Search Results (89)

This paper introduces a new class of probability distributions, termed the generated log exponentiated polynomial (GLEP) family, designed to enhance flexibility in modeling complex real financial data. The proposed family is constructed through a novel cumulative distribution function that combines logarithmic and exponentiated polynomial structures, allowing for rich distributional shapes and tail behaviors. We present comprehensive mathematical properties, including useful series expansions for the density, cumulative, and quantile functions, which facilitate the derivation of moments, generating functions, and order statistics. Characterization results based on the reverse hazard function and conditional expectations are established. The model parameters are estimated using various frequentist methods, including Maximum Likelihood Estimation (MLE), Cramer–von Mises (CVM), Anderson–Darling (ADE), Right Tail Anderson–Darling (RTADE), and Left Tail Anderson–Darling (LEADE), with a comparative simulation study assessing their performance. Risk analysis is conducted using actuarial key risk indicators (KRIs) such as Value-at-Risk (VaR), Tail Value-at-Risk (TVaR), Tail Variance (TV), Tail Mean Variance (TMV), and excess function (EL), demonstrating the model’s applicability in financial and insurance contexts. The practical utility of the GLEP family is illustrated through applications to real and simulated datasets, including house price dynamics and insurance claim sizes. Peaks Over Random Threshold Value-at-Risk (PORT-VaR) analysis is applied to U.K. motor insurance claims and U.S. house prices datasets. Some recommendations are provided. Finally, a comparative study is presented to prove the superiority of the new family. Full article

Background: The aim of this retrospective, single-arm study was to present the technique and preliminary efficacy and safety results of a single-fraction SBRT for LAPC using total intravenous anaesthesia and optical surface guidance as motion management. Methods: Fifty-five patients with locally advanced pancreatic cancer were treated with SBRT with a single-fraction receiving a median BED₁₀ = 128.9 Gy. Forty-two patients received systemic treatment. End points were OS, FFLP, PFS, and toxicity. Actuarial survival analysis and univariate analysis were investigated. Results: The median follow-up was 15 months, mean OS was 18 months (95% CI: 16.7 to 19.3), and the one-year FFLP and 1-year OS were 100% and 90.9% (95% CI: ± 1.5%), respectively. Median PFS was 12 months (95% CI: 9.5 to 14.4), and 1-year PFS was 85.5% (95% CI: ± 1.4%). Thirty-five patients (63.6%) were alive at the time of analysis. No acute/late toxicity > G2/G1 was reported. Conclusions: SBRT for LAPC using total intravenous anaesthesia and optical surface guidance presented as an effective and safe treatment with very low toxicity. Full article

This article presents a transparent and replicable framework to assess the net worth of public pension systems within the broader context of fiscal sustainability and public sector balance sheets. Using Spain as a case study, it transforms Eurostat’s Table 29 data into an actuarial balance sheet and income statement, applying the Swedish open group (SOG) approach. The analysis shows that Spain’s pension system faces a significant funding shortfall, with assets covering only 72% of its liabilities. The proposed method enhances fiscal transparency and provides policymakers with a practical tool to evaluate and improve long-term pension sustainability across different institutional contexts. Full article

This study derives explicit expressions for the Tail Conditional Expectation (TCE) and Tail Variance (TV) within the framework of the extended generalized skew-elliptical (EGSE) distribution. The EGSE family generalizes the class of elliptical distributions by incorporating a selection method, thereby allowing simultaneous and flexible control over location, scale, skewness, and tail heaviness in a unified parametric setting. As notable special cases, our results encompass the extended skew-normal, extended skew-Student-t, extended skew-logistic, and extended skew-Laplace distributions. The derived formulas extend existing results for generalized skew-elliptical distributions and reduce, to a considerable extent, the reliance on numerical integration, thus enhancing their tractability for actuarial and financial risk assessment. The practical utility of the proposed framework is further illustrated through an empirical analysis based on real stock market data, highlighting its effectiveness in quantifying and contrasting the heterogeneous tail risk profiles of financial assets. Full article

This study presents a methodology for evaluating the impact of the pandemic on mortality rates in Italy. The primary objectives are to define criteria for identifying a ‘rise in mortality’, establish a robust evaluation approach, and assess pandemic repercussions using the proposed framework. To conduct a comparative analysis of mortality estimates, two classical models were employed: the Lee–Carter and the Renshaw–Haberman models. The analysis involved utilising actuarial tables and mortality models to quantify pandemic-induced excess deaths by calculating the disparity between these estimates. The proposed method aims to provide a comprehensive and clear understanding of the impact of the pandemic on mortality in Italy. Full article

This study evaluates the current Spanish insurance framework for catastrophic flood risk, administered by the Consorcio de Compensación de Seguros (CCS), based on nationwide loss data reported by the CCS for the period 1996–2020. The analysis of historical claims data enables a clear differentiation between frequent, low-cost events and infrequent, high-impact catastrophes. While the CCS has fulfilled a critical role in post-disaster compensation, the findings highlight the parallel need for ex ante risk mitigation strategies. The study proposes a more efficient, two-tier risk coverage model. Events whose impacts can be managed through standard insurance mechanisms should be underwritten by private insurers using actuarially fair premiums. In contrast, events with catastrophic implications—due to their scale or financial impact—should be addressed through general solidarity mechanisms, centrally managed by the CCS. Such a risk segmentation would improve the financial sustainability of the system and create fiscal space for prevention-oriented incentives. The current design of the CCS scheme may generate moral hazard, as flood exposure is not explicitly priced into the premium structure. Empirical findings support a shift towards a more transparent, incentive-aligned model that combines collective risk sharing with individual risk responsibility—an essential balance for effective climate adaptation and long-term resilience. Full article

Transcatheter aortic valve replacement (TAVR) is a high-risk cardiovascular interventional procedure with a high incidence of postoperative complications, urgently requiring more refined risk identification and mitigation strategies. The main challenges in assessing the risk of TAVR complications lie in the scarcity of real-world data and the co-occurrence of multiple complications. This study developed an adjustment evaluation model that adapts randomised clinical trial (RCT) evidence to real-world data (RWD) and adopted multi-label classification methods that incorporate a LocalGLMnet-like regularization term, enabling data-adaptive parameter shrinkage for more accurate estimation. In the empirical analysis, with real surgical data from a hospital in the United States, a combination of multi-label random sampling and representative multi-label classification algorithms was used to fit the data. The model was compared across multiple evaluation metrics, including Hamming loss, ranking loss, and micro-AUC, to ensure robust results. The model used in this paper bridges the gap between medical risk prediction and insurance actuarial science, provides a practical data modelling foundation and algorithmic support for the future development of post-operative complication insurance products that precisely align with clinical risk. Full article

Capital investment in longevity science—research targeting the biological processes of aging through interventions like cellular reprogramming, AI-driven drug discovery, and biological age monitoring—may create significant divergence between traditional actuarial projections and emerging mortality improvements. This paper examines how accelerating investment in life extension technologies affects mortality improvement trajectories beyond conventional actuarial assumptions, building on the comprehensive investment landscape analysis documented in “Investors in Longevity” supported by venture capital databases, industry reports, and regulatory filings. We introduce an Investment-Adjusted Mortality Model (IAMM) that incorporates capital allocation trends as leading indicators of mortality improvement acceleration. Under high-investment scenarios (annual funding of USD 15+ billion in longevity technologies), current insurance products may significantly underestimate longevity risk, creating potential solvency challenges. Our statistical analysis demonstrates that investment-driven mortality improvements—actual reductions in death rates resulting from new anti-aging interventions—could exceed traditional projections by 18–31% by 2040. We validate our model by backtesting historical data, showing improved predictive performance (35% reduction in MAPE) compared to traditional Lee–Carter approaches during periods of significant medical technology advancement. Based on these findings, we propose modified insurance structures, including dynamic mortality-linked products and biological age underwriting, quantifying their effectiveness in reducing longevity risk exposure by 42–67%. These results suggest the need for actuarial science to incorporate investment dynamics in response to the changing longevity investment environment detailed in “Investors in Longevity”. The framework presented provides both theoretically grounded and empirically tested tools for incorporating investment dynamics into mortality projections and insurance product design, addressing gaps in current risk management approaches for long-term mortality exposure. Full article

Background: The purpose of this study was to evaluate the long-term outcomes of patients with locally advanced gastric adenocarcinoma (LAGC) intended to receive induction chemotherapy, chemoradiation and surgery and to develop an algorithm to estimate the individual risk of relapse in a population-based setting. Methods: Patients with LAGC (cT3-4 and/or N+) were retrospectively evaluated. A pathological response was graded according to the Becker criteria. The nodal regression grade was assessed by a 4-point scale (A–D). A comprehensive analysis of 155 individual patient variables was performed, and logistic regression (LR) was utilized to develop a predictive model for relapse risk. Results: From 2010 to 2024, 48 patients were analyzed. After a median follow-up of 49 months (range, 12–212), the 5-year actuarial PFS and OS rates were 44% and 48%, respectively. Four variables were identified as the most relevant features for training the LR model. Scores for the model accuracy, sensitivity and specificity (mean +/− sd) were 0.79 +/− 0.12, 0.74 +/− 0.221 and 0.88 +/− 0.14, respectively. For a validation dataset, the figures were 0.78, 0.88 and 0.73, respectively. Conclusions: This neoadjuvant strategy seems to correlate with a favorable long-term outcome in a subset of intestinal-type LAGA patients who achieve ypN0 features. Full article

This study addresses key challenges in auto insurance territory risk analysis by examining the complexities of spatial loss data and the evolving landscape of territorial risks before and during the COVID-19 pandemic. Traditional approaches, such as spatial clustering, are commonly used for territory risk assessment but offer limited predictive capabilities, constraining their effectiveness in forecasting future losses, an essential component of insurance pricing. To overcome this limitation, we propose an advanced predictive modeling framework that integrates spatial loss patterns while accounting for the pandemic’s impact. Our Bayesian-based spatial model captures stochastic spatial autocorrelations among territory rating units and their neighboring regions. This approach enables more robust pattern recognition through predictive modeling. By applying this approach to regulatory auto insurance loss datasets, we analyze industry-level trends in claim frequency, loss severity, loss cost, and insurance loading. The results reveal significant shifts in spatial loss patterns before and during the pandemic, highlighting the dynamic interplay between regional risk factors and external disruptions. These insights provide valuable guidance for insurers and regulators, facilitating more informed decision-making in risk classification, pricing adjustments, and policy interventions in response to evolving spatial and economic conditions. Full article

Background/Objectives: Violence and harm to children’s health and well-being remain pressing global concerns, with over one billion children affected annually. Risk assessment tools are widely used to support early identification and intervention, yet their predictive accuracy remains contested. This study aims to systematically evaluate the predictive validity of internationally used child risk assessment tools and examine whether the tools’ characteristics influence their effectiveness. Methods: A comprehensive meta-analysis was conducted using 28 studies encompassing 27 tools and a total sample of 136,700 participants. A three-level meta-analytic model was employed to calculate pooled effect sizes (AUC), assess heterogeneity, and test moderation effects of tool type, length, publication year, assessor type, and target population. The publication bias was tested using Egger’s regression and funnel plots. Results: Overall, the tools demonstrated moderate predictive validity (AUC = 0.686). Among the tool types, the structured clinical judgment (SCJ) tools outperformed the actuarial (AUC = 0.662) and consensus-based tools (AUC = 0.580), suggesting greater accuracy in complex decision-making contexts. Other tool-related factors did not significantly moderate the predictive validity. Conclusions: SCJ tools offer a promising balance between structure and professional judgment. However, all tools have inherent limitations and require careful contextual application. The findings highlight the need for dynamic tools integrating risk and needs assessments and call for practitioner training to improve tool implementation. This study provides evidence-based guidance to inform the development, adaptation, and use of child risk assessment tools in global child protection systems. Full article

Background: Minimally invasive multi-vessel off-pump coronary artery bypass grafting (MICS CABG) through left anterior mini-thoracotomy avoids both extracorporeal circulation and sternotomy and is a very elegant, safe, and effective surgical technique, despite its still-limited adoption in the daily toolkit of cardiac surgeons. The goal of this retrospective, single-centre analysis was to evaluate the long-term outcomes of a large patient cohort undergoing MICS CABG. Methods: This study identified 597 consecutive MICS CABG patients from August 2008 to November 2020. We obtained follow-up data by phone or mail. Every patient had a left internal thoracic artery bypass graft. The second and possibly third grafts were radial arteries, great saphenous vein segments, or right internal thoracic arteries. Results: The median age was 69 years, and 92.1% were male. The median EuroSCORE II was 1.5. There were eight conversions to sternotomy and none to cardiopulmonary bypass. The total arterial revascularisation was 92.5%, with 90.3% complete. The 30-day mortality was 0.5%. A total of 575 patients (95.8%) were tracked for 8 years on average. A Cox regression analysis found that a left ventricular ejection fraction < 50%, peripheral vascular disease, chronic kidney disease, and a history of cerebrovascular accident independently predicted severe adverse cardiac and cerebrovascular events and late death. The actuarial survival rates for one, three, five, eight, and ten years were 99%, 95%, 91%, 85%, and 80%, respectively. Conclusions: In our study group, the technique of MICS CABG has been proven to be a safe and effective surgical revascularisation method, with a low rate of early complications and favourable long-term outcomes in eligible patients. Full article

This paper introduces an innovative sell-type home reversion product aimed at monetizing Singapore’s public flats, serving as a new alternative to the existing Singapore Lease Buyback Scheme (LBS). This new product not only retains the LBS’s guaranteed period of residence in the property along with life annuity incomes but also enhances the product features to meet specific homeowner needs, including the ability to age in place, flexibility in retaining part of the property, options for bequests, and guaranteed principal return. By incorporating these additional features, the new product seeks to stimulate greater demand for monetizing public flats among asset-rich but cash-poor homeowners. An actuarial pricing model is developed to establish a transparent and fair framework for justifying the cost of each product feature. Additionally, we present a cost–benefit analysis from both the provider and consumer perspectives to highlight the major contributions of the new product when compared to the LBS. Full article

In this paper, we propose a weighted Lindley (NWLi) model for the analysis of extreme historical insurance claims. It extends the classical Lindley distribution by incorporating a weight parameter, enabling more flexibility in modeling insurance claim severity. We provide a comprehensive theoretical overview of the new model and explore two practical applications. First, we investigate the mean-of-order P (MOOP_(P)) approach for quantifying the expected claim severity based on the NWLi model. Second, we implement a peaks over a random threshold (PORT) analysis using the value-at-risk metric to assess extreme claim occurrences under the new model. Further, we provide a simulation study to evaluate the accuracy of the estimators under various methods. The proposed model and its applications provide a versatile tool for actuaries and risk analysts to analyze and predict extreme insurance claim severity, offering insights into risk management and decision-making within the insurance industry. Full article

For general insurance pricing, aligning losses with accurate premiums is crucial for insurance companies’ competitiveness. Traditional actuarial models often face challenges like data heterogeneity and mismeasured covariates, leading to misspecification bias. This paper addresses these issues from a Bayesian perspective, exploring connections between Bayesian hierarchical modeling, partial pooling techniques, and the Gustafson correction method for mismeasured covariates. We focus on Non-Differential Berkson (NDB) mismeasurement and propose an approach that corrects such errors without relying on gold standard data. We discover the unique prior knowledge regarding the variance of the NDB errors, and utilize it to adjust the biased parameter estimates built upon the NDB covariate. Using simulated datasets developed with varying error rate scenarios, we demonstrate the superiority of Bayesian methods in correcting parameter estimates. However, our modeling process highlights the challenge in accurately identifying the variance of NDB errors. This emphasizes the need for a thorough sensitivity analysis of the relationship between our prior knowledge of NDB error variance and varying error rate scenarios. Full article