no code implementations • 1 Aug 2023 • Chao Zhang, Xingyue Pu, Mihai Cucuringu, Xiaowen Dong
We present a novel methodology for modeling and forecasting multivariate realized volatilities using customized graph neural networks to incorporate spillover effects across stocks.
1 code implementation • NeurIPS 2021 • Xingyue Pu, Tianyue Cao, Xiaoyun Zhang, Xiaowen Dong, Siheng Chen
The model is trained in an end-to-end fashion with pairs of node data and graph samples.
no code implementations • 23 Aug 2020 • Xingyue Pu, Siu Lun Chau, Xiaowen Dong, Dino Sejdinovic
In this paper, we propose a novel graph learning framework that incorporates the node-side and observation-side information, and in particular the covariates that help to explain the dependency structures in graph signals.