A risk of small defined-benefit pension schemes is that there are too few members to eliminate idiosyncratic mortality risk, that is there are too few members to effectively pool mortality risk. This means that when there are few members in the scheme, there is an increased risk of the liability value deviating signifi…
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48 results for “mortality risk”
Volterra mortality model: Actuarial valuation and risk management with long-range dependenceq-fin.MF
New model incorporates long-range dependence in mortality rates for better valuation and risk management.
problem Lack of appropriate models for valuing and managing mortality securities with long-range dependence.
method Proposes a novel class of Volterra mortality models that incorporate LRD, derived in closed-form solution.
result Models provide flexibility and tractability for valuing and hedging mortality-related products.
Develops a bi-variate stochastic framework to model mortality and interest rates with long-range dependence.
problem Captures long-range dependence and instantaneous correlation in mortality and interest rates.
method Mixed fractional Brownian motions, analytical solutions, risk-neutral measure, sequential parameter estimation.
result Explicit pricing of zero-coupon bonds and extreme mortality bonds, practical implications for pricing and risk management.
In recent years, a market for mortality derivatives began developing as a way to handle systematic mortality risk, which is inherent in life insurance and annuity contracts. Systematic mortality risk is due to the uncertain development of future mortality intensities, or {\it hazard rates}. In this paper, we develop a …
We study the effects of non-systematic and systematic mortality risks on the required initial capital in a pension plan, in the presence of financial risks. We discover that for a pension plan with few members the impact of pooling on the required capital per person is strong, but non-systematic risk diminishes rapidly…
Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank studycs.LG
Study develops a dynamic risk model for COVID-19 mortality using UK Biobank data.
problem Developing tools to monitor high-risk patients during the COVID-19 pandemic.
method Data-driven random forest classification model using baseline characteristics and symptoms.
result Model predicts COVID-19 mortality with excellent performance (AUC: 0.91), identifying novel predictors.
This paper addresses the risk-minimization problem, with and without mortality securitization, à la Föllmer-Sondermann for a large class of equity-linked mortality contracts when no model for the death time is specified. This framework includes the situation where the correlation between the market model and the time o…
The paper proposes a dynamic risk measure approach for evaluating defined-contribution pension funds.
problem Periodic evaluation of defined-contribution pension funds to manage risk and improve projections.
method Dynamic risk measure criterion, model-free reinforcement learning, Lee-Carter mortality model.
result Periodic evaluations lead to more risk-averse strategies, while mortality improvements encourage risk-seeking behaviors.
The aim of this paper is to propose a realistic and operational model to quantify the systematic risk of mortality included in an engagement of retirement. The model presented is built on the basis of model of Lee-Carter. The stochastic prospective tables thus built make it possible to project the evolution of the rand…
Proposes a new model for mortality forecasting considering age groups and cohort effects.
problem Longevity risk due to ageing population.
method Mixed-effects time-series approach with age groups dependency and random cohort effects.
result Remarkable improvements in forecast accuracy compared to the CBD model.
Optimal timing for converting savings into annuities considering mortality risk.
problem Determining the best time to annuitize retirement savings under stochastic mortality.
method Formulated as a three-dimensional optimal stopping problem, reduced to nested one-dimensional problems, solved using PDMP structure.
result Rich structure for the optimal annuitization rule, covering various parameter specifications.
The aim of this paper is to propose a realistic and operational model to quantify the systematic risk of mortality included in an engagement of retirement. The model presented is built on the basis of model of Lee-Carter. The stochastic prospective tables thus built make it possible to project the evolution of the rand…
We develop a theory for pricing non-diversifiable mortality risk in an incomplete market. We do this by assuming that the company issuing a mortality-contingent claim requires compensation for this risk in the form of a pre-specified instantaneous Sharpe ratio. We prove that our ensuing valuation formula satisfies a nu…
GMWB Riders in a Binomial Framework - Pricing, Hedging, and Diversification of Mortality Riskq-fin.PR
We construct a binomial model for a guaranteed minimum withdrawal benefit (GMWB) rider to a variable annuity (VA) under optimal policyholder behaviour. The binomial model results in explicitly formulated perfect hedging strategies funded using only periodic fee income. We consider the separate perspectives of the insur…
In this paper, we discuss the impact of some mortality data anomalies on an internal model capturing longevity risk in the Solvency 2 framework. In particular, we are concerned with abnormal cohort effects such as those for generations 1919 and 1920, for which the period tables provided by the Human Mortality Database …
Paper develops a two-population model to assess longevity basis risk.
problem Mismatch between hedger's liability and hedging instrument causes longevity basis risk.
method Develops a two-population mortality model using Lee-Carter model and renewal process.
result Proposed model provides significant risk reduction when mortality jumps and sampling risk are considered.
Stochastic model for pension insurer assets and liabilities with mortality risk.
problem Modeling assets and liabilities with mortality risk in pensions insurers.
method Multivariate stochastic process for asset and liability returns, capturing dynamics and dependencies.
result Efficient computation of a million scenarios on personal computers.
The study uses ML and AI to forecast pension fund mortality, outperforming traditional methods.
problem Incorporating longevity risk into pension fund financial assessments.
method Employed actuarial learning with ML/AI techniques (regression trees, random forest, boosting, XGBoost, CatBoost, neural networks) on actuarial data.
result ML/AI algorithms outperform the Lee-Carter model in mortality forecasting for pension funds.
Valuation of Mortality Risk via the Instantaneous Sharpe Ratio: Applications to Life Annuitiesq-fin.PR
We develop a theory for valuing non-diversifiable mortality risk in an incomplete market. We do this by assuming that the company issuing a mortality-contingent claim requires compensation for this risk in the form of a pre-specified instantaneous Sharpe ratio. We apply our method to value life annuities. One result of…
Good predictors of ICU Mortality have the potential to identify high-risk patients earlier, improve ICU resource allocation, or create more accurate population-level risk models. Machine learning practitioners typically make choices about how to represent features in a particular model, but these choices are seldom eva…
Optimal annuitization strategy depends on age, labor income, and mortality risk.
problem Maximizing utility from consumption and labor income under age-dependent mortality.
method Dynamic programming approach to derive closed-form solutions.
result Post-retirement labor income acts as a substitute for annuitization.
Managing Systematic Mortality Risk in Life Annuities: An Application of Longevity Derivativesq-fin.CP
This paper assesses the hedge effectiveness of an index-based longevity swap and a longevity cap. Although swaps are a natural instrument for hedging longevity risk, derivatives with non-linear pay-offs, such as longevity caps, also provide downside protection. A tractable stochastic mortality model with age dependent …
Paper proposes robust method to detect risk heterogeneity across ethnic groups.
problem Detecting risk heterogeneity across ethnic groups in ICU studies.
method Proposes a robust framework using Neyman orthogonality for inference.
result Demonstrates improved inferential stability and reduced bias compared to standard methods.
Although timely sepsis diagnosis and prompt interventions in Intensive Care Unit (ICU) patients are associated with reduced mortality, early clinical recognition is frequently impeded by non-specific signs of infection and failure to detect signs of sepsis-induced organ dysfunction in a constellation of dynamically cha…
ICU mortality risk prediction is a tough yet important task. On one hand, due to the complex temporal data collected, it is difficult to identify the effective features and interpret them easily; on the other hand, good prediction can help clinicians take timely actions to prevent the mortality. These correspond to the…
The majority of biomedical studies use limited datasets that may not generalize over large heterogeneous datasets that have been collected over several decades. The current paper develops and validates several multimodal models that can predict 1-year mortality based on a massive clinical dataset. Our focus on predicti…
Early recognition of risky trajectories during an Intensive Care Unit (ICU) stay is one of the key steps towards improving patient survival. Learning trajectories from physiological signals continuously measured during an ICU stay requires learning time-series features that are robust and discriminative across diverse …
Framework predicts mortality risk in MAFLD subjects.
problem Lack of mortality prediction methods for MAFLD subjects.
method Artificial Intelligence-based framework MAFUS using ML algorithms.
result Support Vector Machines (SVM) is the best model for mortality prediction.
We extend the lifecycle model (LCM) of consumption over a random horizon (a.k.a. the Yaari model) to a world in which (i.) the force of mortality obeys a diffusion process as opposed to being deterministic, and (ii.) a consumer can adapt their consumption strategy to new information about their mortality rate (a.k.a. h…
Investigates RI strategies for life insurers with LRD mortality rates.
problem Effect of long-range dependent mortality rates on RI strategies.
method Volterra mortality model, compound Poisson process, open-loop equilibrium mean-variance criterion.
result Explicit equilibrium RI controls derived and uniqueness studied.
The study examines how different interpolation methods affect the decomposition of life insurance surplus.
problem The impact of different interpolation methods on the decomposition of life insurance surplus.
method The study uses the IASU decomposition method to analyze the effects of different interpolation methods (Lee-Carter and linear) on the surplus decomposition.
result Lee-Carter and linear interpolation yield almost identical decompositions, while constant approximations result in different decompositions.
Machine Intelligence for Outcome Predictions of Trauma Patients During Emergency Department Carecs.LG
Machine learning predicts trauma patient mortality risk.
problem Predicting mortality risk in trauma patients using traditional regression models.
method Transfer learning-based machine learning algorithm applied to trauma patient data.
result Machine learning model achieved similar performance to contemporary models without restrictive criteria.
The paper prices a new life insurance policy for couples, considering various contingent benefits.
problem Valuation of life insurance policies for dependent coupled lives.
method Employed the -rule combining real-world and risk-neutral measures for pricing.
result Explicit pricing expressions for the new insurance policy were derived and analyzed.
We propose the use of statistical emulators for the purpose of valuing mortality-linked contracts in stochastic mortality models. Such models typically require (nested) evaluation of expected values of nonlinear functionals of multi-dimensional stochastic processes. Except in the simplest cases, no closed-form expressi…
Bounds derived for contract values in life insurance with financial market interaction.
problem Incompleteness in life tables for modern insurance products.
method Derivation of upper and lower bounds for hybrid functionals of lifetime under different assumptions.
result Characterization of worst- and best-case contract values over compatible mortality processes.
Febrile neutropenia (FN) has been associated with high mortality, especially among adults with cancer. Understanding the patient and provider level heterogeneity in FN hospital admissions has potential to inform personalized interventions focused on increasing survival of individuals with FN. We leverage machine learni…
Paper proposes a natural hedging framework with graphical assessment for longevity risk management.
problem Lack of a unified framework for natural hedging and graphical risk assessment.
method Structured natural hedging framework integrated with a graphical risk metric.
result Demonstrates flexibility, interpretability, and practical value for longevity risk management.
Study proposes a new model for joint survival annuity valuation.
problem Valuation of joint survival annuities and options.
method Linear-rational Wishart mortality model based on stochastic matrix affine process.
result Derives closed-form expression for joint survival annuity and option.
Optimizes retirement income with MBGs and neural networks for longevity risk.
problem Maximizing lifetime withdrawals while managing longevity risk.
method Neural-network optimization under stochastic mortality.
result International diversification and longevity pooling improve retirement outcomes.
Develops a risk score to assist ECMO planning for critically ill patients with viral or unspecified pneumonia.
problem Lack of a risk score to guide ECMO planning for critically ill patients.
method Leverages machine learning to develop the PEER score.
result PEER score predicts mortality and decompensation in patients eligible for ECMO.
The aim of this paper is to compare two asset allocation methods for a pension scheme during the decumulation phase in the simplified portfolio selection between a risky asset following a geometric Brownian motion and a riskless asset. The two asset allocation criteria are the ruin probability of the insurance company …
Various types of structures that enable a group of individuals to pool their mortality risk have been proposed in the literature. Collectively, the structures are called pooled annuity funds. Since the pooled annuity funds propose different methods of pooling mortality risk, we investigate the connections between them …
Study improves mortality prediction in hospital patients using comprehensive feature engineering.
problem Accurate prediction of all-cause in-hospital mortality in healthcare.
method Comprehensive feature engineering approach using vital signs, laboratory results, and demographic data.
result Random Forest model achieved highest performance with AUC of 0.94, significantly outperforming other models.
Deep learning predicts ICU mortality with enhanced interpretability.
problem Improving mortality prediction accuracy and clinician trust in AI.
method Trained a deep learning model on MIMIC-III to interpret nursing notes.
result Model reaches ROC of 0.8629, outperforming SAPS-II.
WRSE predicts dynamic survival distributions in ICU patients.
problem Dynamic assessment of ICU patient mortality risk.
method Non-parametric weighted-resolution ensemble model combining binary classifiers.
result Competitive results with state-of-the-art models, reducing training time.
Optimal healthcare investment timing in a dynamic model with mortality risk.
problem Optimal timing of healthcare investment to reduce mortality risk.
method Dynamic framework, stochastic control, optimal stopping problem, dual transformation.
result Characterization of the optimal investment boundary and numerical solutions.
Risk prediction is central to both clinical medicine and public health. While many machine learning models have been developed to predict mortality, they are rarely applied in the clinical literature, where classification tasks typically rely on logistic regression. One reason for this is that existing machine learning…
We introduce an additive stochastic mortality model which allows joint modelling and forecasting of underlying death causes. Parameter families for mortality trends can be chosen freely. As model settings become high dimensional, Markov chain Monte Carlo (MCMC) is used for parameter estimation. We then link our propose…