{
  "abstract": "Equity-market connectedness can look different depending on whether risk is measured by variance, asymmetry, tail thickness, or jumps. Using 5 min observations for the CSI New Energy Index and ten Shanghai Stock Exchange sector indices from 4 January 2016 to 31 December 2025, we construct 2430 common daily observations of realized volatility, skewness, kurtosis, and jump risk and estimate four TVP-VAR/GFEVD systems. Average total connectedness is 81.2 for volatility, 52.1 for skewness, 41.2 for kurtosis, and 51.1 for jump risk. The new-energy index is a net transmitter in all four full-sample systems, with NET values of 4.7, 4.0, 3.5, and 3.6, respectively, but the dominant transmitter differs by risk dimension: industrials lead volatility and skewness, materials lead kurtosis, and consumer discretionary leads jump risk. Subperiod results show that these roles are not temporally invariant; ZNE becomes a net receiver in several post-2021 risk-measure/period combinations. Alternative lag lengths, forecast horizons, forgetting factors, 15 min aggregation, and an alternative jump-test size leave the broad TVP-VAR conclusions unchanged. The results show that sectoral risk leadership is both dimension-specific and time-varying, with implications for portfolio and sectoral risk monitoring.",
  "abstract_shown": true,
  "affiliations": [
    "Business School, Hunan University, Changsha 410082, China",
    "College of Tourism, Hunan Normal University, Changsha 410081, China",
    "School of Mathematics and Statistics, Central South University, Changsha 410083, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Sai Xie"
    },
    {
      "affs": [
        1
      ],
      "name": "Cai Yang"
    },
    {
      "affs": [
        2
      ],
      "name": "Hongwei Zhang",
      "orcid": "0000-0002-9137-2349"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6pejef1zct91t.bib",
  "cited_by": 0,
  "doi": "10.3390/math14193440",
  "fulltext_url": null,
  "id": "6pejef1zct91t",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/19/3440",
  "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": "W7215042186",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/6pejef1zct91t.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14193440",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/19/3440"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Dynamic Spillovers in Higher Moments and Jumps Across Chinese New Energy and Industry Stock Markets",
  "title_text": "Dynamic Spillovers in Higher Moments and Jumps Across Chinese New Energy and Industry Stock Markets",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/6pejef1zct91t",
  "venue": "Mathematics",
  "versions": []
}