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Graph Signal Processing for Global Stock Market Volatility Forecasting

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  • Zhengyang Chi
  • Junbin Gao
  • Chao Wang

Abstract

The interconnectedness of global financial markets has brought increasing attention to modeling volatility spillover effects. Via incorporating Graph Signal Processing techniques, a novel multivariate framework, extending the traditional Heterogeneous Auto-Regressive model, is developed in the spectral domain constructed by the graph Fourier transformation method. Further, a set of convolution filters with learnable weights is employed to more flexibly aggregate the past mid-term and long-term information. Using 24 global stock market indices, the effectiveness of the proposed model is demonstrated through comprehensive empirical evaluations.

Suggested Citation

  • Zhengyang Chi & Junbin Gao & Chao Wang, 2024. "Graph Signal Processing for Global Stock Market Volatility Forecasting," Papers 2410.22706, arXiv.org.
    • Handle: RePEc:arx:papers:2410.22706
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    References listed on IDEAS

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