A Semi-Parametric Dynamic Conditional Correlation Framework for Forecasting Systemic Risk: Evidence from CoVaR and Marginal Expected Shortfall

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.