SSRNML & AI Methods
A hybrid graph neural network for realized volatility forecasting: Multi-relation attention and dual-path fusion
A graph neural network combining temporal and cross-asset volatility patterns reduces mean squared error and quasi-likelihood loss by approximately 9.9% and 3.4% on Dow stocks.
Featured in No. 134 on 9 Oct 2026 · 2 days after release
- Released
- 7 Oct 2026
- First featured
- No. 134 · 9 Oct 2026
- Published in
- Not yet, as far as Semantic Scholar knows
- Fanfare
- 3 of 5
- Identifier
- SSRN 7576359
- Authors
- Jongu Lee et al.
Citations and venue from Semantic Scholar (ODC-BY), refreshed weekly. Summary: Quant Letter (CC BY 4.0).