{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [
    "Jesse H. Jones Graduate School of Business, Rice University, Houston, TX 77005, USA",
    "School of Economics and Management, Xinjiang University, Urumqi 830046, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jingqi Yao"
    },
    {
      "affs": [
        1
      ],
      "name": "Xuechao Xia"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b8phm1773z8nc.bib",
  "cited_by": 0,
  "doi": "10.3390/math14193587",
  "fulltext_url": null,
  "id": "b8phm1773z8nc",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/19/3587",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2003|Financial Risk and Volatility Modeling"
    ]
  ],
  "openalex": "W7216385764",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/b8phm1773z8nc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14193587",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/19/3587"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Multivariate Hawkes-Driven Dynamic Vine Copulas for Jump Clustering and Systemic Risk Measurement in Financial Markets",
  "title_text": "Multivariate Hawkes-Driven Dynamic Vine Copulas for Jump Clustering and Systemic Risk Measurement in Financial Markets",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/b8phm1773z8nc",
  "venue": "Mathematics",
  "versions": []
}