Multivariate Hawkes-Driven Dynamic Vine Copulas for Jump Clustering and Systemic Risk Measurement in Financial Markets

Extreme co-movements in asset returns tend to arrive in clusters and to propagate across markets, a feature that static dependence models and diffusion-based volatility models fail to capture jointly. This paper develops a dynamic vine copula framework in which the dependence parameters of each bivariate building block are driven by the intensities of a mutually exciting multivariate Hawkes process fitted to the jump arrivals of the underlying return series. The construction yields a closed-form expression for conditional joint density, preserves the flexibility of regular vines in high dimensions, and links tail dependence directly to the self- and cross-excitation structure of jumps. We establish stationarity and ergodicity conditions for the coupled Hawkes–copula system, we derive the asymptotic properties of a two-stage maximum likelihood estimator, and we propose a Monte Carlo procedure for computing Conditional Value-at-Risk (CoVaR) and Marginal Expected Shortfall under the fitted dependence dynamics. An empirical study based on the daily returns of 11 S&P 500 sector indices and 6 major international equity indices over 2005–2025 shows that the proposed model produces significantly better out-of-sample density forecasts and more accurate 5% tail-risk coverage than time-varying Gaussian, Student-t, and DCC-based copula benchmarks and quantile-regression CoVaR, with the largest gains observed during the 2008 financial crisis and the 2020 pandemic sell-off. The estimated excitation matrix further identifies the financial and energy sectors as the dominant transmitters of jump contagion, a classification that is robust to the jump threshold, to the shape of the excitation kernel, to sub-sample choice and, in the international application, to corrections for non-synchronous trading. The framework offers a tractable tool for regulators and portfolio managers to monitor the build-up of systemic risk in real time.