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Does inclusion of GARCH variance in deep learning models improve financial contagion prediction?

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  • Rayadurgam, Vikram Chandramouli
  • Mangalagiri, Jayasree

Abstract

Financial contagions have traditionally been studied using GARCH models to explain the volatility, whereas regression models have been used to predict the stock index values based on different underlying assets. Past research has shown that deep learning regression models have demonstrated superiority over traditional statistical models. This paper creates a hybrid prediction model by using the outputs of GARCH models as an input variable in deep learning regression models. It has been observed that hybrid models provide better results as compared to deep learning models. Such improved prediction shall provide portfolio managers an additional tool to determine the investment strategy.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:finlet:v:54:y:2023:i:c:s1544612323000818
    DOI: 10.1016/j.frl.2023.103707
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    3. Shu, Qi & Xiong, Heng & Jiang, Wenjun & Mamon, Rogemar, 2023. "A novel perspective on forecasting non-ferrous metals’ volatility: Integrating deep learning techniques with econometric models," Finance Research Letters, Elsevier, vol. 58(PC).

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