arbiter
JournalsPricingAccount

Forecasting extreme tail risk in China’s banking sector: an approach based on a component generalized autoregressive conditional heteroscedasticity and mixed data sampling model and extreme value theory

Xiaobin Du & Yan Sun

Journal of Risk2025https://doi.org/10.21314/jor.2025.014article
AJG 2ABDC B
Weight
0.50

What the paper says

No abstract available.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.21314/jor.2025.014

Or copy a formatted citation

@article{xiaobin2025,
  title        = {{Forecasting extreme tail risk in China’s banking sector: an approach based on a component generalized autoregressive conditional heteroscedasticity and mixed data sampling model and extreme value theory}},
  author       = {Xiaobin Du & Yan Sun},
  journal      = {Journal of Risk},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.21314/jor.2025.014},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Forecasting extreme tail risk in China’s banking sector: an approach based on a component generalized autoregressive conditional heteroscedasticity and mixed data sampling model and extreme value theory

Flags are reviewed by the Arbiter methodology team within 5 business days.

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

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.