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Risk Analysis of the Chinese Financial Market with the Application of a Novel Hybrid Volatility Prediction Model

Author

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  • Weibin Wang

    (School of Economics and Management, Sanming University, Sanming 365004, China)

  • Yao Wu

    (School of International Economics and Management, Beijing Technology and Business University, Beijing 100048, China
    Institute of Digital Economy, Beijing Technology and Business University, Beijing 100048, China)

Abstract

This paper endeavors to enhance the prediction of volatility in financial markets by developing a novel hybrid model that integrates generalized autoregressive conditional heteroskedasticity (GARCH) models and long short-term memory (LSTM) neural networks. Using high-frequency data, we first estimate realized volatility as a robust measure of volatility. We then feed the outputs of multiple GARCH models into an LSTM network, creating a hybrid model that leverages the strengths of both approaches. The predicted volatility from the hybrid model is used to generate trading strategy signals, which are subsequently used to build an investment strategy. Empirical analysis using the China Securities Index 300 (CSI300) dataset demonstrates that the hybrid model significantly improves value-at-risk (VaR) prediction performance compared to traditional GARCH models. This study’s findings have broad implications for risk management in financial markets, suggesting that hybrid models incorporating mathematical models and economic mechanisms can enhance derivative pricing, portfolio risk management, hedging transactions, and systemic risk early-warning systems.

Suggested Citation

  • Weibin Wang & Yao Wu, 2023. "Risk Analysis of the Chinese Financial Market with the Application of a Novel Hybrid Volatility Prediction Model," Mathematics, MDPI, vol. 11(18), pages 1-12, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3937-:d:1241277
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    References listed on IDEAS

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    1. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    4. Fuertes, Ana-Maria & Izzeldin, Marwan & Kalotychou, Elena, 2009. "On forecasting daily stock volatility: The role of intraday information and market conditions," International Journal of Forecasting, Elsevier, vol. 25(2), pages 259-281.
    5. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
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    Cited by:

    1. Yue Li & Qingyu Hu & Guilan Xie & Gong Chen, 2023. "Prediction of the Health Status of Older Adults Using Oversampling and Neural Network," Mathematics, MDPI, vol. 11(24), pages 1-33, December.

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