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Dynamic Volatility Linkage between the Hong Kong and U.S. Stock Markets: Evidence from DCC-GARCH-Copula Models

Jiaqi Luo & Wei Ji

Economic Computation and Economic Cybernetics Studies and Research2026https://doi.org/10.24818/18423264/60.1.26.06article
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What the paper says

Using daily returns on the Hang Seng Index and the S&P 500 from 1995 to 2025, this study investigates volatility dynamics and cross-market dependence between the U.S. equity market and Hong Kong as an offshore, China-exposed financial hub within a DCC-GARCH-Copula framework.EGARCH (1,1)-t delivers the best marginal fit, indicating asymmetric and fat-tailed volatility.In the dependence stage, information criteria favour the Plackett copula in both non-crisis and crisis subsamples.The DCC conditional correlation rises sharply and remains persistent during major stress episodes, notably around the Global Financial Crisis.Out-of-sample results indicate that forecasting performance is horizon-and market-specific: DCC is most accurate for short-horizon HSI volatility, BEKK is comparatively more robust at longer horizons, and GO-GARCH performs well for the S&P 500 across several settings.Overall, the research conclusion provides a reference for investors to mitigate cross-market risks; jointly modelling volatility and dependence provides a more reliable basis for state-contingent risk assessment and cross-border portfolio allocation.

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https://doi.org/https://doi.org/10.24818/18423264/60.1.26.06

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@article{jiaqi2026,
  title        = {{Dynamic Volatility Linkage between the Hong Kong and U.S. Stock Markets: Evidence from DCC-GARCH-Copula Models}},
  author       = {Jiaqi Luo & Wei Ji},
  journal      = {Economic Computation and Economic Cybernetics Studies and Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.24818/18423264/60.1.26.06},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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