Reliability assessment of systems with multiple performance characteristic by fusing random effects wiener processes with R-vine Copula

This research examines the long-term and nonlinear dependency patterns between Türkiye’s BISTTRKY index and the stock markets of four GCC nations—Saudi Arabia (TDWL), Qatar (QTRGE), UAE (DFM), and Oman (MSM30)—using data spanning from May 2010 to March 2025. By applying Johansen cointegration tests and the Vector Error Correction Model (VECM), the study identifies significant long-term equilibrium connections, notably strong between BISTTRKY and TDWL, QTRGE, and DFM. To address complex and asymmetric interdependencies beyond linear associations, R-vine copula models are employed, which surpass traditional copulas in detecting tail dependencies and dynamic risk linkages. The results indicate that although regional stock markets show varying levels of integration with BISTTRKY, nonlinear dependencies—particularly during extreme market conditions—restrict the potential for portfolio diversification. These findings hold significant implications for investors pursuing cross-border hedging strategies and for policymakers focused on managing systemic risks and enhancing regional financial collaboration. While the study offers solid empirical evidence, it is constrained by its omission of macroeconomic variables and the assumption of static dependence structures. Future research could investigate dynamic copula models, include external factors, and broaden the analysis to other emerging markets for a more thorough understanding of regional financial interconnectedness.

A framework for 3D direct sampling-based environmental contours of wind, wave, and current using ABKP model and R-vine copula

Joint probability aggregation for regional wind power forecasting via R-vine copula and Kolmogorov-Arnold networks

Occupational stress is a well-studied topic, and recent advances in wearable technologies have enabled a way to analyse continuous signals that respond to indicators of stress such as heart rate (HR), electrodermal activity (EDA), skin temperature (TEMP), among others. While studies have successfully classified various stress levels, few studies have been conducted to understand the interdependence between the physiological signals. This study aimed to analyse the interdependence of these multivariate signals using the copula method; the R-vine copula method can capture the interconnection among these signals and provide conditional probability. We analysed the physiological stress signals of 15 Covid nurses’ stress classified into three levels. The analyses indicated that EDA mostly correlates with skin temperature, with Kendall’s tau value (∼ 0.43-0.45) and Spearman’s Rho value (∼ 0.60-0.66) for various stress levels. EDA and heart rate were also seen to vary with stress, indicating a correlation between them. The study revealed that for HR over 100bpm, there is an 80% probability that the EDA value would be around 2.5 μs for no-stress, 4 μs for medium stress and 6 μs for high stress conditions. The study also uncovered the multivariate dependency among the three indicators (HR, EDA, and TEMP) that determines an individual’s stress level by providing the likelihood of a certain stress level. The bootstrap-based confidence interval provided parameter uncertainty quantification whereas a comparison with the logistic regression method has provided further interpretability and confidence in the current study. Similar studies using copulas can also be helpful to understand the dependencies of other variables on stress and, consequently, on various mental disorders.

This study proposes Bayesian estimation of multivariate regular vine (R-vine) copula models with generalized autoregressive conditional heteroskedasticity (GARCH) margins modeled by Gaussian-mixture distributions. The Bayesian estimation approach includes Markov chain Monte Carlo and variational Bayes with data augmentation. Although R-vines typically involve computationally intensive procedures limiting their practical use, we address this challenge through parallel computing techniques. To demonstrate our approach, we employ thirteen bivariate copula families within an R-vine pair-copula construction, applied to a large number of marginal distributions. The margins are modeled as exponential-type GARCH processes with intertemporal capital asset pricing specifications, using a mixture of Gaussian and generalized Pareto distributions. Results from an empirical study involving 100 financial returns confirm the effectiveness of our approach.

A water-energy-food-land nexus framework for multi-objective optimization and risk assessment integrating deep reinforcement learning and Copula-based modeling.

Compound dry/wet and hot extremes decreased wheat/maize yield revealed by SHAP-RF and R-Vine Copula

To address the challenges posed by multi-source and high-dimensional variable dependencies in the service process of bogie frames, a method based on the R-Vine Copula model is developed. A finite element model of the bogie frame is developed, and its static strength under exceptional conditions is evaluated according to the UIC615-4 standard. A parameterized model is constructed using APDL scripting, followed by a design of experiments and Sobol sensitivity analysis to identify design variables exhibiting strong interactions. An optimal R-vine Copula structure tree is established, and the associated copula parameters are selected through maximum likelihood. Optimal Vine Copula functions are selected and validated using the AIC criterion. Variable correlation data consistent with the optimal marginal distribution functions are generated via the Vine Copula sampling algorithm. The failure function of the bogie frame serves for reliability evaluation in combination with an improved RBF surrogate model, considering both independent and correlated variable assumptions. Comparison of the reliability results demonstrates the effectiveness of the proposed method, providing a more realistic assessment of the bogie frame’s structural reliability under actual service conditions.

Time-Variant Reliability Prediction Considering Multiple Failure Modes Based on R-Vine Copula

This study develops an integrated risk modeling framework to assess capital adequacy and optimize portfolio performance for Thai life and non-life insurers. Leveraging ARMA–GJR–GARCH models with skewed Student-t innovations, extreme value theory, and dynamic R-vine copulas, the framework effectively captures volatility, tail risks, and evolving asset interdependencies. Utilizing daily data from 2014 to 2024, the models generate value-at-risk forecasts consistent with international standards such as Basel III’s 10-day 99% VaR and rolling Sharpe ratios for portfolios integrating green bonds compared to traditional asset allocations. The results demonstrate that green bonds, fixedincome instruments funding renewable energy and other environmental projects, significantly improve risk-adjusted returns and have the potential to reduce capital requirements, particularly for life insurers with long-term sustainability mandates. These findings underscore the importance of portfolio-level capital assessment and support the proactive integration of ESG considerations into supervisory investment guidelines to enhance financial resilience and align the insurance sector with Thailand’s sustainable finance agenda.

Multi-scenario stochastic assessment of operational risk of integrated energy system based on R-vine Copula

Generation of realistic synthetic data has garnered considerable attention in recent years, particularly in the health research domain due to its utility in, for instance, sharing data while protecting patient privacy or determining optimal clinical trial design. While much work has been concentrated on synthetic image generation, generation of realistic and complex synthetic tabular data of the type most commonly encountered in classic epidemiological or clinical studies is still lacking, especially with regard to generating data for randomized controlled trials (RCTs). There is no consensus regarding the best way to generate synthetic tabular RCT data such that the underlying multivariate data distribution is preserved. Motivated by an RCT in the treatment of Human Immunodeficiency Virus, we empirically compared the ability of several strategies and three generation techniques (two machine learning, the other a more classical statistical method) to faithfully reproduce realistic data. Our results suggest that using a sequential generation approach with an R-vine copula model to generate baseline variables, followed by a simple random treatment allocation to mimic the RCT setting, and subsequent regression models for variables post-treatment allocation (such as the trial outcome) is the most effective way to generate synthetic tabular RCT data that capture important and realistic features of the real data.

This study develops an integrated risk modeling framework to assess capital adequacy and optimize portfolio performance for Thai life and non-life insurers. Combining ARMA–GJR–GARCH models with skewed Student-t innovations, extreme value theory, and dynamic R-vine copulas captures volatility, tail risks, and evolving asset interdependencies. Using daily data from 2014 to 2024, the models generate value-at-risk forecasts and rolling Sharpe ratios for portfolios with and without green bonds. The results show that green bond inclusion improves risk-adjusted returns and reduces capital requirements, particularly for life insurers, aligning with their long-term solvency mandates. Although a greenium effect is not clearly observed relative to Thai sovereign bonds, green bonds enhance diversification within a multivariate framework. These findings highlight the importance of evaluating capital requirements at the portfolio level and suggest that regulators incorporate ESG considerations into supervisory investment guidelines to strengthen financial resilience and align the insurance sector with Thailand’s sustainable finance goals.

This study develops an integrated risk modeling framework to assess capital adequacy and optimize portfolio performance for Thai life and non-life insurers. Combining ARMA–GJR–GARCH models with skewed Student-t innovations, Extreme Value Theory, and dynamic R-vine copulas captures volatility, tail risks, and evolving asset interdependencies. Using daily data from 2014 to 2024, the models generate Value-at-Risk forecasts and rolling Sharpe ratios for portfolios with and without green bonds. Results show that green bond inclusion improves risk-adjusted returns and reduces capital requirements, particularly for life insurers, aligning with their long-term solvency mandates. Although a greenium effect is not clearly observed relative to Thai sovereign bonds, green bonds enhance diversification within a multivariate framework. These findings highlight the importance of evaluating capital requirements at the portfolio level and suggest that regulators incorporate ESG considerations into supervisory investment guidelines to strengthen financial resilience and align the insurance sector with Thailand’s sustainable finance goals.

Exploring the combined effects of drought and drought-flood abrupt alternation on vegetation using interpretable machine learning model and r-vine copula function

As labour costs in China increase, labour-intensive industries are migrating to ASEAN countries, attracted by lower labour costs and market potential. This shift not only affects the economies of China and ASEAN but also reshapes the global manufacturing landscape. This paper investigates the correlation and spillover of supply chain risks using production exposure indicators derived from inter-country input-output data and the R-Vine Copula model. We assess the risk spillover of each country within the global supply chain. Our findings indicate that industrial relocation can significantly alter supply chain structures, thereby affecting the concentration and direction of risks. While China's role in the global textile supply chain has diminished, it remains significant, with notable increases in the roles of Vietnam and Thailand. This research reveals the supply chain risks between China and ASEAN in the context of industrial transfer, exploring the sustainable development of production linkages and sustainable economic cooperation among countries.

Weather derivative markets, particularly Chicago Mercantile Exchange (CME) Heating Degree Day (HDD) and Cooling Degree Day (CDD) futures, face challenges from complex temperature dynamics and spatially heterogeneous co-extremes that standard Gaussian models overlook. Using daily data from 13 major U.S. cities (2014–2024), we first construct a two-stage baseline model to extract standardized residuals isolating stochastic temperature deviations. We then estimate the Extreme Value Index (EVI) of HDD/CDD residuals, finding that the nonlinear degree-day transformation amplifies univariate tail risk, notably for warm-winter HDD events in northern cities. To assess multivariate extremes, we compute Tail Dependence Coefficient (TDC), revealing pronounced, geographically clustered tail dependence among HDD residuals and weaker dependence for CDD. Finally, we compare Gaussian, Student’s t, and Regular Vine Copula (R-Vine) copulas via joint VaR–ES backtesting. The R-Vine copula reproduces HDD portfolio tail risk, whereas elliptical copulas misestimate portfolio losses. These findings highlight the necessity of flexible dependence models, particularly R-Vine, to set margins, allocate capital, and hedge effectively in weather derivative markets.

In this article, an R-vine copula model is proposed to detect the nonlinear interrelationships between the oil market and five Chinese new-energy-related stock markets from 2017 to 2022, i.e., photovoltaic, new energy vehicles, energy storage, wind power, and nuclear power industries. Firstly, the transmission of downward and upward risk spillover effects (RSEs) is measured from the oil market to the five Chinese new-energy-related stock markets. Subsequently, a CoVaR backtesting methodology is developed to demonstrate the availability of the R-vine copula-CoVaR model. The empirical studies strongly show that the oil market exhibits a significant asymmetric RSE on the five Chinese new-energy-related stock markets. Furthermore, different Chinese new-energy-related stock markets have varying responses to the positive and negative impacts of the oil market. Specifically, the photovoltaic, energy storage, and wind power industries are more sensitive to such adverse effects. However, the new energy vehicle and nuclear power industries are more likely to be positively affected.

Neuronal networks involve 100 billion neurons communicating with each other in a highly nonlinear manner. The neurons in a network transmit information via oscillations called action potentials or spikes. Synchronized oscillations in networks of coupled neurons are also generally observed in neuronal systems and they play an important role in neuronal communication, information processing, and coordination of motor activity. Action potentials between neurons are not always perfectly synchronized and in general, the degree of synchrony is higher if the coupling between oscillators is stronger so that eventually a synchronization threshold may be reached. For subthreshold values of coupling, the oscillators may exhibit intermittent synchronization phenomena, where dynamics is synchronous in some time intervals and not synchronous in others. This partial, intermittent synchrony is also important for biological applications where it may potentially facilitate the high adaptability of biological systems as they react to different environmental impacts. Modeling studies have suggested that channel noise can influence information processing, spike time reliability, stochastic resonance, firing irregularity, subthreshold dynamics, and action potential initiation and propagation in morphologically detailed models. Here, we analyze the intermittent phase-locking in a network of three neurons. Accordingly, we investigated statistically the effect of noise in the intermittent transition to synchronized behavior for coupled neuronal oscillators due to noise integrity. The Hodgkin-Huxley model is a groundbreaking mathematical model that describes the generation and propagation of action potentials in neurons. It was developed by Alan Hodgkin and Andrew Huxley (1952) [3] and has since become a fundamental tool in neuroscience research.