Search Results (359)

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance. Full article

This article provides an overview of the current state-of-the-art in cyber risk and cyber risk management, focusing on the mathematical models that have been created to help with risk quantification and insurance pricing. We discuss the main ways that cyber risk is measured, starting with vulnerability functions that show how systems react to threats and going all the way up to more complex stochastic and dynamic models that show how cyber attacks change over time. Next, we examine cyber insurance, including the structure and main features of the cyber insurance market, as well as the growing role of cyber reinsurance in strategies for transferring risk. Finally, we review the mathematical models that have been proposed in the literature for setting the prices of cyber insurance premiums and structuring reinsurance contracts, analysing their advantages, limitations, and potential applications for more effective risk management. The aim of this article is to provide researchers and professionals with a clear picture of the main quantitative tools available and to point out areas that need further research by summarising these contributions. Full article

Credit-linked notes (CLNs) are vital for transferring and diversifying credit risks in asset securitization, yet their application in China remains limited despite policy support. This paper optimizes China’s CLN pricing mechanism by developing the structured model incorporating the dynamic default boundary and the probability of government implicit guarantees. The model transforms the pricing problem into a semi-unbounded problem via partial differential methods, yielding an explicit pricing solution through Poisson’s formula. Empirical analysis reveals that government implicit guarantees are observed in systemically important institutions in the domestic CLN market and significantly reduce credit risk premiums, with Monte Carlo simulations indicating an approximately positive linear correlation between guarantee probability and CLN prices. Our results demonstrate the dual impact of implicit guarantees—lowering risk premiums while potentially hindering market discipline. This research advances China’s credit derivative pricing theory, offering institutions a pricing tool and further providing policy and practical suggestions for regulatory authorities. Full article

The recent losses and damages due to climate change have destabilized the insurance industry. As global warming is one of the most critical aspects of climate change, it is essential to investigate to what extent greenhouse gas emissions affect the financial stability of insurers. Insurers typically do not emit substantial greenhouse gases directly, while their underwriting and investment activities play a substantial role in enabling companies that do. This article uses panel data regressions to analyze companies in all insurance segments and in all geographic regions of the world from 2004 to 2023. The main finding is that insurers that increase their greenhouse gas emissions become financially unstable. This result is consistent in all three scopes (scope 1, scope 2, and scope 3) of emissions. Furthermore, the findings reveal that this impact is related to reserves and reinsurance. Specifically, reserves increase with greenhouse gas emissions, while premiums ceded to reinsurers decline. Thus, high-emissions insurers retain a significant share of carbon risk and eventually become financially weak. The results encourage several policy recommendations, highlighting the need for instruments that improve the assessment and disclosure of insurers’ carbon footprints. This is crucial to achieving environmental targets and improving the stability of both the insurance market and the economic system. Full article

Beef fat injection technology, used to enhance the perceived quality of lower-grade meat, often results in artificially marbled beef that mimics the visual traits of Wagyu, characterized by dense fat distribution. This practice, driven by the high cost of Wagyu and the affordability of fat-injected beef, has led to the proliferation of mislabeled “Wagyu-grade” products sold at premium prices, posing potential food safety risks such as allergen exposure or consumption of unverified additives, which can adversely affect consumer health. Addressing this, this study introduces a smart sensing system integrated with handheld mobile devices, enabling consumers to capture beef images during purchase for real-time health-focused assessment. The system analyzes surface texture and color, transmitting data to a server for classification to determine if the beef is artificially marbled, thus supporting informed dietary choices and reducing health risks. Images are processed by applying a region of interest (ROI) mask to remove background noise, followed by partitioning into grid blocks. Local binary pattern (LBP) texture features and RGB color features are extracted from these blocks to characterize surface properties of three beef types (Wagyu, regular, and fat-injected). A support vector machine (SVM) model classifies the blocks, with the final image classification determined via majority voting. Experimental results reveal that the system achieves a recall rate of 95.00% for fat-injected beef, a misjudgment rate of 1.67% for non-fat-injected beef, a correct classification rate (CR) of 93.89%, and an F1-score of 95.80%, demonstrating its potential as a human-centered healthcare tool for ensuring food safety and transparency. 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

There is a growing market for plant-based alternative beverages (PBAs) promoted as alternatives to dairy milk. Part of their popularity is that consumers consider them better for both the environment and human health. These perceptions, however, may not be entirely supported by scientific evidence. A holistic comparison of dairy milk and PBAs is difficult because their prices typically do not reflect their environmental and nutritional health impacts, although PBAs tend to be significantly more expensive than dairy milk. Here, we integrate key results from the scientific literature using a True Cost Accounting (TCA) approach to compare dairy milk and five PBAs based on their market retail price and a quantification—and when possible, monetization—of key environmental, nutritional, and social impacts: Global Warming Potential (GWP), dietary risks, and forced labor, respectively. We compare whole dairy milk with five PBAs: soy, almond, oat, coconut, and pea, which account for 97% of retail market sales in the USA. The results show that while environmental, nutritional, and social benefits attributed to PBAs compared to dairy milk exist and can be significant, they are heterogenous, and for some PBAs, they may not be as significant as commonly perceived, particularly when the price premium they command are considered. Full article

In recent decades, the intensification of extreme events, such as floods, earthquakes, and hydrogeological instability, together with the spread of pollutants harmful to health, has highlighted the vulnerability of territories and the need to direct urban policies towards sustainable strategies. The built assets and the real estate sector play a key role in this context; indeed, being among the first ones to be exposed to the effects of climate change, they serve as a crucial tool for the implementation of governance strategies that are more focused on environmental issues. However, the insufficient allocation of public resources to interventions to secure the territory has made it essential to involve private capital interested in combining the legitimate needs of performance with the “ethicality” of the investment. In light of the outlined framework, real estate managers are called upon to take into consideration the environmental risks associated with real estate investments and accurately represent them to investors, especially in the fundraising phase. The tools currently used for the analysis of such risks are based on their perception measured by the “risk premium” criterion, reconstructed on the basis of previous trends and the analyst’s expertise. The poor ability to justify the nature of the risk premium and the uncertainty about future scenario evolutions make this approach increasingly less valid. The present work, starting from the aspects of randomness of the risk premium criterion, aims at its evolution through the inclusion of environmental risk components (seismic, hydrogeological, and pollution). Full article

This research examines the challenges of issuing green bonds due to a lack of established benchmarks. We compare regional differences between the U.S. and the E.U., hypothesizing that issuers of green bonds stand to benefit from comparing them to conventional (black) bonds. As most investors prioritize net positive returns as opposed to intangible sustainability metrics, the existence of a “green premium”, defined as the opportunity to price green bonds differently, remains to be proven. To this end, we employ a time-varying parameter vector autoregression (TVP-VAR), first deriving dynamic variance–covariance matrices and then conducting variance decomposition analysis to gauge connectedness and spillover effects of various bond benchmarks. Implementing multivariate portfolio construction strategies, we investigate the hedging capabilities of green and black bonds. Our findings show that both green and black bonds contribute to portfolio diversification as a risk management strategy. The paper highlights the role played by green bonds in promoting financial stability. Full article

The Black–Scholes model is a fundamental concept in modern financial theory. It is designed to estimate the theoretical value of derivatives, particularly option prices, by considering time and risk factors. In the context of agricultural insurance, this model can be applied to premium determination due to the similar characteristics shared with the option pricing mechanism. The primary challenge in its implementation is determining a fair premium by considering the potential financial losses due to crop failure. Therefore, this study aimed to analyze the determination of rainfall index-based agricultural insurance premiums using the standard and fractional Black–Scholes models. The results showed that a solution to the fractional model could be obtained through the Daftardar-Gejji Laplace method. The premium was subsequently calculated using the Black–Scholes model applied throughout the growing season and paid at the beginning of the season. Meanwhile, the fractional Black–Scholes model incorporated the fractional order parameter to provide greater flexibility in the premium payment mechanism. The novelty of this study was in the application of the fractional Black–Scholes model for agricultural insurance premium determination, with due consideration for the long-term effects to ensure more dynamism and flexibility. The results could serve as a reference for governments, agricultural departments, and insurance companies in designing agricultural insurance programs to mitigate risks caused by rainfall fluctuations. Full article

As global climate change intensifies and carbon emission policies become increasingly stringent, carbon risk has emerged as a crucial factor influencing corporate operations and financial markets. Based on data from A-share listed companies in China from 2009 to 2022, this paper empirically examines the pricing mechanism of carbon risk in the Chinese capital market and explores how different corporate signaling behaviors affect the carbon risk premium. The findings reveal the following: (1) Carbon risk exhibits a significant positive premium (annualized at about 1.33% per standard deviation), which remains robust over longer time windows and after replacing the measurement variables. (2) Heterogeneity analysis shows that the carbon risk premium is not significant in high-energy-consuming industries or before the signing of the Paris Agreement, possibly due to changes in investor expectations and increased green awareness. Additionally, a significant difference in the carbon risk premium exists between brown and green stocks, reflecting a “labeling effect” of green attributes. (3) Issuing green bonds, as an active corporate signaling behavior, effectively mitigates the carbon risk premium, indicating that market investors highly recognize and favor firms that actively convey green signals. (4) A “greenwashing” indicator constructed from textual analysis of environmental information disclosure suggests that greenwashing leads to a mispricing of the carbon risk premium. Companies that issue false green signals—publicly committing to environmental protection but failing to implement corresponding emission reduction measures—may mislead investors and create adverse selection problems. Finally, this paper provides recommendations for corporate carbon risk management and policy formulation, offering insights for both research and practice in the field. Full article

Herbal medicines have significant industrial value in East Asia. Zizyphus jujuba Mill. var. spinosa, used in Korea for treating insomnia, is often confused with Zizyphus mauritiana Lam., which has unverified medicinal properties yet is sold at premium prices. This misclassification undermines consumer trust and poses health risks. This study proposes a deep learning-based classification system trained on RGB-GE data, combining grayscale and edge-detected images with RGB inputs to enhance feature extraction while reducing color-dependency. Our method achieves superior generalization while maintaining cost-effectiveness. The system incorporates Grad-CAM for model interpretation and reliability. By comparing accuracy and speed across basicCNN, DenseNet, and InceptionV3 models, we identified an optimal solution for on-site herbal medicine classification, achieving 98.36% accuracy with basicCNN, ensuring reliable quality control. Full article

Venerupis corrugata (pullet carpet shell) is a premium native clam species in Portugal. This species is highly perishable, typically sold live within 3 or 4 days, posing a significant risk of loss. Therefore, efforts to extend its shelf-life are relevant. The impact of the storage temperature (3, 5, 8 and 12 °C) on clams in plastic net bags and the effect of modified atmosphere packaging (MAP) were investigated. The survival percentage and microbiological and chemical parameters were evaluated, as well as sensory characteristics. The survival percentage and sensory aspects results indicate that the longest time with 95% live clams was observed at 5 °C and 8 °C, but lower temperatures (3 and 5 °C) have lower death rates after the threshold. In the MAP tests, the clams were kept closed due to confinement in plastic trays applying a vacuum, before gas flushing that drew the lid film over the clams. However, a negative effect of CO₂ was observed for clams, with lower survival when packaged in 30% CO₂. The shelf-life increased by only 1–2 days under >70% O₂ with no CO₂. These results show that this species is very sensitive, and MAP is not commercially effective for this application. Full article

This paper introduces a two-phase decision support system based on information theory and financial practices to assist investors in solving cardinality-constrained portfolio optimization problems. Firstly, the approach employs a stock-picking procedure based on an interactive multi-criteria decision-making method (the so-called TODIM method). More precisely, the best-performing assets from the investable universe are identified using three financial criteria. The first criterion is based on mutual information, and it is employed to capture the microstructure of the stock market. The second one is the momentum, and the third is the upside-to-downside beta ratio. To calculate the preference weights used in the chosen multi-criteria decision-making procedure, two methods are compared, namely equal and entropy weighting. In the second stage, this work considers a portfolio optimization model where the objective function is a modified version of the Sharpe ratio, consistent with the choices of a rational agent even when faced with negative risk premiums. Additionally, the portfolio design incorporates a set of bound, budget, and cardinality constraints, together with a set of risk budgeting restrictions. To solve the resulting non-smooth programming problem with non-convex constraints, this paper proposes a variant of the distance-based parameter adaptation for success-history-based differential evolution with double crossover (DISH-XX) algorithm equipped with a hybrid constraint-handling approach. Numerical experiments on the US and European stock markets over the past ten years are conducted, and the results show that the flexibility of the proposed portfolio model allows the better control of losses, particularly during market downturns, thereby providing superior or at least comparable ex post performance with respect to several benchmark investment strategies. Full article

This study empirically investigates the impacts of the Russia–Ukraine war on the performance of brown and green stocks in Europe. Analyzing stocks listed on exchanges in 25 European countries, we find that, prior to this war, stocks of more energy-dependent firms (brown stocks) yielded higher returns compared to those of less energy-dependent firms (green stocks). However, after the unexpected Russian invasion, brown stocks underperformed relative to green stocks. As the conflict reached a stalemate and energy supplies were restored, brown stocks regained their advantage over green stocks. Additionally, brown stocks exhibited greater volatility following the invasion. Utilizing various factor models, we identify a pronounced negative energy risk premium during the initial Russia–Ukraine war outbreak period. This study highlights the dynamic stock market responses to energy supply and regulatory changes in Europe, reflecting the market’s evolving perception of energy supply risks and regulatory risks linked to the transition towards a net-zero economy. Full article