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Tag: Actuarial Science & Risk Modeling

  • Pension reform improves long-term fiscal sustainability in Russia

    Pension reform improves long-term fiscal sustainability in Russia

    What the study found

    The study found that Russia’s 2018 pension reform, which raised the statutory retirement age, is associated with a short-run drop in consumption but stronger long-term growth in output, investment, government spending, and exports. The authors also report improved fiscal sustainability, including a lower required budget-balancing VAT rate and a smaller pension fund deficit.

    Why the authors say this matters

    The authors conclude that the findings highlight the importance of structural reforms for long-term macroeconomic stability. They also say the results show that demographics and external shocks, such as oil prices, play a critical role in pension system performance.

    What the researchers tested

    The researchers developed a dynamic overlapping generations general equilibrium model for the Russian economy. An overlapping generations model is a type of economic model that tracks different age groups over time. They used demographic projections, variable labor supply responses, and exogenous oil price scenarios to compare post-reform outcomes with baseline scenarios without the reform.

    What worked and what didn't

    Raising the retirement age moderately reduced consumption in the short run, but it was linked to more robust long-term growth in output, investment, government spending, and exports. The reform improved fiscal sustainability by lowering the required VAT rate and the pension fund deficit, with stronger effects under adverse demographic conditions or low oil prices. The fiscal effect was muted in optimistic demographic scenarios with strong labor force growth, but remained significant when population aging intensified fiscal pressure.

    What to keep in mind

    The abstract does not describe detailed model limitations beyond the scenarios examined. The findings are based on model-based comparisons of reform and no-reform trajectories, not on direct observation of future outcomes.

    • The study modeled Russia’s 2018 pension reform, which raised the statutory retirement age.
    • It found a moderate short-run decline in consumption after the reform.
    • Long-term output, investment, government spending, and exports were projected to grow more strongly.
    • The reform lowered the required budget-balancing VAT rate and the pension fund deficit.
    • Fiscal benefits were larger under adverse demographic conditions or low oil prices.
    • The fiscal effect was weaker in optimistic demographic scenarios with strong labor force growth.

  • Affine model gives explicit valuation formulas for insured contracts

    What the study found

    The study proposes a general affine approach for valuing insurance contracts with guarantees, and it yields explicit valuation formulas for variable annuities and related products. The authors report that the framework can model financial markets, mortality, and policyholder behaviour together, while allowing dependence between mortality and equity dynamics.

    Why the authors say this matters

    The authors say medium- and long-term insurance products need participation in equity market returns to stay competitive, but guarantees are needed to remove downside risk. The study suggests that a unified insurance-finance framework with analytical tractability may help value these contracts in a flexible way.

    What the researchers tested

    The researchers studied a general setup for jointly modelling financial markets, mortality, and policyholder behaviour. They used affine processes, a class of stochastic processes that often allows explicit formulas, and modelled surrender intensities as functions of the driving affine process.

    What worked and what didn't

    The affine framework produced explicit valuation formulas for variable annuities and related contracts. It also permitted flexible dependence between mortality and equity dynamics, and it introduced endogenous market dependence into lapse behaviour through surrender intensities. The abstract does not report a comparison with other models or any failed cases.

    What to keep in mind

    The abstract does not describe empirical data, numerical tests, or limitations of the approach. It also does not state which contract types beyond variable annuities were evaluated in detail.

    • The paper proposes an affine framework for valuing insurance products with guarantees.
    • It gives explicit valuation formulas for variable annuities and related contracts.
    • The model jointly handles financial markets, mortality, and policyholder behaviour.
    • Surrender intensities are linked to the driving affine process, creating market-dependent lapse behaviour.
    • The abstract does not report empirical validation or specific limitations.

  • Dependence uncertainty changes joint life insurance risk evaluation

    What the study found

    The study found that risk evaluation for some standard joint life insurance contracts depends monotonically on the concordance order of the underlying copula, which is a mathematical description of dependence between lifetimes. It also found that bounds for the mean, Value-at-Risk, and Expected Shortfall can be computed when the uncertainty set is defined by a norm-ball around a reference copula.

    Why the authors say this matters

    The authors conclude that this is relevant for pricing and evaluating joint life insurance products when data and information are limited and the dependence structure is uncertain. They also suggest that their bounds can improve existing bounds based on the available information.

    What the researchers tested

    The researchers studied robust pricing and risk evaluation of joint life insurance products under dependence uncertainty between two lifetimes. They examined standard contracts, distortion risk measures, and uncertainty sets centered on a reference copula, and they analyzed the problem using linear programs.

    What worked and what didn't

    For the class of contracts they considered, risk evaluation based on a distortion risk measure was monotone with respect to the concordance order of the copula. They proved that the bounds for the mean, Value-at-Risk, and Expected Shortfall are computed by combinations of linear programs under the norm-ball uncertainty setting. Their numerical analysis showed that sensitivity to the choice of copula differs by risk measure and contract type, and that their proposed bounds can improve existing bounds.

    What to keep in mind

    The abstract focuses on two lifetimes and a class of standard contracts, so the results may not apply beyond that setting. The abstract does not describe specific numerical details, and it does not provide further limitations beyond the stated dependence uncertainty framework.

    • The paper studies joint life insurance risk evaluation under uncertainty about dependence between two lifetimes.
    • For some standard contracts, distortion-risk-based evaluation is monotone with respect to the copula's concordance order.
    • Bounds for the mean, Value-at-Risk, and Expected Shortfall can be computed using combinations of linear programs when the uncertainty set is a norm-ball around a reference copula.
    • Sensitivity to the copula choice varies by risk measure and contract type.
    • The proposed bounds may improve existing bounds based on available information.

  • Hermite process distributions are shown to have densities

    What the study found

    The study found that, for any distinct times, the vector formed by a Hermite process has a density with respect to Lebesgue measure. In other words, its finite-dimensional distributions are absolutely continuous.

    Why the authors say this matters

    The authors note that the non-Gaussian case had not yet been settled, unlike the Gaussian case of fractional Brownian motion. They also say their methodology could extend to other non-Gaussian models.

    What the researchers tested

    The researchers studied Hermite processes of order q ≥ 1 with self-similarity parameter H in (1/2, 1). They extended a three-step approach from the Gaussian setting using Malliavin calculus, including a determinant identity for the Malliavin matrix, strong local nondeterminism at the level of Malliavin derivatives, and the Bouleau-Hirsch criterion.

    What worked and what didn't

    The approach worked in the sense that it led to the density result for the vector of values at distinct times. The abstract does not describe any failed steps or negative results.

    What to keep in mind

    The summary only states results for finite-dimensional distributions at distinct times, not for other properties of Hermite processes. Limitations beyond this scope are not described in the available abstract.

    • Hermite processes of order q ≥ 1 were shown to have finite-dimensional densities.
    • The result applies for self-similarity parameter H in (1/2, 1).
    • The proof uses Malliavin calculus and a three-step strategy adapted from the Gaussian case.
    • The abstract says the non-Gaussian case had not yet been settled before this work.
    • The authors suggest the method may extend to other non-Gaussian models.

  • Preventive health spending is linked to longevity in portfolio decisions

    What the study found

    The study found a framework for including preventive health expenditures in lifetime portfolio decisions under uncertain lifetimes. It treats age at death as a random variable and models how allocating wealth to prevention can reduce mortality risk and extend life expectancy.

    Why the authors say this matters

    The authors conclude that the findings offer insights for individual financial planning and for public health policy in a setting where longevity is increasing and health and economic decisions are more connected. The study suggests that personal characteristics and demographic factors shape trade-offs among consumption, investment, and health.

    What the researchers tested

    The researchers built a model combining financial portfolio optimization with actuarial mortality modeling. They used numerical simulations to examine how optimal prevention strategies vary by gender, age, and country-specific mortality profiles.

    What worked and what didn't

    The simulations showed that optimal prevention strategies vary systematically by gender, age, and mortality profile. The abstract does not report specific strategies that performed best or any strategies that failed.

    What to keep in mind

    This summary is based only on the abstract, so detailed limitations are not described. The paper presents a model and simulation results, but the abstract does not provide empirical validation or real-world outcome data.

    • The paper proposes a framework that adds preventive health spending to lifetime portfolio selection.
    • Age at death is modeled as a random variable under uncertain lifetimes.
    • Allocating wealth to prevention is modeled as a way to reduce mortality risk and extend life expectancy.
    • Numerical simulations show that optimal prevention strategies vary by gender, age, and country-specific mortality profiles.
    • The authors say the findings may inform financial planning and public health policy.

  • Age-grouping framework improves mortality forecast accuracy

    What the study found

    The study found that grouping subgroups with similar mortality patterns and borrowing information across them can improve the accuracy of mortality rate forecasts. The proposed framework was reported to perform better than the classical mortality models it extended.

    Why the authors say this matters

    The authors suggest this matters because more accurate forecasts of mortality rates are useful for mortality prediction frameworks. They conclude that using information from similar population–gender–age subgroups can strengthen forecasting performance.

    What the researchers tested

    The researchers extended classical mortality models by adding borrowed information from population, gender, and age subgroups with similar mortality patterns. They evaluated several distance measures together with four linkage methods to capture structural similarities among mortality trajectories, using data from the Human Mortality Database.

    What worked and what didn't

    The proposed approach showed superior predictive performance in the empirical analyses reported in the abstract. The abstract does not specify which distance measures or linkage methods worked best individually, or which alternatives performed less well.

    What to keep in mind

    The available summary does not provide detailed numerical results, specific model comparisons, or limitations. It also does not describe how the method performed across every subgroup or setting beyond the reported empirical analyses.

    • The study extends classical mortality models by borrowing information from similar subgroups.
    • It uses population, gender, and age subgroup mortality patterns.
    • Several distance measures were tested with four linkage methods.
    • Empirical analyses using Human Mortality Database data reported superior predictive performance.
    • The abstract does not give detailed numerical results or limitations.

  • Spatial modification improved Lee–Carter mortality model fit

    What the study found

    The study found that a spatial modification of the Lee–Carter model, a common mortality modeling method, can address the problem of age parameters that are not constant over time. The authors report that adding a cluster effect helped with this issue and produced better model fit than the original model.

    Why the authors say this matters

    The authors suggest this matters because mortality rates are often modeled over age and time, and the study indicates that spatial patterns may help explain why the Lee–Carter model's age parameters change over time. They conclude that using spatial analysis may improve mortality modeling.

    What the researchers tested

    The researchers proposed a spatial modification of the Lee–Carter mortality model and used simulation to examine whether cluster effects could explain the nonconstant age-parameter problem. They also applied the approach to mortality data from Japan, France, the United States, and Taiwan, using spatial cluster detection methods such as spatial scan statistics.

    What worked and what didn't

    In computer simulation, the authors found that cluster effects were a possible source of the nonconstant age-parameter problem, and adding the cluster effect could solve it. In the country data they analyzed, the proposed approach showed better fitting results and smaller mean absolute percentage errors than the Lee–Carter model.

    What to keep in mind

    The abstract does not describe detailed limitations or the size of the data sets. It also only states results for the countries analyzed and for the simulation setting described in the abstract.

    • The study proposes a spatial modification of the Lee–Carter mortality model.
    • The authors say cluster effects may explain why Lee–Carter age parameters are not constant over time.
    • Simulation results suggested that adding a cluster effect could solve the nonconstant parameter problem.
    • Applications to Japan, France, the U.S., and Taiwan showed better fit than the original Lee–Carter model.
    • The proposed approach also produced smaller mean absolute percentage errors than the Lee–Carter model.

  • Mortality patterns increasingly shaped by skewness and kurtosis

    What the study found

    The study found that lifespan disparity, a measure of variation in ages at death, can be closely explained by statistical features of age-at-death distributions. In adult populations, the first four standardized moments of these distributions determine about 95% of life disparity values.

    Why the authors say this matters

    The authors conclude that adding skewness and kurtosis, two measures describing asymmetry and tail shape in a distribution, yields new insights into mortality compression and lifespan variability trends. They also note that these measures help show how mortality patterns have shifted over time.

    What the researchers tested

    The researchers analyzed data from the Human Mortality Database, using 7,408 life tables from 41 countries covering 1751 to 2019. They examined how standard statistical moments, including variance, skewness, and kurtosis, relate to lifespan disparity and its trends.

    What worked and what didn't

    Remaining life expectancy contributed the most to lifespan disparity, but its share declined over time as variance, skewness, and kurtosis made up larger shares. The study also reports shifts in skewness toward more symmetric age-at-death distributions in recent trends, and kurtosis patterns suggesting a persistent fraction of deaths at distribution extremes, including a plateau in the number of centenarians.

    What to keep in mind

    The abstract describes an empirical analysis of a mathematical relationship, so the findings are limited to the datasets and populations included. The available summary does not describe additional limitations beyond the focus on adult populations for the 95% result.

    • Lifespan disparity can be expressed as a linear combination of standard statistical moments of age-at-death distributions.
    • From 1751 to 2019, the share of lifespan disparity explained by remaining life expectancy declined.
    • Variance, skewness, and kurtosis accounted for larger shares of lifespan disparity over time.
    • For adult populations aged 30 and older, about 95% of life disparity values were determined by the first four standardized moments.
    • Kurtosis patterns suggested a persistent fraction of deaths at distribution extremes and a plateau in centenarians.

  • Correlated regime-switching raises guaranteed annuity option prices

    What the study found

    The study found that guaranteed annuity option (GAO) prices are materially higher when interest-rate risk and mortality risk are modeled together with correlation and regime-switching. GAOs are contracts that let policyholders turn accumulated savings into a life annuity at a guaranteed minimum rate.

    Why the authors say this matters

    The authors say accurate valuation of these long-term, survival-contingent contracts is essential for solvency assessment and risk management. The findings indicate that the framework may be practically relevant for managing longevity-linked guarantees under economic and demographic uncertainty.

    What the researchers tested

    The researchers developed a pricing framework for GAOs that models interest rates and mortality rates as correlated stochastic processes with regime-switching governed by a finite-state continuous-time Markov chain. They estimated model parameters using U.S. interest rates and cohort mortality data with quasi-maximum likelihood estimation, and derived a semi-analytic valuation formula based on the joint distribution of the processes.

    What worked and what didn't

    Numerical results showed that including correlation and regime-switching increases GAO prices relative to conventional one-state models. The semi-analytic approach was reported to provide substantial computational advantages over standard Monte Carlo simulations. Sensitivity analysis identified parameters most relevant for long-horizon pricing and solvency considerations, but the abstract does not list those parameters.

    What to keep in mind

    The summary does not provide detailed numerical estimates, parameter values, or a full list of limitations. It also does not describe how the framework performs beyond the tested U.S. interest-rate and cohort-mortality data.

    • GAOs are annuity contracts with a guaranteed minimum conversion rate at maturity.
    • The study modeled interest rates and mortality rates as correlated processes with regime-switching.
    • GAO prices were higher than in conventional one-state models when correlation and regime-switching were included.
    • The semi-analytic method was said to be faster than standard Monte Carlo simulation.
    • Sensitivity analysis highlighted parameters important for long-horizon pricing and solvency.

  • Health insurance was the strongest predictor of healthy aging

    What the study found

    The study found that an XGBoost model could predict healthy aging in adults aged 50 and older better than logistic regression and a multi-layer perceptron. The authors also report that health insurance type was the most predictive feature in their analysis.

    Why the authors say this matters

    The authors conclude that their findings underscore the significant role of health insurance in contributing to healthy aging. They present this as relevant to understanding how social determinants of health, meaning social and economic conditions that shape health, relate to healthy aging.

    What the researchers tested

    The researchers used data from the All of Us Research Program registered tier dataset v7 in a retrospective cohort study. They included participants aged 50 and older who answered at least one social determinants of health survey question and had electronic health record data, and they trained logistic regression, a multi-layer perceptron, and XGBoost models to predict a composite healthy aging outcome based on comorbidities, cognitive conditions, and mobility function.

    What worked and what didn't

    The best-performing model was XGBoost with random oversampling, with an AUROC of 0.793 and an F1 score of 0.697. The same model also showed similar positive and negative predictive values across race and sex groups, and feature importance ranked health insurance type above employment status, substance use, and health insurance coverage. The abstract says XGBoost outperformed logistic regression and the multi-layer perceptron, but it does not provide detailed comparative scores for those models.

    What to keep in mind

    This summary is limited to what is stated in the abstract. The abstract does not describe external validation, causal inference, or limitations of the dataset beyond the study design and included population.

    • XGBoost was the strongest model for predicting healthy aging in this cohort.
    • Health insurance type was the top-ranked predictive feature.
    • The study included 99,935 participants aged 50 and older.
    • Healthy aging was defined using comorbidities, cognitive conditions, and mobility function.
    • The authors report similar predictive values across race and sex groups for the best model.