Multivariate Realized Volatility Forecasting with Graph Neural Network¶
Bibliographic record. Follow the original-source link for the publication.
| Field | Value |
|---|---|
| Primary domain | Volatility |
| Other domains | — |
| Methods | Financial Ml |
| Facets | — |
| Authors | Qinkai Chen, Christian-Yann Robert |
| Published | 2021-12-17 |
| Source | arXiv Quantitative Finance History |
| Identifiers | arxiv:2112.09015 |
| URL | Open original source |
Editorial synthesis¶
Why it matters¶
Integrating relational structure with limit-order-book data may improve multivariate short-term volatility forecasts; the abstract does not show that these forecasts translate into tradable signals. (abstract:S1, abstract:S2, abstract:S3, abstract:S4, abstract:S5, abstract:S6)
Main author claims¶
- They introduce a Graph Transformer Network for short-term realized-volatility forecasting in a multivariate, order-book-based framework. (
abstract:S3,abstract:S4) - The authors report: The model combines limit order book features with temporal and cross-sectional relations from multiple sources. (
abstract:S5) - The authors report: Experiments on about 500 stocks from the S&P 500 report better performance than benchmarks. (
abstract:S6)
Data, method, or discussion scope¶
Scope is multivariate short-horizon realized-volatility forecasting over roughly 500 S&P 500 stocks using graph-transformer models and LO B inputs. (abstract:S3, abstract:S5, abstract:S6)
Main limitations¶
Graph construction, benchmark definitions, and computational constraints are not specified, and external generalization checks are absent. (abstract:S4, abstract:S5, abstract:S6)
Relationships¶
- None recorded.