Search Results (27)

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. Full article

A Rank-Based Copula-Adjusted Mann–Kendall (R-CaMK) is proposed, with an end-to-end mathematical and computational framework that integrates rank-based multivariate dependence modelling (regular vines where data permit, Gaussian copula fallback otherwise), parametric spatial bootstrap for calibrated Mann–Kendall inference, and integer programming for budgeted sensor selection. At each site, the deterministic trend is removed, AR(1) margins are fitted, and residuals are transformed to ranks; the joint rank structure is modelled via R-vines or a Gaussian copula. Spatially coherent null series are simulated from the fitted model to estimate Var$\left(S\right)$ for the Mann–Kendall S-statistic and to compute empirical p-values. A detection score $w_j$ is defined and an integer linear programme (ILP) is solved to select sensors under cost/budget constraints. Simulation experiments show improved Type-I control and realistic power estimation relative to standard corrections; an application to seven long annual maximum flow sites in New South Wales demonstrates calibrated inference and operational selection decisions. Full article

Accurate modelling of near surface wind speeds is essential for robust resource assessment, turbine design, and grid integration. This study presents a unified framework comparing four candidate marginal distributions—Weibull, Gamma, Lognormal, and Generalised Extreme Value (GEV)—across 21 years of daily observations from 11 sites in New South Wales and southern Queensland, Australia. Parameters are estimated by maximum likelihood, with L-moments used when numerical fitting fails. Univariate goodness-of-fit is evaluated via information criteria (Akaike Information Criterion, AIC; Bayesian Information Criterion, BIC) and distributional tests (Anderson–Darling, Cramér–von Mises, Kolmogorov–Smirnov). To capture spatial dependence, we fit an 11-dimensional regular vine (“R-vine”) copula to the probability-integral-transformed data, selecting pair-copula families by AIC and estimating parameters by sequential likelihood. A composite score (70% univariate, 30% copula) ranks distributions per location. Results demonstrate that Lognormal best matches central behaviour at most sites, Weibull remains competitive for bulk modelling, Gamma often excels in moderate tails, and GEV best represents extremes. All turbine yield results presented are illustrative, showing how statistical choices impact energy estimates; they should not be interpreted as operational forecasts. In a case study, 5000 joint simulations from the top-two models drive IEC V90 and E82 power curves, revealing up to 10% variability in annual energy yield due solely to marginal choice. This workflow provides a replicable template for comprehensive wind resource and load hazard analysis in complex terrains. Full article

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. Full article

For a long time, drought disasters have brought about a wide range of negative impacts on human socio-economics. Especially in large basins with many tributaries, once hydrological drought occurs synchronously in several tributaries, the hydrological drought condition in the mainstream will be aggravated, which will lead to more serious losses. However, there is still a lack of research on the probabilistic risk of simultaneous hydrologic droughts in various areas of large watersheds. In this study, the Standardized Runoff Index was used to characterize hydrological drought, and the Standardized Runoff Index (SRI) sequence characteristics of each region were analyzed. Subsequently, a multiregional hazard encounter probability distribution model with an R-vine structure was constructed with the help of the vine copula function to study the risk pattern of simultaneous hydrological drought in multiple tributaries under environmental changes. The model results showed that the probability of the four basins gradually decreased from 7.5% to 0.16% when the SRI changed from ≤−0.5 to ≤−2.0, indicating that the likelihood of the joint distribution of the compound disaster decreases with increase in the drought extremes. Meanwhile, the probability of hydrological drought in the three major basins showed significant spatial differences, and the risk ranking was Dongting Lake Basin > Poyang Lake Basin > Han River Basin. The model constructed in this study reveals the disaster risk law, provides theoretical support for the measurement of hydrological drought risk in multiple regions at the same time, and is of great significance for the prediction of compound drought disaster risk. Full article

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. Full article

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. Full article

With the growing global emphasis on sustainable development goals, Environmental, Social, and Governance (ESG) factors have emerged as critical considerations in shaping economic policies and strategies. This study employs the ARMA-eGARCH-skewed t and Vine Copula models, combined with the CoVaR method, to investigate the dependence structure and risk spillover pathways across various industrial sectors in China within the ESG framework. By modeling the complex interdependencies among sectors, this research uncovers the relationships between individual industries and the ESG benchmark index, while also analyzing the correlations across different sectors. Furthermore, this study quantifies the risk contagion effects across distinct industries under extreme market conditions and maps the pathways of risk spillovers. The findings highlight the pivotal role of ESG considerations in shaping industrial structures. Empirical results demonstrate that industries such as agriculture, energy, and manufacturing exhibit significant systemic risk characteristics in response to ESG fluctuations. Specifically, the identified risk spillover pathway follows the sequence: agriculture → consumption → ESG → manufacturing → energy. The CoVaR values for agriculture, energy, and manufacturing indicate a significant potential for risk contagion. Moreover, sectors such as real estate, finance, and information technology exhibit significant risk spillover effects. These findings offer valuable empirical evidence and a theoretical foundation for formulating ESG-related policies. This study suggests that effective risk management, promoting green finance, encouraging technological innovation, and optimizing industrial structures can significantly mitigate systemic risks. These measures can contribute to maintaining industrial stability and fostering sustainable economic development. Full article

This study investigates systemic risk, return patterns, and diversification within the Johannesburg Stock Exchange (JSE) during the COVID-19 pandemic, utilizing data-centric approaches and the ARMA-GARCH vine copula-based conditional value-at-risk (CoVaR) model. By comparing three investment strategies—industry sector-based, asset risk–return plot-based, and clustering-based—this research reveals that the industrial and technology sectors show no ARCH effects and remain isolated from other sectors, indicating potential diversification opportunities. Furthermore, the analysis employs C-vine and R-vine copulas, which uncover weak tail dependence among JSE sectors. This finding suggests that significant fluctuations in one sector minimally impact others, thereby highlighting the resilience of the South African economy. Additionally, entropy measures, including Shannon and Tsallis entropy, provide insights into the dynamics and predictability of various portfolios, with results indicating higher volatility in the energy sector and certain clusters. These findings offer valuable guidance for investors and policymakers, emphasizing the need for adaptable risk management strategies, particularly during turbulent periods. Notably, the industrial sector’s low CoVaR values signal stability, encouraging risk-tolerant investors to consider increasing their exposure. In contrast, others may explore diversification and hedging strategies to mitigate risk. Interestingly, the industry sector-based portfolio demonstrates better diversification during the COVID-19 crisis than the other two data-centric portfolios. This portfolio exhibits the highest Tsallis entropy, suggesting it offers the best diversity among the types analyzed, albeit said diversity is still relatively low overall. However, the portfolios based on groups and clusters of sectors show similar levels of diversity and concentration, as indicated by their identical entropy values. Full article

Recently, the introduction of Environmental, Social, and Governance (ESG) scores has become crucial for investment decisions and in minimizing portfolio risk. This study aims to understand the relationship between ESG scores and Value-at-Risk (VaR), computed by using a Vine copula–GARCH based approach, chosen for its reliability in detecting interdependencies among multiple stocks. In fact, one of the main challenges in estimating VaR for a stock portfolio is capturing the dependence structure among a large number of assets. The dataset consists of 16 companies listed on the FTSE100 index. The corresponding ESG scores were collected over a comprehensive period of five years, from 2018 to 2022, covering both normal and stressed market conditions. Additionally, a focused analysis was conducted for the period from 2020 to 2022 to isolate the specific effects of the COVID crisis. The results indicate that an increase in assets with the highest ESG scores reduces potential losses in the portfolio. This finding underscores the importance of integrating high-level ESG scores into portfolios to mitigate market risk. Additionally, during periods characterized by stressed market conditions, the impact of ESG scores on VaR is even more pronounced, demonstrating that sustainable assets are more resilient in times of crisis. Full article

Floods are a common natural disaster whose severity in terms of duration, water resource volume, peak, and accumulated rainfall-based damage is likely to differ significantly for different geographical regions. In this paper, we first propose a novel hourly flood index ($SWR{I}_{24-hr-S}$) derived from normalising the existing 24-hourly water resources index ($WR{I}_{24-hr-S}$) in the literature to monitor flood risk on an hourly scale. The proposed $SWR{I}_{24-hr-S}$ is adopted to identify a flood situation and derive its characteristics, such as the duration (D), volume (V), and peak (Q). The comprehensive result analysis establishes the practical utility of $SWR{I}_{24-hr-S}$ in identifying flood situations at seven study sites in Fiji between 2014 and 2018 and deriving their characteristics (i.e., D, V, and Q). Secondly, this study develops a vine copula-probabilistic risk analysis system that models the joint distribution of flood characteristics (i.e., D, V, and Q) to extract their joint exceedance probability for the seven study sites in Fiji, enabling probabilistic flood risk assessment. The vine copula approach, particularly suited to Fiji’s study sites, introduces a novel probabilistic framework for flood risk assessment. The results show moderate differences in the spatial patterns of joint exceedance probability of flood characteristics in different combination scenarios generated by the proposed vine copula approach. In the worst-case scenario, the probability of any flood event occurring where the flood volume, peak, and duration are likely to exceed the 95th-quantile value (representing an extreme flood event) is found to be less than 5% for all study sites. The proposed hourly flood index and the vine copula approach can be feasible and cost-effective tools for flood risk monitoring and assessment. The methodologies proposed in this study can be applied to other data-scarce regions where only rainfall data are available, offering crucial information for flood risk monitoring and assessment and for the development of effective mitigation strategies. Full article

Financial market data are abundant with outliers, and the search for an appropriate extreme value theory (EVT) approach to apply is an endless debate in the statistics of extremes research. This paper uses EVT methods to model the five-year daily all-share total return index (ALSTRI) and the daily United States dollar (USD) against the South African rand (ZAR) exchange rate of the Johannesburg stock exchange (JSE). The study compares the block maxima approach and the peaks-over-threshold (POT) approach in terms of their ability to model financial market data. The 100-year return levels for the block maxima approach were found to be almost equal to the maximum observations of the financial markets of 10,860 and R18.99 for the ALSTRI and the USD–ZAR, respectively. For the peaks-over-threshold (POT) approach, the results show that the ALSTRI and the USD–ZAR exchange rate will surpass 17,501.63 and R23.72, respectively, at least once in 100 years. The findings in this study reveal a clear distinction between block maxima and POT return level estimates. The POT approach return level estimates were comparably higher than the block maxima estimates. The study further revealed that the blended generalised extreme value (bGEVD) is more suitable for relatively short-term forecasting, since it cuts off at the 50-year return level. Therefore, this study will add value to the literature and knowledge of statistics and econometrics. In the future, more studies on bGEVD, vine copulas, and the r-largest-order bGEVD can be conducted in the financial markets. Full article

With the accelerated pace of financial globalization and the gradual increase in linkages among financial markets, correctly identifying and describing the risk spillover and network diffusion in the financial system is extremely important for the prevention and management of systemic risk. Based on this, this paper takes the equity markets of 17 countries around the world from 2007 to 2022 as the research object, measures the volatility spillover effect of global financial markets using R-Vine Copula and the DY spillover index, constructs the volatility spillover network of global financial markets, discovers the spillover and diffusion pattern of global financial market risks, and provides relevant suggestions for systemic risk management. It is found that (1) there are certain aggregation characteristics in the network diffusion of global financial market volatility spillover; (2) developed European countries such as the Netherlands, France, the UK, and Germany are at the center of the network and have a strong influence; (3) Asian countries such as China, Japan, and India are at the periphery of the network; and (4) shocks from crisis events enhance the global financial market volatility spillover effect. Based on the above findings, effective prevention of global financial market risk volatility spillover and network diffusion and reduction in systemic risk need to be carried out in two ways. First, by focusing on the financial markets of key countries in the network, such as the Netherlands, the UK, France, and Germany. The second approach is to mitigate the uneven development in global financial markets and reduce the high correlation among them. Full article

The rationality of heavy vehicle models is crucial to the structural safety assessment of bridges. To establish a realistic heavy vehicle traffic flow model, this study proposes a heavy vehicle random traffic flow simulation method that fully considers the vehicle weight correlation based on the measured weigh-in-motion data. First, a probability model of the key parameters in the actual traffic flow is established. Then, a random traffic flow simulation of heavy vehicles is realized using the R-vine Copula model and improved Latin hypercube sampling (LHS) method. Finally, the load effect is calculated using a calculation example to explore the necessity of considering the vehicle weight correlation. The results indicate that the vehicle weight of each model is significantly correlated. Compared to the Monte Carlo method, the improved LHS method better considers the correlation between high-dimensional variables. Furthermore, considering the vehicle weight correlation using the R-vine Copula model, the random traffic flow generated by the Monte Carlo sampling method ignores the correlation between parameters, leading to a weaker load effect. Therefore, the improved LHS method is preferred. Full article

Copulas are a quite flexible and useful tool for modeling the dependence structure between two or more variables or components of bivariate and multivariate vectors, in particular, to predict losses in insurance and finance. In this article, we use the VineCopula package in R to study the dependence structure of some well-known real-life insurance data and identify the best bivariate copula in each case. Associated structural properties of these bivariate copulas are also discussed with a major focus on their tail dependence structure. This study shows that certain types of Archimedean copula with the heavy tail dependence property are a reasonable framework to start in terms modeling insurance claim data both in the bivariate as well as in the case of multivariate domains as appropriate. Full article