RePEcLLMs & Text
Transformer-based CoVaR: Systemic Risk in Textual Information
Integrating financial news embeddings from large language models with market data, the study improves systemic risk forecasts using conditional value-at-risk without requiring large datasets.
Featured in No. 133 on 2 Oct 2026 · 9 days after release
- Released
- 23 Sep 2026
- First featured
- No. 133 · 2 Oct 2026
- Published in
- Not yet, as far as Semantic Scholar knows
- Fanfare
- 3 of 5
- Identifier
- RePEc:bri:uobdis:26/840
- Authors
- Junyu Chen et al.
Citations and venue from Semantic Scholar (ODC-BY), refreshed weekly. Summary: Quant Letter (CC BY 4.0).