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Multivariate Realized Volatility Forecasting with Graph Neural Network

arXiv 2112.09015 ↗First posted 16 Dec 2021q-fin.CP cs.CE cs.LGcited by 0

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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.

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.

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21 of 83 references.

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