen, Danping Li, Yumin Wang, Xiaobai Zhu
In this paper, we study the optimal VIX-linked target benefit (TB) pension design. By applying the dynamic programming approach, we show the optimal risk-sharing structure for the benefit payment exhibits a linear form that consists of three components: (1) a model-robust performance adjustment, (2) a counter-cyclical volatility adjustment that depends on the VIX index, and (3) a TB level that is partially
-
Risk sharing in equity-linked insurance products: Stackelberg equilibrium between an insurer and a reinsurer ASTIN Bull. (IF 1.9) Pub Date : 2023-10-18 Yevhen Havrylenko, Maria Hinken, Rudi Zagst
We study the optimal investment-reinsurance problem in the context of equity-linked insurance products. Such products often have a capital guarantee, which can motivate insurers to purchase reinsurance. Since a reinsurance contract implies an interaction between the insurer and the reinsurer, we model the optimization problem as a Stackelberg game. The reinsurer is the leader in the game and maximizes
-
Target benefit versus defined contribution scheme: a multi-period framework ASTIN Bull. (IF 1.9) Pub Date : 2023-09-01 Ping Chen, Haixiang Yao, Hailiang Yang, Dan Zhu
A target benefit plan (TBP) is a collective defined contribution (DC) plan that is growing in popularity in Canada. Similar to DC plans, TBPs have fixed contribution rates, but they also implement pooling of longevity and investment risk. In this paper, we formulate a multi-period model that incorporates two sources of risk – asset risk and labor income risk for active members. We present an optimal
-
Multi-population mortality modelling: a Bayesian hierarchical approach ASTIN Bull. (IF 1.9) Pub Date : 2023-08-25 Jianjie Shi, Yanlin Shi, Pengjie Wang, Dan Zhu
Modelling mortality co-movements for multiple populations has significant implications for mortality/longevity risk management. This paper assumes that multiple populations are heterogeneous sub-populations randomly drawn from a hypothetical super-population. Those heterogeneous sub-populations may exhibit various patterns of mortality dynamics across different age groups. We propose a hierarchical
-
Range-based risk measures and their applications ASTIN Bull. (IF 1.9) Pub Date : 2023-08-15 Marcelo Brutti Righi, Fernanda Maria Müller
We propose a family of range-based risk measures to generalize the role of value at risk (VaR) in the formulation of range value at risk (RVaR) considering other risk measures induced by a tail level. We discuss this type of measure in detail and its theoretical properties and representations. Moreover, we present a score function to evaluate the forecasts of these measures. In order to present the
-
A hybrid data mining framework for variable annuity portfolio valuation ASTIN Bull. (IF 1.9) Pub Date : 2023-07-28 Hyukjun Gweon, Shu Li
A variable annuity is a modern life insurance product that offers its policyholders participation in investment with various guarantees. To address the computational challenge of valuing large portfolios of variable annuity contracts, several data mining frameworks based on statistical learning have been proposed in the past decade. Existing methods utilize regression modeling to predict the market
-
Ratemaking in a changing environment ASTIN Bull. (IF 1.9) Pub Date : 2023-07-18 A. Nii-Armah Okine
In pricing insurance contracts based on the individual policyholder’s aggregate losses for non-life insurers, the literature has mainly focused on using detailed information from policies and closed claims. However, the information on open claims can reflect shifts in the distribution of the expected claim payments better than closed claims. Such shifts may be needed to be reflected in the ratemaking
-
Risk management with local least squares Monte Carlo ASTIN Bull. (IF 1.9) Pub Date : 2023-07-14 Donatien Hainaut, Adnane Akbaraly
The least squares Monte Carlo method has become a standard approach in the insurance and financial industries for evaluating a company’s exposure to market risk. However, the non-linear regression of simulated responses on risk factors poses a challenge in this procedure. This article presents a novel approach to address this issue by employing an a-priori segmentation of responses. Using a K-means
-
Reinsurance games with variance-premium reinsurers: from tree to chain ASTIN Bull. (IF 1.9) Pub Date : 2023-07-11 Jingyi Cao, Dongchen Li, Virginia R. Young, Bin Zou
This paper studies dynamic reinsurance contracting and competition problems under model ambiguity in a reinsurance market with one primary insurer and n reinsurers, who apply the variance premium principle and who are distinguished by their levels of ambiguity aversion. The insurer negotiates reinsurance policies with all reinsurers simultaneously, which leads to a reinsurance tree structure with full
-
Optimal commissions and subscriptions in mutual aid platforms ASTIN Bull. (IF 1.9) Pub Date : 2023-07-04 Yixing Zhao, Yan Zeng
This paper investigates an operation mechanism for mutual aid platforms to develop more sustainably and profitably. A mutual aid platform is an online risk-sharing platform for risk-heterogeneous participants, and the platform extracts revenues by charging participants commission and subscription fees. A modeling framework is proposed to identify the optimal commissions and subscriptions for mutual
-
Cyber insurance-linked securities ASTIN Bull. (IF 1.9) Pub Date : 2023-06-08 Alexander Braun, Martin Eling, Christoph Jaenicke
We investigate the feasibility of cyber risk transfer through insurance-linked securities (ILS). On the investor side, we elicit the preferred characteristics of cyber ILS and the corresponding return expectations. We then estimate the cost of equity of insurers and compare it to the Rate on Line expected by investors to match demand and supply in the cyber ILS market. Our results show that cyber ILS
-
Intergenerational risk sharing in a defined contribution pension system: analysis with Bayesian optimization ASTIN Bull. (IF 1.9) Pub Date : 2023-05-17 An Chen, Motonobu Kanagawa, Fangyuan Zhang
We study a fully funded, collective defined contribution (DC) pension system with multiple overlapping generations. We investigate whether the welfare of participants can be improved by intergenerational risk sharing (IRS) implemented with a realistic investment strategy (e.g., no borrowing) and without an outside entity (e.g., shareholders) that helps finance the pension fund. To implement IRS, the
-
Estimating the VaR-induced Euler allocation rule ASTIN Bull. (IF 1.9) Pub Date : 2023-05-02 N.V. Gribkova, J. Su, R. Zitikis
The prominence of the Euler allocation rule (EAR) is rooted in the fact that it is the only return on risk-adjusted capital (RORAC) compatible capital allocation rule. When the total regulatory capital is set using the value-at-risk (VaR), the EAR becomes – using a statistical term – the quantile-regression (QR) function. Although the cumulative QR function (i.e., an integral of the QR function) has
-
Bridging the gap between pricing and reserving with an occurrence and development model for non-life insurance claims ASTIN Bull. (IF 1.9) Pub Date : 2023-04-24 Jonas Crevecoeur, Katrien Antonio, Stijn Desmedt, Alexandre Masquelein
Due to the presence of reporting and settlement delay, claim data sets collected by non-life insurance companies are typically incomplete, facing right censored claim count and claim severity observations. Current practice in non-life insurance pricing tackles these right censored data via a two-step procedure. First, best estimates are computed for the number of claims that occurred in past exposure
-
Shortcuts for the construction of sub-annual life tables ASTIN Bull. (IF 1.9) Pub Date : 2023-04-24 Jose M. Pavía, Josep Lledó
Fuelled by the big data explosion, a new methodology to estimate sub-annual death probabilities has recently been proposed, opening new insurance business opportunities. This new approach exploits all the detailed information available from millions of microdata records to develop seasonal-ageing indexes (SAIs) from which sub-annual (quarterly) life tables can be derived from annual tables. In this
-
The use of autoencoders for training neural networks with mixed categorical and numerical features ASTIN Bull. (IF 1.9) Pub Date : 2023-04-24 Łukasz Delong, Anna Kozak
We focus on modelling categorical features and improving predictive power of neural networks with mixed categorical and numerical features in supervised learning tasks. The goal of this paper is to challenge the current dominant approach in actuarial data science with a new architecture of a neural network and a new training algorithm. The key proposal is to use a joint embedding for all categorical
-
Modelling socio-economic mortality at neighbourhood level ASTIN Bull. (IF 1.9) Pub Date : 2023-04-11 Jie Wen, Andrew J.G. Cairns, Torsten Kleinow
In this study, we quantify the relationship between socio-economic status and life expectancy and identify combinations of socio-economic variables that are particularly useful for explaining mortality differences between neighbourhoods in England. We achieve this by examining socio-economic variation in mortality experiences across small areas in England known as lower layer super output areas (LSOAs)
-
Premium control with reinforcement learning ASTIN Bull. (IF 1.9) Pub Date : 2023-04-11 Lina Palmborg, Filip Lindskog
We consider a premium control problem in discrete time, formulated in terms of a Markov decision process. In a simplified setting, the optimal premium rule can be derived with dynamic programming methods. However, these classical methods are not feasible in a more realistic setting due to the dimension of the state space and lack of explicit expressions for transition probabilities. We explore reinforcement
-
Survival energy models for mortality prediction and future prospects ASTIN Bull. (IF 1.9) Pub Date : 2023-04-03 Yasutaka Shimizu, Kana Shirai, Yuta Kojima, Daiki Mitsuda, Mahiro Inoue
The survival energy model (SEM) is a recently introduced novel approach to mortality prediction, which offers a cohort-wise distribution function of the time of death as the first hitting time of a “survival energy” diffusion process to zero. In this study, we propose a novel SEM that can serve as a suitable candidate in the family of prediction models. We also proposed a method to improve the prediction
-
The impact of simultaneous shocks to financial markets and mortality on pension buy-out prices ASTIN Bull. (IF 1.9) Pub Date : 2023-03-30 Ayşe Arık, Ömür Uğur, Torsten Kleinow
In this paper, we determine the fair value of a pension buyout contract under the assumption that changes in mortality can have an impact on financial markets. Our proposed model allows for shocks to occur simultaneously in mortality rates and financial markets, so that strong changes in mortality rates can affect interest rates and asset prices. This approach challenges the common but very strong
-
The 3-step hedge-based valuation: fair valuation in the presence of systematic risks ASTIN Bull. (IF 1.9) Pub Date : 2023-03-14 Daniël Linders
In this paper, we introduce the 3-step hedge-based valuation for the valuation of hybrid claims. We consider an insurance portfolio which is exposed to traded risks, diversifiable risks and non-traded systematic risks. The class of 3-step hedge-based valuations is equivalent with the class of fair valuations. Closed-form solutions are derived for a portfolio of unit-linked contracts under the assumption
-
Risk allocation through shapley decompositions, with applications to variable annuities ASTIN Bull. (IF 1.9) Pub Date : 2023-03-13 Frédéric Godin, Emmanuel Hamel, Patrice Gaillardetz, Edwin Hon-Man Ng
This paper introduces a flexible risk decomposition method for life insurance contracts embedding several risk factors. Hedging can be naturally embedded in the framework. Although the method is applied to variable annuities in this work, it is also applicable in general to other insurance or financial contracts. The approach relies on applying an allocation principle to components of a Shapley decomposition
-
Worst-case moments under partial ambiguity ASTIN Bull. (IF 1.9) Pub Date : 2023-03-13 Qihe Tang, Yunshen Yang
The model uncertainty issue is pervasive in virtually all applied fields but especially critical in insurance and finance. To hedge against the uncertainty of the underlying probability distribution, which we refer to as ambiguity, the worst case is often considered in quantifying the underlying risk. However, this worst-case treatment often yields results that are overly conservative. We argue that
-
Measuring non-exchangeable tail dependence using tail copulas ASTIN Bull. (IF 1.9) Pub Date : 2023-02-28 Takaaki Koike, Shogo Kato, Marius Hofert
Quantifying tail dependence is an important issue in insurance and risk management. The prevalent tail dependence coefficient (TDC), however, is known to underestimate the degree of tail dependence and it does not capture non-exchangeable tail dependence since it evaluates the limiting tail probability only along the main diagonal. To overcome these issues, two novel tail dependence measures called
-
A calendar year mortality model in continuous time ASTIN Bull. (IF 1.9) Pub Date : 2023-02-22 Donatien Hainaut
This article proposes a continuous time mortality model based on calendar years. Mortality rates belong to a mean-reverting random field indexed by time and age. In order to explain the improvement of life expectancies, the reversion level of mortality rates is the product of a deterministic function of age and of a decreasing jump-diffusion process driving the evolution of longevity. We provide a
-
Tail index partition-based rules extraction with application to tornado damage insurance ASTIN Bull. (IF 1.9) Pub Date : 2023-02-22 Arthur Maillart, Christian Y. Robert
The tail index is an important parameter that measures how extreme events occur. In many practical cases, this tail index depends on covariates. In this paper,we assume that it takes a finite number of values over a partition of the covariate space. This article proposes a tail index partition-based rules extraction method that is able to construct estimates of the partition subsets and estimates of
-
Distributionally robust reinsurance with expectile ASTIN Bull. (IF 1.9) Pub Date : 2023-02-16 Xinqiao Xie, Haiyan Liu, Tiantian Mao, Xiao Bai Zhu
We study a distributionally robust reinsurance problem with the risk measure being an expectile and under expected value premium principle. The mean and variance of the ground-up loss are known, but the loss distribution is otherwise unspecified. A minimax problem is formulated with its inner problem being a maximization problem over all distributions with known mean and variance. We show that the
-
Optimal consumption, investment, and insurance under state-dependent risk aversion ASTIN Bull. (IF 1.9) Pub Date : 2023-01-23 Mogens Steffensen, Julie Bjørner Søe
We formalize a consumption–investment–insurance problem with the distinction of a state-dependent relative risk aversion. The state dependence refers to the state of the finite state Markov chain that also formalizes insurable risks such as health and lifetime uncertainty. We derive and analyze the implicit solution to the problem, compare it with special cases in the literature, and illustrate the
-
Portfolio performance under benchmarking relative loss and portfolio insurance: From omega ratio to loss aversion ASTIN Bull. (IF 1.9) Pub Date : 2023-01-16 Tak Wa Ng, Thai Nguyen
We study an optimal investment problem under a joint limited expected relative loss and portfolio insurance constraint with a general random benchmark. By making use of a static Lagrangian method in a complete market setting, the optimal wealth and investment strategy can be fully determined along with the existence and uniqueness of the Lagrangian multipliers. Our numerical demonstration for various
-
Target benefit pension plan with longevity risk and intergenerational equity ASTIN Bull. (IF 1.9) Pub Date : 2023-01-12 Ximin Rong, Cheng Tao, Hui Zhao
We study a stochastic model for a target benefit pension plan suffering from rising longevity and falling fertility. Policies for postponing retirement are carried out to hedge the payment difficulties caused by the aging population. The plan members’ contributions are set in advance while the pension payments reflect intergenerational equity by a target payment level and intergenerational risk sharing
-
A defined benefit pension plan model with stochastic salary and heterogeneous discounting ASTIN Bull. (IF 1.9) Pub Date : 2022-12-01 Ricardo Josa-Fombellida, Paula López-Casado, Jorge Navas
We study the time-consistent investment and contribution policies in a defined benefit stochastic pension fund where the manager discounts the instantaneous utility over a finite planning horizon and the final function at constant but different instantaneous rates of time preference. This difference, which can be motivated for some uncertainties affecting payoffs at the end of the planning horizon
-
Forecasting mortality rates with a coherent ensemble averaging approach ASTIN Bull. (IF 1.9) Pub Date : 2022-11-25 Le Chang, Yanlin Shi
Modeling and forecasting of mortality rates are closely related to a wide range of actuarial practices, such as the designing of pension schemes. To improve the forecasting accuracy, age coherence is incorporated in many recent mortality models, which suggests that the long-term forecasts will not diverge infinitely among age groups. Despite their usefulness, misspecification is likely to occur for
-
Modelling mortality: A bayesian factor-augmented var (favar) approach ASTIN Bull. (IF 1.9) Pub Date : 2022-11-25 Yang Lu, Dan Zhu
Longevity risk is putting more and more financial pressure on governments and pension plans worldwide due to pensioners’ increasing trend of life expectancy and the growing numbers of people reaching retirement age. Lee and Carter (1992, Journal of the American Statistical Association, 87(419), 659–671.) applied a one-factor dynamic factor model to forecast the trend of mortality improvement, and the
-
NEW LOSS RESERVE MODELS WITH PERSISTENCE EFFECTS TO FORECAST TRAPEZOIDAL LOSSES IN RUN-OFF TRIANGLES ASTIN Bull. (IF 1.9) Pub Date : 2022-09-21 Farha Usman, Jennifer S.K. Chan
Modelling loss reserve data in run-off triangles is challenging due to the complex but unknown dynamics in the claim/loss process. Popular loss reserve models describe the mean process through development year, accident year, and calendar year effects using the analysis of variance and covariance (ANCOVA) models. We propose to include in the mean function the persistence terms in the conditional autoregressive
-
EXTENDING THE LEE–CARTER MODEL WITH VARIATIONAL AUTOENCODER: A FUSION OF NEURAL NETWORK AND BAYESIAN APPROACH ASTIN Bull. (IF 1.9) Pub Date : 2022-09-12 Akihiro Miyata, Naoki Matsuyama
In this study, we propose a nonlinear Bayesian extension of the Lee–Carter (LC) model using a single-stage procedure with a dimensionality reduction neural network (NN). LC is originally estimated using a two-stage procedure: dimensionality reduction of data by singular value decomposition followed by a time series model fitting. To address the limitations of LC, which are attributed to the two-stage
-
MULTI-STATE MODELLING OF CUSTOMER CHURN ASTIN Bull. (IF 1.9) Pub Date : 2022-09-08 Yumo Dong, Edward W. Frees, Fei Huang, Francis K. C. Hui
Customer churn, which insurance companies use to describe the non-renewal of existing customers, is a widespread and expensive problem in general insurance, particularly because contracts are usually short-term and are renewed periodically. Traditionally, customer churn analyses have employed models which utilise only a binary outcome (churn or not churn) in one period. However, real business relationships
-
ESTIMATION OF FUTURE DISCRETIONARY BENEFITS IN TRADITIONAL LIFE INSURANCE ASTIN Bull. (IF 1.9) Pub Date : 2022-09-06 Florian Gach, Simon Hochgerner
In the context of life insurance with profit participation, the future discretionary benefits (FDB), which are a central item for Solvency II reporting, are generally calculated by computationally expensive Monte Carlo algorithms. We derive analytic formulas to estimate lower and upper bounds for the FDB. This yields an estimation interval for the FDB, and the average of lower and upper bound is a
-
EVALUATING THE TAIL RISK OF MULTIVARIATE AGGREGATE LOSSES ASTIN Bull. (IF 1.9) Pub Date : 2022-07-15 Wenjun Jiang, Jiandong Ren
In this paper, we study the tail risk measures for several commonly used multivariate aggregate loss models where the claim frequencies are dependent but the claim sizes are mutually independent and independent of the claim frequencies. We first develop formulas for the moment (or size biased) transforms of the multivariate aggregate losses, showing their relationship with the moment transforms of
-
MORTALITY CREDITS WITHIN LARGE SURVIVOR FUNDS ASTIN Bull. (IF 1.9) Pub Date : 2022-06-15 Michel Denuit, Peter Hieber, Christian Y. Robert
Survivor funds are financial arrangements where participants agree to share the proceeds of a collective investment pool in a predescribed way depending on their survival. This offers investors a way to benefit from mortality credits, boosting financial returns. Following Denuit (2019, ASTIN Bulletin, 49, 591–617), participants are assumed to adopt the conditional mean risk sharing rule introduced
-
SELECTING BIVARIATE COPULA MODELS USING IMAGE RECOGNITION ASTIN Bull. (IF 1.9) Pub Date : 2022-05-24 Andreas Tsanakas, Rui Zhu
The choice of a copula model from limited data is a hard but important task. Motivated by the visual patterns that different copula models produce in smoothed density heatmaps, we consider copula model selection as an image recognition problem. We extract image features from heatmaps using the pre-trained AlexNet and present workflows for model selection that combine image features with statistical
-
TREE-BASED MACHINE LEARNING METHODS FOR MODELING AND FORECASTING MORTALITY ASTIN Bull. (IF 1.9) Pub Date : 2022-05-20 Dorethe Skovgaard Bjerre
Machine learning has recently entered the mortality literature in order to improve the forecasts of stochastic mortality models. This paper proposes to use two pure, tree-based machine learning models: random forests and gradient boosting, based on the differenced log-mortality rates to produce more accurate mortality forecasts. These forecasts are compared with forecasts from traditional, stochastic
-
MULTIVARIATE DISTRIBUTIONS WITH TIME AND CROSS-DEPENDENCE: AGGREGATION AND CAPITAL ALLOCATION ASTIN Bull. (IF 1.9) Pub Date : 2022-04-27 Xiang Hu, Lianzeng Zhang
This paper investigates risk aggregation and capital allocation problems for an insurance portfolio consisting of several lines of business. The class of multivariate INAR(1) processes is proposed to model different sources of dependence between the number of claims of the portfolio. The total capital required for the whole portfolio is evaluated under the TVaR risk measure, and the contribution of
-
TARGET VOLATILITY STRATEGIES FOR GROUP SELF-ANNUITY PORTFOLIOS ASTIN Bull. (IF 1.9) Pub Date : 2022-04-11 Annamaria Olivieri, Samuel Thirurajah, Jonathan Ziveyi
While the current pandemic is causing mortality shocks globally, the management of longevity risk remains a major challenge for both individuals and institutions. It is high time there be private market solutions designed for efficient longevity risk transfer among various stakeholders such as individuals, pension funds and annuity providers. From individuals’ point of view, appealing features of post-retirement
-
MODERN LIFE-CARE TONTINES ASTIN Bull. (IF 1.9) Pub Date : 2022-04-05 Peter Hieber, Nathalie Lucas
The tendency of insurance providers to refrain from offering long-term guarantees on investment or mortality risk has shifted attention to mutual risk pooling schemes like (modern) tontines, pooled annuities or group self annuitization schemes. While the literature has focused on mortality risk pooling schemes, this paper builds on the advantage of pooling mortality and morbidity risks, and their inherent
-
CALIBRATING THE LEE-CARTER AND THE POISSON LEE-CARTER MODELS VIA NEURAL NETWORKS ASTIN Bull. (IF 1.9) Pub Date : 2022-03-31 Salvatore Scognamiglio
This paper introduces a neural network (NN) approach for fitting the Lee-Carter (LC) and the Poisson Lee-Carter model on multiple populations. We develop some NNs that replicate the structure of the individual LC models and allow their joint fitting by simultaneously analysing the mortality data of all the considered populations. The NN architecture is specifically designed to calibrate each individual
-
FUNCTIONAL PROFILE TECHNIQUES FOR CLAIMS RESERVING ASTIN Bull. (IF 1.9) Pub Date : 2022-03-10 Matúš Maciak, Ivan Mizera, Michal Pešta
One of the most fundamental tasks in non-life insurance, done on regular basis, is risk reserving assessment analysis, which amounts to predict stochastically the overall loss reserves to cover possible claims. The most common reserving methods are based on different parametric approaches using aggregated data structured in the run-off triangles. In this paper, we propose a rather non-parametric approach
-
A SIMPLE AND NEARLY OPTIMAL INVESTMENT STRATEGY TO MINIMIZE THE PROBABILITY OF LIFETIME RUIN ASTIN Bull. (IF 1.9) Pub Date : 2022-02-16 Xiaoqing Liang, Virginia R. Young
We study the optimal investment strategy to minimize the probability of lifetime ruin under a general mortality hazard rate. We explore the error between the minimum probability of lifetime ruin and the achieved probability of lifetime ruin if one follows a simple investment strategy inspired by earlier work in this area. We also include numerical examples to illustrate the estimation. We show that
-
THE SAINT MODEL: A DECADE LATER ASTIN Bull. (IF 1.9) Pub Date : 2022-01-20 Søren F. Jarner, Snorre Jallbjørn
While many of the prevalent stochastic mortality models provide adequate short- to medium-term forecasts, only few provide biologically plausible descriptions of mortality on longer horizons and are sufficiently stable to be of practical use in smaller populations. Among the very first to address the issue of modelling adult mortality in small populations was the SAINT model, which has been used for