Impacts of regional dependence and cyclone systems on joint return periods of coastal compound flooding in China

• High-order-moment spillovers between S&P500 and ESG ETFs are tested • A Gram-Charlier extension with high-order-moment spillovers is implemented • ESG markets are not immune to cross-kurtosis transmission • Governance and Socially concerned assets are resilient to cross-skewness • Environmentally concerned assets present cross-skewness linkages to S&P500 This paper implements a recently developed multivariate volatility model, based on multivariate Gram-Charlier expansions, that incorporates high-order moment spillovers across assets, to examine the skewness and kurtosis transmission between the S&P500 index and Environmental, Social and Governance (ESG) based Exchange Traded Funds (ETFs). The evidence shows that ESG ETFs are not immune to tail dependence transmission (cross-kurtosis spillover). However, Governance and Social focused assets appear more resilient to transitory shocks originating in other markets (cross-skewness spillover). Interestingly, Environment-focused ETFs exhibit significant cross-skewness linkages with traditional market indices.

Traditional tsunami prediction relies on binary classification using machine learning, which lacks physical interpretability and extrapolation capability. This study introduces a novel probabilistic framework for tsunami genesis characterization using Multivariate Extreme Value Theory (MEVT) and Archimedean copula dependence modeling. We analyse an expanded dataset of 1,002 significant earthquakes [Formula: see text] drawn from the USGS/NEIC catalog spanning 1976-2022 and supplemented with moment-magnitude conversions following Scordilis (2006), yielding 391 confirmed tsunami events (39.0%). Generalized Extreme Value and Generalized Pareto distributions are fitted to the marginal parameters; negative shape parameters [Formula: see text] confirm light-tailed magnitude behavior consistent with tectonic fault-dimension constraints. Among three competing Archimedean families, the Clayton copula provides the best fit for magnitude-significance dependence ([Formula: see text] = 2.367, AIC = -725.17, AIC weight = 98.4%) with strong lower tail dependence ([Formula: see text] = 0.746, Kendall’s [Formula: see text] = 0.542), while magnitude-depth and depth-significance pairs display near-independence best described by the Frank copula. These bivariate structures are embedded in a trivariate nested Gumbel-Hougaard copula for complete joint modelling. A closed-form limit state equation is derived via the First Order Reliability Method (FORM): [Formula: see text], where inverse depth contributes 26.4% of total variance. Cross-validation yields AUC = 0.607 ± 0.038, Brier score = 0.233, and ECE = 0.031. This framework provides engineers with a physics-based, directly calculable tool for probabilistic tsunami hazard assessment (PTHA), bridging structural reliability theory with seismological practice.

This study investigates the dynamic impact of climate change performance on extreme tail risk transmission across global financial markets. Based on the “Too Extreme to Fail” conceptual framework, we propose a cascading failure network model using QRNN-∆CoVaR and QRNN-∆CoES to quantify the domino effect of tail risk propagation. The model captures tail dependencies and reveals how variations in climate governance performance modulate the intensity and pathways of risk contagion. Our main analysis utilizes daily market data from 1998 to 2024, aligned with the Climate Change Performance Index data from 2007 through a matched time window approach. The findings demonstrate that climate-sensitive factors significantly amplify systemic vulnerabilities, whereas superior climate governance serves as a critical risk buffer during periods of extreme volatility. Empirical results reveal significant spatial and temporal heterogeneity in risk contribution, with certain regions exhibiting higher sensitivity and momentum during major financial crises. Backtesting results confirm that our proposed nonlinear framework provides superior accuracy in quantifying global systemic risks compared to traditional linear methods, offering a robust tool for climate-integrated financial stability monitoring.

In this article we address the problem of high quantile estimation conditional on a related variable being extreme. The problem set-up is of interest in a number applications to evaluate tail risk of a focal variable in the tail of a conditioning variable. A primary example we consider is the assessment of systemic risk in financial markets using a risk measure known as the conditional value-at-risk (CoVaR). The proposed estimator is based on a novel approach to handle the bivariate tail dependence structure through an adjustment factor that can be used in conjunction with univariate high quantile estimation techniques. We establish the asymptotic behavior of the estimator under relatively weak assumptions, and illustrate its performance via simulation studies and a real data example. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Given the large investment in public transit systems worldwide as emphasized, it is of interest for policy makers to ask how the benefits of public transportation in urban areas compare with the associated costs. Using published data from eighty-one urbanized areas (UAZs) in the United States, we present a copula-based methodology for analyzing benefit-to-cost ratios of public transit systems under uncertainty. To describe the research motivation, we discuss the limitations of the existing probabilistic cost-benefit analysis (CBA) approaches, namely, Gaussian assumptions, linear correlation, and tail dependencies. This article provides a robust approach to evaluating how well transit systems compare across different urban areas and extends the probabilistic CBA models to incorporate nonlinear dependence.

Extreme events over large spatial domains may exhibit highly heterogeneous tail dependence characteristics, yet most existing spatial extremes models yield only one dependence class over the entire spatial domain. To accurately characterize dependence in extreme events, we propose a mixture model that achieves flexible dependence properties and allows high-dimensional inference ( ∼ 600 spatial locations in our data example) for extremes of spatial processes. We modify the popular random scale construction that multiplies a Gaussian random field by a single radial variable; we allow the radial variable to vary smoothly across space and add non-stationarity to the Gaussian process. As the level of extremeness increases, this single model exhibits both asymptotic independence at long ranges and either asymptotic dependence or independence at short ranges. We make joint inference on the dependence model and a marginal model using a copula approach within a Bayesian hierarchical model. Three different simulation scenarios show close to nominal frequentist coverage rates. Lastly, we apply the model to a dataset of extreme summertime precipitation over the central United States. We find that the joint tail of precipitation exhibits nonstationary dependence structure that cannot be captured by limiting extreme value models or current state-of-the-art sub-asymptotic models. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

In August 2023, the launch of Shanghai Containerized Freight Index (SCFI) futures provides a suitable tool for risk management in the container shipping market, as well as new options for risk management of other financial assets. However, limited research exists on the influencing factors behind container freight rate fluctuations. This paper explores the nonlinear dynamic interdependence between the SCFI and 12 factors from the stock, commodity, carbon, and other markets using a data decomposition-reconstruction-based time-varying copula method, which can assist the stakeholders in hedging risk at different timescales. The findings reveal that most factors show no or limited upper tail dependence with SCFI in the short term. Medium- and long-term dependence is significantly stronger, indicating structural connections over longer horizons. Moreover, the dependence intensifies during extreme risk events. Generally, downside tail risks exert a greater influence on SCFI in the medium to long term, while upside tail risks are found to affect SCFI at any time horizon. This paper focuses on the tail risk interdependence analysis between SCFI and other assets, because the launch of SCFI futures makes the stakeholders to use this future to build risk management portfolios with other assets inevitably. The result provides useful implications to stakeholders with varying financial or investment attributes associated with shipping industry, aiding them in clarifying the different tail risk associations between SCFI futures and other assets at different timescales.

This paper introduces the Copula Asymmetry Index (CAI), a rolling, rank-based measure of asymmetric tail dependence between equity returns and implied-volatility proxies. CAI is defined as the difference between the empirical frequency of joint “equity-down & volatility-up” tail events and that of the mirror state (“equity-up & volatility-down”) within a rolling window. Building on this core asymmetry measure, we develop CAI++, an implementation framework that transforms CAI into an operational defensive allocation signal through smoothing, standardization, delayed execution, hysteresis, and cost-aware portfolio mapping. Using daily data from 2000 onward across a broad cross-section of 50 equity-volatility pairs, we evaluate the CAI++ strategy against buy-and-hold equity, a 60/40 benchmark, an inverse-volatility risk-parity portfolio, and a moving-average timing rule. Cross-sectional results indicate that CAI improves terminal outcomes relative to equity-only exposure for most pairs and shows particularly strong performance versus 60/40 in both final wealth and Sharpe. However, CAI does not dominate structurally diversified low-volatility allocations: risk parity retains a pronounced advantage in downside risk and risk-adjusted metrics. Overall, the findings support CAI as a tail-aware overlay for equity-centric and balanced portfolios rather than a substitute for institutional low-volatility baselines.

This study quantifies risk spillover effects from multi-dimensional energy markets to China's Guangdong carbon market by constructing an EGARCH-CQR-based CoVaR model, which integrates the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) framework to capture volatility leverage effects and clustering, combined with quantile regression for precise characterization of cross-market tail dependencies. Empirical analysis reveals significant structural heterogeneity in energy-to-carbon risk spillovers: traditional energy markets, such as oil and coke, exhibit ‘high-intensity, high-volatility’ shock patterns that transmit abrupt short-term risks during global crises like the COVID-19 outbreak, whereas new energy markets, including new energy vehicles and wind power, demonstrate ‘low-intensity, persistent’ spillover dynamics reflecting stronger market resilience. Additionally, China's ‘Dual Carbon’ policy reinforcement is identified as a critical policy transmission channel that significantly intensifies risk linkages between high-carbon energy sectors and the carbon market, with model validation confirming the robustness and coverage capability of the proposed GARCH-CQR-CoVaR framework.

Systemic tail dependence in disruptive technology ETFs & crypto assets: A partial correlation network

Bitcoin and major precious metals are frequently discussed as hedges against equity drawdowns, inflation surprises, and policy uncertainty, which implicitly assumes a degree of functional equivalence in their risk behavior. Existing work remains limited in assessing high-dimensional dependence structures in the Bitcoin and precious metals system, without relying on bivariate conditional risk measures or restrictive copula frameworks. This study therefore aims to quantify bilateral and multivariate tail dependence and systemic spillovers between Bitcoin and selected precious metals and to identify the best-performing multivariate tail-risk specification, with model comparison conducted using AIC and BIC. The analysis uses 3,665 daily observations of adjusted closing prices spanning January 2013 to September 2024, sourced from Yahoo Finance. Marginal returns are fitted with an ARFIMA–FIGARCH skewed-t model, while cross-asset tail dependence is estimated via vine-copula quantile regression (C- and D-vines) using a 252-day rolling window (one-day step) with 10,000 copula simulations. Results indicate that Bitcoin exhibits substantially larger downside systemic contributions than precious metals, whereas gold displays the smallest systemic risk profile. Across information criteria, the D-vine–based SCoVaR specification provides the best overall fit, indicating that vine-based multivariate tail-risk measures better characterize systemic spillovers between the cryptocurrency and traditionally defensive assets under extreme market conditions. These results motivate future research on broader cryptocommodity networks and macro-financial conditioning, while practitioners and regulators can use the D-vine SCoVaR to monitor and mitigate downside spillovers in mixed-asset portfolios.

ABSTRACT This study investigates the hedging effectiveness and dependence structure between gold, gold ETFs, and both conventional and clean energy ETFs through copula-based models, including static and time-varying copulas. Daily return data from 2020 to 2023 is employed, with marginal volatilities filtered via GARCH models. Results show gold ETFs exhibit both upper and lower tail dependence with energy ETFs, with Gumbel and Clayton tail dependence coefficients ranging from 0.14 to 0.24 (upper) and 0.01 to 0.14 (lower), respectively. Conversely, gold displays weaker tail dependence, with Clayton lower-tail coefficients near zero for most energy ETF pairs. Time-varying copulas reveal that gold maintains negative average correlations (as low as −0.37) with traditional energy ETFs during crises, highlighting its role as a reliable hedge. These findings suggest that gold serves as a more effective hedging asset than gold ETFs, particularly in turbulent periods, and is especially suited for hedging traditional energy ETF exposures. The study contributes to the literature by integrating GARCH filtering with multiple copula types to capture dynamic, nonlinear, and asymmetric dependencies – a methodologically superior approach for assessing hedging effectiveness. It holds important implications for portfolio managers and risk-averse investors seeking to mitigate energy sector exposure through alternative asset allocations.

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.

It is increasingly the case with modern time series that many data sets of practical interest contain abrupt changes in structure. These changes may occur in complex characteristics such as the extremal dependence structure, and identifying such structural breaks remains a challenging problem. Many existing change point detection algorithms focus on changes in dependence across the entire distribution, rather than the tails, and approaches that are tailored to extremes typically make strict parametric assumptions or they are only applicable to bivariate data. We propose a nonparametric MOving sum based approach for detecting multiple changes in the Pairwise Extremal Dependence (MOPED) of multivariate regularly varying data. To avoid the classical problem of threshold selection in the study of multivariate extremes, we further propose a multiscale, multi-threshold variant of MOPED that pools change point estimates across choices of the threshold and the bandwidth used in local estimation. Good performance of MOPED is illustrated in a simulation study, and we showcase its ability to identify subtle changes in tail dependence class in the absence of correlation changes. We further demonstrate the usefulness of MOPED by identifying changes in the extremal connectivity of electroencephalogram (EEG) signals of seizure-prone neonates.

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

Abstract This study employs time-varying parameter vector autoregressive models in 3-month windows surrounding the 2023 Israel–Hamas conflict to analyze dynamic risk spillovers across diverse global assets. We find a modest postinvasion decrease in overall connectedness, suggesting market segmentation. US stable assets (i.e., the dollar and long-term US bonds) emerge as primary net risk transmitters, while US and European equities become key net receivers. A frequency-based analysis further shows that high-frequency connectedness falls moderately, indicating short-term segmentation, whereas low-frequency connectedness rises marginally, suggesting long-term integration. Crucially, a tail dependence analysis reveals a fundamental shift: postconflict, riskier assets (e.g., fossil fuels, US stocks, and Bitcoin) become the main net transmitters of downside risk, while traditional safe havens become the primary net receivers. Optimal portfolio allocations increase weights in US and European equities, European bonds, and Bitcoin after the invasion. US stocks effectively diversify oil asset exposures, while gold and Bitcoin provide diversification benefits for other financial assets.

Abstract This study explores the statistical dependence between wind speed and surge height along the Dutch coast using a large synthetic dataset. Storms were clustered based on wind direction, tidal offset, wind rotation, tidal peak, surge and wind exceedance duration, resulting in 16 clusters per wind direction and per location. Apart from wind direction, comparing clusters revealed a limited impact of clustering based on these storm characteristics on the choice of the best-fitting copula model, suggesting sub-clustering may not be necessary for accurately representing the statistical dependence between extreme wind speeds and surge heights. The BB8 copula generally provided the best fit to the data. However, the observed upper tail dependence did not decrease to zero, particularly for western to northern wind directions, indicating non-negligible dependence in joint extremes of wind speed and surge height. Therefore, applying the BB8 copula (or any other copula model without upper tail dependence) may lead to underestimation of the flood risk, when applied in probabilistic analyses. The findings from this study provide valuable insights for refining hydraulic load models for reliability assessments and design of flood defenses.

In this paper, we provide generalizations of the functional equations that characterize the lack-of-memory properties: more specifically, we extend the univariate functional equation introduced by Kaminsky (1983, An aging property of the Gompertz survival function and a discrete analog (Tech. Rep.). Department of Mathematical Statistics, University of Umeå, Sweden) and the corresponding bivariate strong and weak versions studied in Marshall and Olkin (2015, A bivariate Gompertz–Makeham life distribution. Journal of Multivariate Analysis, 139, 219–226. https://doi.org/10.1016/j.jmva.2015.02.011) by allowing the conditional survival distribution to be a fully general time dependent distortion of the unconditional one. Since the univariate functional equation leads only to a trivial case and the solutions of the strong bivariate functional equation have been already studied in the literature, the analysis focuses on the weak bivariate case, where joint residual lifetimes are conditioned on survival beyond a common threshold t. In view of potential applications to insurance risk analysis, we study the impact of the time dependent distortion on the aging properties and on the dependence structure of the residual lifetimes via time-varying Kendall's function and tail dependence coefficients: moreover, we provide some illustrative examples showing that these distributions can model both broken hearth effect as well as its reverse version.

Traditional machine learning models for concrete compressive strength prediction provide only single-value estimates without quantifying the probability of meeting design requirements, leaving engineers unable to make risk-informed decisions. This study addresses this critical limitation by developing a novel probabilistic prediction framework that integrates explainable machine learning with Copula-based joint distribution modeling. Using a dataset of 1030 concrete samples with curing ages ranging from 1 to 365 days, we first established an XGBoost 2.1.4 prediction model achieving R2 = 0.9211 (RMSE = 4.51 MPa) on the test set. SHAP 0.49.1 (SHapley Additive exPlanations) analysis identified curing age (33.3%) and water–cement ratio (28.8%) as the dominant features, together accounting for 62.1% of predictive importance. These two controllable engineering parameters were then selected as core variables for probabilistic modeling. The key innovation lies in integrating Copula-based dependence modeling with explainable machine learning (XGBoost–SHAP) to quantify the compliance probability of concrete strength under specific mix designs and curing conditions, thereby supporting risk-informed quality control decisions. Through systematic comparison of five Copula families (Gaussian, Student t, Clayton, Gumbel, and Frank), we identified optimal dependence structures: Gaussian Copula (ρ = −0.54) for the water–cement ratio–strength relationship and Clayton Copula for the age–strength relationship, revealing asymmetric tail dependence patterns invisible to conventional correlation analysis. The three-dimensional Copula model enables engineers to estimate compliance probability—the likelihood of concrete achieving target strength under specific mix designs and curing conditions. We propose an illustrative three-tier decision rule for construction quality management based on the compliance probability P: P ≥ 0.95 (high-confidence approval), 0.80 ≤ P < 0.95 (warning zone requiring enhanced monitoring), and P < 0.80 (high risk suggesting corrective actions such as mix adjustment or extended curing), noting that these thresholds can be recalibrated to project-specific risk tolerance and local specifications. This framework supports a paradigm shift from reactive “mix-then-test” quality control to proactive “predict-then-decide” construction management, providing quantitative risk assessment tools previously unavailable in deterministic prediction approaches.