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Does multi-scale GARCH information enhance volatility prediction?

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  • Yu, Rentian
  • Xiao, Haotian
  • Zhu, Yukun
  • Zhang, Gongqiu

Abstract

The GARCH-LSTM model is widely used for volatility prediction. However, it fails to account for feature dependencies across different frequencies, and the lack of an attention mechanism limits its predictive accuracy. To address these issues, this paper introduces the GENSHIN framework, which integrates GARCH information with a multi-scale graph neural network to model volatility across multiple frequency bands with MixHop and multi-head attention mechanism. Empirical studies on the volatility of major Chinese stock indices demonstrate that GENSHIN outperforms other deep learning models, such as TimesNet and LSTM. These results highlight the effectiveness of multi-scale information in improving volatility forecasting.

Suggested Citation

  • Yu, Rentian & Xiao, Haotian & Zhu, Yukun & Zhang, Gongqiu, 2025. "Does multi-scale GARCH information enhance volatility prediction?," Finance Research Letters, Elsevier, vol. 78(C).
    • Handle: RePEc:eee:finlet:v:78:y:2025:i:c:s1544612325004593
    DOI: 10.1016/j.frl.2025.107196
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    1. Wang, Yijun & Andreeva, Galina & Martin-Barragan, Belen, 2023. "Machine learning approaches to forecasting cryptocurrency volatility: Considering internal and external determinants," International Review of Financial Analysis, Elsevier, vol. 90(C).
    2. Yuping Song & Xiaolong Tang & Hemin Wang & Zhiren Ma, 2023. "Volatility forecasting for stock market incorporating macroeconomic variables based on GARCH‐MIDAS and deep learning models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 51-59, January.
    3. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    4. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    7. Nelson, Daniel B., 1992. "Filtering and forecasting with misspecified ARCH models I : Getting the right variance with the wrong model," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 61-90.
    8. 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.
    9. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    10. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    11. Benoit Mandelbrot, 1967. "The Variation of Some Other Speculative Prices," The Journal of Business, University of Chicago Press, vol. 40, pages 393-393.
    12. Rayadurgam, Vikram Chandramouli & Mangalagiri, Jayasree, 2023. "Does inclusion of GARCH variance in deep learning models improve financial contagion prediction?," Finance Research Letters, Elsevier, vol. 54(C).
    13. Çelik, Sibel & Ergin, Hüseyin, 2014. "Volatility forecasting using high frequency data: Evidence from stock markets," Economic Modelling, Elsevier, vol. 36(C), pages 176-190.
    14. Nguyen, Hien Thi & Nguyen, Hoang & Tran, Minh-Ngoc, 2024. "Deep learning enhanced volatility modeling with covariates," Finance Research Letters, Elsevier, vol. 69(PB).
    15. Mensi, Walid & Lee, Yun-Jung & Al-Yahyaee, Khamis Hamed & Sensoy, Ahmet & Yoon, Seong-Min, 2019. "Intraday downward/upward multifractality and long memory in Bitcoin and Ethereum markets: An asymmetric multifractal detrended fluctuation analysis," Finance Research Letters, Elsevier, vol. 31(C), pages 19-25.
    16. M. Angeles Carnero, 2004. "Persistence and Kurtosis in GARCH and Stochastic Volatility Models," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 319-342.
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