Introduction
The interaction between socioeconomic conditions, lifestyle factors, and mortality is a crucial issue for improving mortality modeling and
forecasting. The traditional Lee–Carter family models capture long-term mortality dynamics through a latent time index but do not explicitly incorporate exogenous determinants. This study proposes an extension of the Lee–Carter framework that integrates a composite
syndemic index derived from multiple socioeconomic and health-related variables. To address multicollinearity and preserve temporal dependence among covariates, a dynamic dimensionality-reduction approach based on Dynamic Principal Component Analysis (dPCA) is introduced.

Methods
Central mortality rates are modeled as a function of age-specific parameters, a latent mortality trend, and an additional exogenous component representing a syndemic index. Identification constraints ensure the uniqueness of the model parameters. A set of socioeconomic,
behavioral, and healthcare indicators for Italy and its regions from 1995 to 2023 is used to determine the multidimensional index. Standard Principal Component Analysis (PCA) and dPCA are applied to reduce the dimensionality of the covariates. While PCA identifies latent factors based on variance maximization, dPCA explicitly accounts for temporal dependence by extracting orthogonal dynamic components from lagged data matrices.

Results
Exploratory analysis reveals a strong clustering of economic and health expenditure variables, with obesity and alcohol consumption associated with higher income levels, while smoking and hospital-based healthcare structures are linked to more traditional socioeconomic contexts. The dPCA-based specification further enhances the robustness of the index by preserving temporal relationships among variables.

Conclusions
Integrating a dynamically derived syndemic index into the Lee–Carter framework improves the representation of mortality dynamics by explicitly linking mortality trends to evolving socioeconomic and health environments. The dPCA approach offers a more robust method for dimensionality reduction, thanks to the preservation of the temporal structure, leading to more stable parameter estimations and a more in-depth interpretation of mortality drivers.

Global warming and climate change have become critical issues for all countries, prompting them to transition from brown (fossil) energy resources to green (clean) energy resources with the aim of achieving a low-carbon economy. However, if financial actors cannot fully anticipate changes in climate policies and regulations, then this situation can trigger climate transition risk. Under certain climate transition scenarios, this new type of financial risk implies a downturn or an upturn in both brown and green stock markets, thereby potentially leading to financial instability in a country.

In this study, we aim to examine whether financial instability is directionally predictable under certain market scenarios related to climate transitions. Financial instability is reflected by a low value of so-called financial stability index (FSI), constructed by integrating financial stability proxies, such as interest rates, exchange rates, yield curves, inflation, and money supply. The FSI construction is carried out using nonlinear principal component analysis (PCA) through some kernel functions. The directional predictability of financial instability is assessed across some quantile levels under climate transition scenarios by proposing some modified versions of the cross-quantilogram. Using data for some selected Asian countries, we reveal significantly positive directional predictability effects, particularly in developed countries.

Anti-Money Laundering (AML) and regulatory compliance are critical components of financial risk management, aimed at preventing financial crimes such as money laundering, fraud, and terrorist financing. The rapid emergence of Decentralized Finance (DeFi) introduces new challenges for AML due to its pseudonymous, borderless, and intermediary-free nature. Traditional monitoring and compliance mechanisms are often insufficient to detect illicit activity in decentralized networks, creating potential vulnerabilities for financial institutions and regulators.

This paper examines AML risks in the context of DeFi, with a focus on staking and other decentralized financial services. DeFi platforms utilize smart contracts and decentralized applications (Dapps) to provide lending, trading, and investment opportunities without central intermediaries. While these systems offer transparency and innovative financial models, they also complicate transaction monitoring, identity verification, and regulatory oversight. Different staking methods, including on-chain staking, liquid staking, and exchange-based staking, illustrate both the operational opportunities and compliance challenges within DeFi networks.

The paper further discusses risk mitigation strategies, including enhanced due diligence, transaction monitoring tools, and integration of regulatory frameworks tailored to decentralized systems. By bridging AML practices with emerging DeFi technologies, this study highlights approaches to ensure financial integrity while leveraging the benefits of decentralized finance.

Introduction
In ageing populations, pension and long-term care are increasingly affected by the joint evolution of longevity, morbidity, and disability. Standard NDC pensions do not explicitly account for changing health conditions after retirement, even though health-related heterogeneity affects both life expectancies and economic needs.

Methods
We propose a health-coningent NDC framework in which benefits can vary with retirees’ current health and disability status. A continuous-time five-state Markov model is used to represent transitions among health, disability, and death. Transition intensities are estimated on U.S. Health and Retirement Study longitudinal data from 1998 to 2020, with states defined using multimorbidity and Activities of Daily Living limitations. Alternative pension designs are evaluated with respect to four evaluation criteria: financial sustainability, actuarial fairness, consistency with health-related economic needs, and homogeneity of benefits within current health states .

Results
The analysis shows that these four criteria cannot, in general, be simultaneously satisfied. A standard NDC design is financially sustainable but generates actuarial unfairness by redistributing pension wealth from shorter-lived, fragile retirees to healthier and longer-lived individuals. Conditioning benefits only on health at retirement restores actuarial fairness at retirement, but creates benefit inhomogeneity over time. Dynamic health-contingent schemes mitigate these shortcomings, but only through explicit trade-offs among redistribution, benefit uniformity, and needs-based protection.

Conclusions
The proposed framework provides a quantitative basis for assessing how NDC pensions can incorporate health- and disability-contingent protection during retirement. It shows that stronger protection for fragile retirees can be financed within the cohort without increasing aggregate expected pension expenditure. However, this implementation requires explicit choices over the trade-off between actuarial fairness, benefit homogeneity, and needs-based redistribution.

The increasing interconnectedness and high dimensionality of modern financial markets have significantly increased the complexity of modelling systemic financial risk. Traditional econometric and volatility models often fail to capture nonlinear dependencies, structural instability, and noise contamination present in large financial datasets. This study proposes an advanced statistical modelling framework that combines spectral methods from Random Matrix Theory with data-driven dynamic modelling using Neural Stochastic Differential Equations to analyze systemic risk in high-dimensional financial systems.

Random Matrix Theory is applied to extract meaningful correlation structures from empirical covariance matrices of financial returns and to distinguish genuine market information from noise arising in large asset universes. This spectral filtering enables more stable estimation of correlation networks and improves the detection of systemic dependencies across financial institutions and assets. To capture the nonlinear stochastic evolution of market dynamics, neural stochastic differential equations are employed to learn drift and diffusion processes governing asset interactions and volatility propagation over time.

The integration of spectral statistical methods with neural stochastic modelling provides a flexible framework for identifying early warning signals of systemic instability and extreme financial events. Empirical experiments on high-dimensional financial datasets demonstrate that the proposed approach improves risk estimation and enhances the detection of emerging systemic vulnerabilities compared with conventional statistical models. By bridging tools from Applied Mathematics, Statistical Modelling, and Quantitative Finance, this research contributes to the development of robust analytical frameworks for systemic financial risk management.

This paper investigates dynamic connectedness between emerging digital assets, Regenerative Finance (ReFi) and AI tokens, and energy markets from 2021 to 2025. Utilizing a DCC-GARCH model with R² decomposition, we analyze time-varying shock transmission among these assets, renewable energy, fossil fuels, and key fear indices (VIX, OVX, GVIZ). This framework captures evolving interdependencies and volatility spillovers across traditional and digital financial ecosystems, providing a comprehensive understanding of cross-market linkages during a period characterized by significant technological advancement and energy transition dynamics.

Our results reveal that ReFi and AI tokens consistently act as net transmitters of volatility shocks to energy markets, with transmission intensity increasing markedly during geopolitical crises and periods of macroeconomic uncertainty. This finding challenges conventional assumptions about digital assets as isolated speculative vehicles and underscores their growing systemic importance. Conversely, fossil fuels remain net receivers throughout the sample period, suggesting their susceptibility to external shocks originating from digital and renewable energy sectors. The total connectedness index surges significantly during market stress, substantially eroding traditional portfolio diversification benefits and highlighting the limitations of conventional risk management frameworks in contemporary financial markets.

Comparative portfolio analysis demonstrates that minimum variance strategies consistently outperform both naive and correlation-based approaches in turbulent market conditions, offering superior capital preservation and enhanced risk-adjusted returns for investors. These findings provide critical insights for institutional investors, portfolio managers, and policymakers navigating the complex intersection of digital finance, technological innovation, and the ongoing energy transition. Understanding these dynamic relationships enables more effective hedging strategies, improved asset allocation decisions, and better regulatory frameworks in an increasingly interconnected financial landscape where digital and traditional assets increasingly coexist and interact.

The central hypothesis of this research posits that enhancing literacy regarding debt obligations significantly attenuates the probability of systemic over-indebtedness and personal insolvency. By bolstering borrower creditworthiness and strategic financial foresight, such literacy reduces the asymmetric information and credit risk perceived by financial institutions, thereby facilitating a substantive and measurable reduction in applied interest rates. This structural mechanism is particularly critical for low-income households, who remain the most susceptible to predatory lending, debt traps, and the compounding effects of escalating financing costs.

To rigorously evaluate this hypothesis, we examine the empirical relationship between targeted financial literacy interventions—specifically those designed to incentivize disciplined debt repayment—and their subsequent longitudinal outcomes on household leverage levels and institutional credit risk metrics. Utilizing a comprehensive dataset from Ecuador encompassing nearly 2,000 individuals tracked between 2023 and 2024, this study employs a quantitative approach to assess the efficacy of financial education in fostering long-term economic stability and inclusive growth. The findings suggest that educational proficiency in financial management acts as a non-traditional collateral, lowering default probabilities and optimizing the credit market's efficiency. Consequently, the study advocates for the integration of financial literacy into public policy as a primary tool for poverty alleviation and the strengthening of the national financial architecture.

Classical risk measures are designed to evaluate the risk of uncertain monetary payoffs (or losses), whereas in time series analysis, risk is typically assessed for logarithmic returns. An axiomatic foundation for this latter perspective has recently been developed under the umbrella of so-called return risk measures (RRMs). In this theory, the positive cone of the linear space of essentially bounded random variables, or subsets of this cone, such as its interior, plays a key role.

Our contribution consists of extending the RRM definition to general ordered vector spaces and characterizing positive homogeneity in terms of the geometric epigraph. Then, to study geometric convexity and establish connections to classical risk measures, we specialize to AM-algebras admitting an order unit. Geometric convexity is also characterized via the geometric epigraph. This setting encompasses a variety of domains, including Euclidean spaces and spaces of multidimensional and essentially bounded random variables. The latter is new in the RRM literature and enables us to study several novel classes of RRMs, including multivariate RRMs, systemic RRMs, and their set-valued versions. We present results regarding standard properties; for example, finiteness, separability and dual representations. Finally, we introduce and discuss concrete kinds of multivariate RRMs and illustrate their performances numerically.

This study estimates the causal effects of traffic crashes on subsequent driving behaviour, focusing on total annual kilometres driven, the proportion of kilometres driven above the posted speed limit, and the share of driving in urban areas and at night. The analysis relies on weekly data provided by an insurtech company, collected over a two-year period.

We implement a difference-in-differences (DiD) quasi-experimental design that evaluates changes in driving outcomes in the weeks following a crash. The treatment group consists of individuals who experienced an at-fault crash during the overall observation period and for whom complete information is available for the design’s observational window, which spans over 25 weeks. The treatment period corresponds to the week in which the crash occurs (week 13), preceded by a 12-week pre-treatment period and followed by a 12-week post-treatment period. The control group comprises individuals who did not experience a crash during the observation period. The causal effects are estimated using a two-way fixed effects (TWFE) panel model that incorporates both additive and multiplicative control covariates. A semiparametric approach based on inverse probability weighted estimation is employed to assess the robustness of the results.

We conclude that being at fault in a crash has a statistically significant negative effect on the four driving outcomes. The largest negative effects one week after the crash are observed in the share of urban and night-time driving. For all four outcomes, these effects gradually diminish and approach zero after twelve weeks. The decline is faster for the share of urban driving. Different driver and vehicle characteristics are associated with distinct behaviour. The effects for young women and men differ with respect to speed outcomes: while the effect for women is more negative one week after the crash, it decreases more rapidly than the corresponding effect for men.

Longevity risk has become increasingly significant in actuarial and financial applications due to improvements in life expectancy and demographic changes. The uncertainty of future mortality trends motivates the use of stochastic mortality models to capture mortality randomness. Building on the foundational Lee−Carter (LC) model and a recent Fourier-based extension for modelling time-varying age patterns, this study proposes an enhanced LC−Fourier model tailored to Malaysian mortality dynamics. Mortality data disaggregated by age and gender from 1980 to 2023 were obtained from the Department of Statistics Malaysia (DOSM). The proposed model decomposes the age-sensitivity parameter of the LC model into a Fourier series of three sinusoidal components with time-varying amplitude and phase parameters. This flexible structure reflects the presence of two splitting ages and three peak-age effects, enabling the model to capture multiple age-specific mortality patterns observed in the Malaysian population. Model performance for both the LC and LC−Fourier frameworks is evaluated using the Mean Absolute Percentage Error (MAPE) for fitted mortality rates and life expectancy at birth across male and female groups. Empirical results demonstrate that the two-split, three-peak LC−Fourier model achieves a superior fit and yields improved mortality projections compared with the baseline LC model, particularly at older ages. The enhancements are most notable for females. These findings highlight the model’s ability to capture structural changes in age-dependent mortality patterns that are not adequately represented by the original LC model.