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Multivariate Realized Volatility Forecasting with Graph Neural Network

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  • Qinkai Chen
  • Christian-Yann Robert

Abstract

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.

Suggested Citation

  • Qinkai Chen & Christian-Yann Robert, 2021. "Multivariate Realized Volatility Forecasting with Graph Neural Network," Papers 2112.09015, arXiv.org, revised Dec 2021.
    • Handle: RePEc:arx:papers:2112.09015
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    References listed on IDEAS

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    Cited by:

    1. Artem Lensky & Mingyu Hao, 2023. "Learning to Predict Short-Term Volatility with Order Flow Image Representation," Papers 2304.02472, arXiv.org, revised Mar 2024.
    2. Mathieu Rosenbaum & Jianfei Zhang, 2022. "On the universality of the volatility formation process: when machine learning and rough volatility agree," Papers 2206.14114, arXiv.org.

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