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Enhanced volatility spillover network prediction of Chinese financial institutions using GCN-LSTM model

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  • Gu, Qinen
  • Li, Shaofang
  • Qin, Jiaying

Abstract

By constructing the volatility spillover network based on LASSO-VAR and generalized variance decomposition method, this study develops the graph convolutional network and long short-term memory (GCN-LSTM) model to predict the volatility spillover network of 31 listed Chinese financial institutions between 2014 and 2022. Our findings confirm the volatility spillover of bank, security and insurance sectors exhibits heterogeneity and during the stock market crash, the volatility spillover network among institutions is enhanced, and the spillover from security to bank and insurance plays a crucial role in accumulating systemic financial risk. The empirical results demonstrate the proposed GCN-LSTM model yields promising predictive performance than models including ARMA, SVM, RF, and LSTM.

Suggested Citation

  • Gu, Qinen & Li, Shaofang & Qin, Jiaying, 2025. "Enhanced volatility spillover network prediction of Chinese financial institutions using GCN-LSTM model," Finance Research Letters, Elsevier, vol. 85(PC).
    • Handle: RePEc:eee:finlet:v:85:y:2025:i:pc:s1544612325012917
    DOI: 10.1016/j.frl.2025.108033
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    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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