Accurate forecasting of systemic risk is crucial for financial stability and regulatory oversight. Traditional parametric models often fail to capture the complex, nonlinear dependence structures in financial markets, particularly during periods of distress. This paper proposes a semi-parametric dynamic conditional correlation (DCC) framework that integrates nonparametric estimation of marginal distributions with a parametric DCC specification to improve the forecasting of systemic risk measures—Conditional Value-at-Risk (CoVaR) and Marginal Expected Shortfall (MES). We employ a kernel-based quantile regression approach for marginal modeling and combine it with a DCC-GARCH structure for capturing time-varying conditional correlations. Empirical results using daily equity returns for a panel of 20 large U.S. financial institutions over the period January 2000 to December 2023, with emphasis on three stress episodes—the 2007–2009 global financial crisis, the 2011 European sovereign debt crisis spillover, and the March 2020 COVID-19 market disruption—demonstrate that the suggested semi-parametric DCC framework yields more accurate and robust one-day-ahead forecasts of CoVaR and MES compared to fully parametric alternatives, with the advantage widening during stress periods. During the 2007–2009 crisis sub-period, the SP-DCC model reduces the Fissler–Ziegel loss for CoVaR by 19.0% relative to the Gaussian-DCC benchmark and by 6.5% relative to the strongest parametric benchmark (skewed-t DCC). Importantly, the SP-DCC specification is the only model in our comparison set for which the null hypotheses of correct unconditional and conditional coverage are not rejected at the 1% significance level during stress periods, whereas the Gaussian-DCC benchmark significantly over-rejects. The model also exhibits improved performance in identifying systemically important institutions and tail interdependencies during stress episodes, with rankings that align more closely with official G-SIB designations and SRISK-based rankings than those implied by Gaussian-DCC benchmarks. Our findings underscore the importance of flexible marginal modeling in systemic risk measurement and provide policymakers with an enhanced toolkit for macroprudential surveillance.

Accurate forecasting of systemic risk is crucial for financial stability and regulatory oversight. Traditional parametric models often fail to capture the complex, nonlinear dependence structures in financial markets, particularly during periods of distress. This paper proposes a semi-parametric dynamic conditional correlation (DCC) framework that integrates nonparametric estimation of marginal distributions with a parametric DCC specification to improve the forecasting of systemic risk measures—Conditional Value-at-Risk (CoVaR) and Marginal Expected Shortfall (MES). We employ a kernel-based quantile regression approach for marginal modeling and combine it with a DCC-GARCH structure for capturing time-varying conditional correlations. Empirical results using U.S. financial institutions’ equity returns during the 2007–2009 crisis and the post-crisis period demonstrate that the suggested semi-parametric DCC framework yields more accurate and robust forecasts of CoVaR and MES compared to fully parametric alternatives. Importantly, the SP-DCC is the only model that maintains correct unconditional and conditional coverage at the 1% VaR level during stress periods, while Gaussian-DCC significantly over-rejects. The model also exhibits superior performance in identifying systemically important institutions and tail interdependencies during stress episodes. Our findings underscore the importance of flexible marginal modeling in systemic risk measurement and provide policymakers with an enhanced toolkit for macroprudential surveillance.

This paper examines the empirical causal relationship between energy consumption, CO2 emissions and economic growth for six oil-exporting countries from the Golf Cooperation Council (GCC) region over the period 2000:2011. Bootstrap panel Granger causality test approach is used which take account the cross-sectional dependency and the heterogeneity across countries. The empirical results support a bi-directional causality between economic growth and energy consumption for Bahrin and one-way Granger causality running from economic growth to energy consumption for United Emirate Arab and Qatar. Regarding to GDP-CO2 emissions nexus, a reverse relationship from CO2 to GDP for Bahrin and Kuwait is found. However, a two-way Granger causality between CO2 emissions and energy consumption for United Arab Emirate is found.

Multifractality and market volatility are important features of financial time series and may provide useful information for understanding complex market dynamics. This paper proposes a diagnostic framework combining wavelet multifractal analysis and Dynamic Mode Decomposition (DMD) to investigate stock price and trading volume series associated with major Saudi brands. The multifractal analysis reveals heterogeneous results across brands: statistically significant multifractality is detected for only five of the twelve brands (B1, B6, B7, B8, and B9), while for the remaining seven brands the null hypothesis of a monofractal process cannot be rejected. This indicates that multifractal behavior is not a universal feature of the sample. DMD is applied as a data-driven decomposition tool to identify dominant temporal modes and characteristic frequencies in both stock prices and trading volume series. Forecasting results indicate that the predictive value of DMD is limited and brand-specific, with performance improvements observed only for selected brands. We also compare DMD characteristics with conventional risk measures, including Value-at-Risk (VaR) and Expected Shortfall (ES). The relationship between DMD modes and risk indicators is exploratory and suggestive rather than formally demonstrated. Overall, the proposed framework provides a descriptive and diagnostic perspective on market dynamics rather than a direct risk measurement or forecasting tool.