Search Results (314)

Patients with BRAF^V600E-mutated metastatic lung cancer benefit from both BRAF plus MEK inhibitors and immune checkpoint inhibitor (ICI)–chemotherapy. This study evaluated the cost-effectiveness of first-line ICI–chemotherapy compared with BRAF plus MEK inhibitors in these patients. This economic analysis, with a 15-year time horizon and an annual 3% discount, was conducted from the perspective of the healthcare sectors in Taiwan and the US. Simulated patients were entered into partitioned survival models upon initiation of first-line therapies. The model inputs were derived from the FRONT-BRAF study (progression-free/overall survival, adverse events, and subsequent therapies), insurance payments or retail prices (costs of drugs, physician visits, monitoring, adverse events, and end-of-life care), and a hospital cohort (health utility). Deterministic and probabilistic analyses were performed. The incremental cost-effectiveness ratios (ICERs) of ICI–chemotherapy compared with BRAF plus MEK inhibitors (Taiwan: $73,561/QALY; US: $290,279/QALY) exceeded the willingness-to-pay (WTP) thresholds (Taiwan: $70,000/QALY; US: $150,000/QALY). The drug costs of subsequent therapies and the utility values of the progressive-disease state were the major determinants of ICERs. In Taiwan, ICI–chemotherapy had a 41.0% probability of being cost-effective at the WTP threshold. ICI–chemotherapy had a higher probability of being cost-effective than BRAF plus MEK inhibitors when the WTP exceeded $300,000/QALY in the US. Our analysis suggests that, despite the longer survival of first-line ICI–chemotherapy compared with BRAF plus MEK inhibitors, ICI–chemotherapy is not a cost-effective strategy for patients with BRAF^V600E-mutated metastatic lung cancer. Full article

Global retail e-commerce sales have surged, yet product fit uncertainty remains a significant challenge, leading to rising product return rates. To address consumer concerns about return shipping costs, major Chinese online retail platforms have introduced return shipping insurance (RSI). Retailers can choose between Retailer-RSI (RRSI), which is provided by the retailer, and Customer-RSI (CRSI), which is purchased by consumers. Despite these options, information asymmetry causes insurers to assess return rates with bias—referred to as managerial confidence bias. Consequently, platforms are increasingly partnering with insurers to enhance their RSI offerings. This study develops a game-theoretical model to examine the dynamics between a platform and an insurer, as well as the impact of managerial confidence bias on RSI strategies. Our analysis reveals that the platform–insurer relationship is crucial in determining the optimal RSI strategy. Under an independent insurer, RSI is viable only if the insurer underestimates product return rates (i.e., exhibits overconfidence bias); RRSI is preferred if the bias is sufficiently strong, whereas CRSI is chosen otherwise. In contrast, under a dependent insurer, CRSI is favored by the retailer only when its return handling costs are substantially high; otherwise, RRSI is preferred. Furthermore, RSI consistently increases consumer surplus by reducing return hassle costs while only mildly raising the product price. However, the independent insurer’s bias leads to its own profit loss, resulting in a “loss–win–win–win” scenario across stakeholders. In contrast, the dependent insurer, supported by platform subsidies, can yield a “win–win–win–win” outcome that aligns stakeholder interests and enhances long-term platform benefits. Full article

Understanding how consumers perceive the value of travel packages is essential for pricing and product design. Grounded in behavioral economics frameworks, such as Prospect Theory and Mental Accounting, this study analyses satisfaction across three progressive travel packages before and after explicit price disclosure, exploring multi-attribute service valuation and the moderating influence of traveller profiles. Using a quantitative approach with 387 higher education participants, expected satisfaction was measured through a two-phase price disclosure design. Inferential statistical analyses revealed that satisfaction levels decreased significantly for all packages once prices were revealed, with the sharpest decline occurring in the highly comprehensive, all-inclusive option, validating a psychological threshold of value saturation. Packages comprising only essential elements (flights, accommodation with breakfast and insurance) yielded the highest consistent post-price satisfaction, with these core structural components identified as the absolute most valued attributes. Findings suggest that explicit price disclosure acts as a negative moderator of expected satisfaction, triggering an immediate psychological pain of paying, particularly among independent travellers who exhibit higher price sensitivity and remain more analytical of bundled configurations than users of physical travel agencies. This study provides a framework for stakeholders to avoid over-bundling and optimize product efficiency. Furthermore, it contributes to academic discourse on generational consumer behaviour by highlighting how individual travel organization profiles within an emerging European cohort shape the perceived utility and fairness of tourism pricing. Full article

Accurate modeling of dependence in insurance data is essential for pricing, reserving, and capital allocation, particularly when claim frequencies and severities interact in complex ways. Classical independence assumptions often fail in practice, and standard copula models face challenges when applied to discrete or mixed data. This paper develops a nonparametric framework for dependence modeling in count–count and hybrid frequency–severity settings using Sklar-type density estimation. Two exact Sklar-type decompositions clarify the structure of dependence in discrete data, while a kernel-based analogue and a hybrid estimator provide flexible, fully nonparametric tools for discrete and mixed insurance variables. A comprehensive simulation study across multiple sample sizes and dependence levels evaluates estimator performance using several divergence measures. An application to a motor-insurance dataset demonstrates that the proposed methods capture dependence patterns with high fidelity and yield accurate joint distribution estimates. The results highlight the practical value of nonparametric Sklar-type estimators for contemporary actuarial modeling involving discrete or hybrid data. Full article

The transition of thermal power systems toward lower-carbon electricity raises a critical strategic question: whether to rely on cross-border electricity imports or reactivate domestic nuclear capacity under supply constraints. This study examines the trade-offs between these alternatives within a sustainable finance framework. A contingent-claim model is developed in which a life insurer provides long-term financing to a biomass-energy supplier, a thermal power plant, and a nuclear power plant operating under carbon-pricing regulation. The framework links electricity-market decisions with financial risk valuation, allowing the joint effects of biomass utilization, carbon regulation, electricity imports, and nuclear-security risks to be evaluated. The results show that biomass integration and tighter carbon regulation reduce short-term profitability in thermal generation but support long-run decarbonization. Cross-border electricity imports improve system flexibility and reduce operational volatility, strengthening the financial position of thermal producers. In contrast, nuclear-security disruptions significantly increase default risk for nuclear assets, reflecting their exposure to operational and regulatory uncertainty. By integrating energy-transition strategies with contingent-claim valuation, the analysis highlights the role of financial intermediation in shaping investment incentives and risk allocation in the electricity sector. The findings suggest that coordinated policies combining market integration, low-carbon transition strategies, and stable financing mechanisms can enhance system resilience. Full article

We employ an empirical framework for real estate securities that incorporates portfolio optimization, return distribution tail diagnostics, risk metrics, modeling of long-range dependence in return volatility, regression against benchmark indices, and option pricing, treating these as necessary layers of a risk-management structure that concentrates on downside risk. Optimization compared mean–variance against downside-sensitive conditional value at risk. Tail behavior was assessed via skewness, kurtosis, and extreme value theory; volatility persistence was examined using ARMA–FIGARCH models. Benchmark dependence was examined via the capital asset pricing model (CAPM), employing endogenous and exogenous market proxies. Insurance instruments via European options were priced using a doubly subordinated normal inverse Gaussian pricing model capable of modeling skewed, heavy-tailed return distributions. Significant findings for the optimized portfolios include return distributions with losses that are heavier-tailed than gains; a transition in time from moderate-to-high long-range dependence in conditional volatility; smaller values of CAPM “alpha” and “beta” for minimum-risk portfolios compared to tangent portfolios; and significant implied volatility values. Full article

Under the combined impacts of climate change and urbanization, flood disasters have exhibited increasing non-stationarity, low-frequency but high-impact characteristics, and enhanced spatial dependence. Traditional indemnity-based flood insurance has certain limitations in claim efficiency and loss assessment. In contrast, index-based flood insurance, characterized by objective triggering mechanisms, rapid claim settlement, and low operational costs, has gradually become an important tool for flood catastrophe risk management. Based on a literature review approach, this study systematically reviews the index system, pricing mechanisms, and basis risk of index-based flood insurance, and provides a comprehensive analysis from the perspectives of index construction, threshold determination, and payout design. The results indicate that index systems have evolved from single hazard indicators to coupled indices integrating hazard characteristics and loss information, and multiple pricing approaches have been developed, including fixed, linear, piecewise payout, and probabilistic payout schemes (payouts determined by loss probabilities rather than fixed thresholds). Among the reviewed approaches, inundation-area-based indices generally show stronger consistency with actual losses at urban scales, whereas precipitation-based indices are more suitable for large-scale regional applications due to their rapid triggering capability. However, basis risk remains a critical issue, mainly arising from index errors, spatial scale mismatches, and inappropriate threshold settings. Therefore, to address the identified limitations of basis risk, threshold uncertainty, and spatial mismatches, future research should focus on multi-dimensional risk indices, dynamic threshold setting, and optimized spatial risk zoning, as well as the integration of remote sensing and machine learning methods to improve the consistency between indices and actual losses. The findings provide practical guidance for insurers in product design, for policymakers in regional flood risk financing, and for disaster managers in improving climate adaptation strategies. Full article

Crop insurance has to be priced and screened before the season’s main losses are known. This paper asks how far an insurer can get using only the information already available when a contract is enrolled: where the farm is, what crop and practice are insured, the chosen coverage level, and the local hail rate history. Using administrative records from 2006 to 2024, we build a practical underwriting score that separates the chance of a claim from the likely size of a loss, then test it on recent years and compare it with simpler rules and alternative models. The score ranks contracts better than regional averages, hail rate rules, or premium-based sorting, and the highest-ranked fifth of contracts contains about one third of the realized losses. Still, it misses much of the most severe loss risk. Coverage choice helps with prediction but also reflects farmer decisions, and thus the score should be read as a contract risk measure rather than a causal measure of hazard. These results suggest an enrollment-time information constraint for the specifications tested here, while leaving room for richer administrative and hazard-based extensions. Better tail prediction may require weather, farm history, and spatial information not used in the strict score. Full article

Insurance pricing plays a central role in risk management and financial decision-making, as accurate premium estimation directly impacts portfolio stability and profitability. This study investigates insurance pure premium estimation by integrating classical actuarial models with modern machine learning techniques. We compare the traditional frequency–severity decomposition framework with direct modeling approaches, including XGBoost and Tweedie models. For claim frequency, we evaluate Poisson-based models, generalized additive models, and XGBoost. For claim severity, we compare a Gamma generalized linear model with XGBoost. The results show that XGBoost improves predictive performance for both components based on the evaluation metrics considered. Within the decomposition framework, the XGBoost–XGBoost model achieves the lowest prediction error among the models considered. However, lift-based analysis reveals that the XGBoost–Gamma model provides superior risk segmentation, highlighting a trade-off between prediction accuracy and risk ranking. Direct modeling approaches, while competitive, do not consistently achieve lower error than the decomposition framework across the evaluation metrics considered. Overall, the findings demonstrate that machine learning enhances predictive performance, but its effectiveness is maximized within the frequency–severity framework. The results highlight the importance of both frequency and severity modeling in insurance pricing, while suggesting that their relative contributions to risk segmentation depend on model specification and evaluation criteria. These findings have important implications for risk management and pricing strategies in insurance portfolios. Full article

This study investigates the impact of jointly modeling jumps in asset prices and mortality rates on the valuation of insurance guarantees. Mortality dynamics are specified using two extended frameworks based on the classical Lee–Carter model, with and without the inclusion of jump components. Financial asset returns are modeled using Merton jump–diffusion processes. In the proposed specification, asset prices evolve according to a two-regime Merton model, where the regimes correspond to pandemic and non-pandemic market conditions. Using historical mortality data for the U.S. population and financial market data from the S&P 500 index, we evaluate the pricing implications for a Guaranteed Minimum Death Benefit (GMDB) rider. Contract values and Greeks are computed across multiple issue ages and policy maturities. The empirical results highlight the importance of accounting for simultaneous mortality and market jumps and demonstrate that their interaction has a material effect on the valuation of GMDB products. Full article

As climate risks intensify and urbanization accelerates, cities face growing challenges in safeguarding infrastructure, livelihoods, and public well-being. Sustainable insurance has emerged as a key tool for mitigating climate-related risks; however, existing models often lack integration with smart city frameworks and climate resilience strategies. This study conducts a bibliometric review to map the global research landscape of sustainable insurance in climate-resilient smart cities, providing insights into emerging trends, thematic clusters, and knowledge gaps. Using data from the Scopus database and VOSviewer-based keyword co-occurrence analysis, this study identifies four key research clusters: economic-policy integration, climate risk governance, digital urban innovation, and health within the SDG framework. The findings reveal that emerging models such as parametric insurance, microinsurance, and data-driven pricing can align financial protection with real-time climate risks, incentivizing resilience investments and expanding coverage to vulnerable communities. These clusters illustrate the field’s transition toward systems-based approaches, highlighting the need for integrated solutions that blend financial, technological, and social dimensions of resilience. Study recommendations emphasize the integration of insurance into urban planning, the expansion of public–private partnerships, regulatory modernization, and the use of smart city data for dynamic risk pricing. This research offers implications for insurers, governments, urban planners, and development agencies, and positions insurance as a cross-cutting enabler that bridges ESG principles, digital governance, and inclusive sustainability. Full article

Accurate mortality prediction is essential to actuarial practice as it is directly linked to insurance pricing, reserving, and the management of longevity risk. This study proposes a deep neural network (DNN) model for the mortality rates of multiple populations; it is composed of long short-term memory (LSTM) and convolutional neural network (CNN) components. As mortality trends evolve over long time horizons, and as capturing the complex dependencies among mortality rates across countries or regions with a linear model is challenging, the LSTM and CNN were applied to mortality modeling. The former can automatically learn long-term dependencies of sequential data, whereas the latter can extract local features from grid or sequential data. Formulated as a nonlinear generalization of the Lee–Carter decomposition, the model maps the log-mortality matrix $logM$ to future $logm(x,t)$ end-to-end and generates multi-step forecasts through dynamic recursive prediction. Then, the DNN and baseline models were used to fit mortality data of 21 countries from the Human Mortality Database (HMD), which were divided into training and test sets with the year 2000 as the split point. Extensive numerical experiments from the perspectives of accuracy, stability, and reliability of long-term forecasting revealed that DNN models yield better predictive performance, particularly the LSTM-CNN model. It combines the LSTM, CNN, and fully connected network (FCN) layers and thus exploits each deep neural network to fit nonlinear age, period, and cohort effects as well as their interactive terms to achieve better predictive performance. However, the CNN still outperformed other models for certain groups. In addition, the conclusions hold for remaining life expectancy. Full article

This study introduces a new two-parameter exponential (TPEX) model for modeling skewed phenomena and risk analysis, motivated by the need for flexible yet tractable models capturing asymmetric behavior in actuarial, financial, and reliability data. An extensive simulation study evaluated seven estimation procedures: maximum likelihood estimation (MLE), ordinary least squares (OrLS), weighted least squares (WLSQ), Cramér–von Mises (CVM), Anderson–Darling estimation (ADE), Kolmogorov estimation (KE), L-moments, and Bayesian estimation, comparing bias, efficiency, and stability across sample sizes and parameter settings. Four real-data applications were conducted: two comparing estimation methods on relief and survival datasets and two assessing competitive performance against exponential-type models. Key risk indicators (KRIs), including the Value at Risk (VaR), Tail Value at Risk (TVaR), Tail Variance (TV), Tail Mean–Variance (TMV), and expected loss (EL), were computed using UK motor non-comprehensive claims and US house price data, illustrating the model’s relevance for insurance reserving and market risk assessment. Full article

This paper evaluates a Sustainable Energy Efficiency Business Model (SEEBM) for small and medium sized enterprises (SMEs) in the European industrial sector. The sustainability-oriented model, developed by the authors, combines Energy Performance Contracting (EPC), Energy Supply Contracting (ESC), and Energy Saving Insurance (ESI) within a unified framework to support industrial decarbonisation. The study identifies key performance indicators and translates them into a System Dynamics model using a Design-Based Research approach. The model is built from secondary data drawn from 45 SME case studies in the European SMEmPower project and is validated through extreme condition testing and behavioural sensitivity analysis. Results indicate that the integrated model significantly enhances financial performance, reducing the average payback period from average 36 months to 10 months. Sensitivity analysis highlights the influence of contract duration, energy saving rates, and energy prices on both payback and emissions reduction outcomes. This research introduces a novel dynamic framework integrating EPC, ESC, and ESI, enabling time-based evaluation of investment viability and environmental impact. It offers a replicable decision support tool for policymakers and market actors seeking scalable, low risk pathways to SME decarbonisation. Overall, the model provides practical insights for improving investment decisions while accelerating the transition toward sustainable industrial systems across Europe. Full article

Nigeria entered the 2020 COVID-19-related oil price downturn without the fiscal buffers that numerous resource-rich economies had built over time. Despite heavy dependence on petroleum revenues, the country has made limited use of stabilization tools such as structured hedging programs, sovereign savings mechanisms, or strategic reserves, leaving public finances exposed to external shocks. Drawing on political choice theory and the resource governance literature, this study examines how institutional conditions shaped crisis management during the 2020 oil price collapse and the COVID-19 pandemic. The study combines qualitative institutional analysis with a stochastic counterfactual simulation. It compares Nigeria’s policy approach with those of oil-producing countries including Mexico, Saudi Arabia, the United Arab Emirates, Angola, and Ghana, using data from the IMF, World Bank, Afreximbank, and peer-reviewed sources. The counterfactual simulation is calibrated to Nigeria’s 2019 federal budget oil benchmark of US $60 per barrel, with the IMF’s 2019 petroleum price assumption used as a robustness check. The model treats hedging as a form of partial fiscal insurance rather than full stabilization. Results suggest that hedging sufficient to offset 10%, 20%, and 30% of the shock would have improved 2020 GDP decline from −1.80% to approximately −1.62%, −1.44%, and −1.26%, respectively. The analysis identifies institutional gaps in Nigeria’s use of hedging, sovereign savings, and reserve infrastructure. The counterfactual results indicate that even modest oil hedging could have meaningfully softened the 2020 downturn, with the 20% scenario reducing GDP contraction by an estimated 0.36 percentage points. These findings suggest that governance constraints contributed materially to fiscal vulnerability. The study proposes a four-pillar framework centered on risk hedging, revenue savings, strategic investment, and institutional reform to strengthen fiscal stability and resilience to external shocks. Full article