# Multivariate Realized Volatility Forecasting with Graph Neural Network

arXiv: https://arxiv.org/abs/2112.09015 · HTML: https://mlsanity.com/paper/2112.09015/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 83 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 21 of 21)

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- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
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- [Universal features of price formation in financial markets: perspectives from Deep Learning](https://mlsanity.com/paper/1803.06917/) (2018)
- [HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction](https://mlsanity.com/paper/1908.07999/) (2019)
- [Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction](https://mlsanity.com/paper/2005.04955/) (2020)
- [Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility](https://mlsanity.com/paper/2109.12621/) (2021)

## Abstract (arXiv)

The existing publications demonstrate that the limit order book data is useful in predicting short-term volatility in stock markets. Since stocks are not independent, changes on one stock can also impact other related stocks. In this paper, we are interested in forecasting short-term realized volatility in a multivariate approach based on limit order book data and relational data. To achieve this goal, we introduce Graph Transformer Network for Volatility Forecasting. The model allows to combine limit order book features and an unlimited number of temporal and cross-sectional relations from different sources. Through experiments based on about 500 stocks from S&P 500 index, we find a better performance for our model than for other benchmarks.