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Forecasting realized volatility through financial turbulence and neural networks

Author

Listed:
  • Souto Hugo Gobato

    (1 International School of Business at HAN University of Applied Sciences, Ruitenberglaan 31, 6826 CC Arnhem, the, Netherlands)

  • Moradi Amir

    (2 International School of Business at HAN University of Applied Sciences, Ruitenberglaan 31, 6826 CC Arnhem, the Netherlands)

Abstract

This paper introduces and examines a novel realized volatility forecasting model that makes use of Long Short-Term Memory (LSTM) neural networks and the risk metric financial turbulence (FT). The proposed model is compared to five alternative models, of which two incorporate LSTM neural networks and the remaining three include GARCH(1,1), EGARCH(1,1), and HAR models. The results of this paper demonstrate that the proposed model yields statistically significantly more accurate and robust forecasts than all other studied models when applied to stocks with middle-to-high volatility. Yet, considering low-volatility stocks, it can only be confidently affirmed that the proposed model yields statistically significantly more robust forecasts relative to all other models considered.

Suggested Citation

  • Souto Hugo Gobato & Moradi Amir, 2023. "Forecasting realized volatility through financial turbulence and neural networks," Economics and Business Review, Sciendo, vol. 9(2), pages 133-159, April.
    • Handle: RePEc:vrs:ecobur:v:9:y:2023:i:2:p:133-159:n:8
    DOI: 10.18559/ebr.2023.2.737
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    References listed on IDEAS

    as
    1. Latoszek Michał & Ślepaczuk Robert, 2020. "Does the inclusion of exposure to volatility into diversified portfolio improve the investment results? Portfolio construction from the perspective of a Polish investor," Economics and Business Review, Sciendo, vol. 6(1), pages 46-81, March.
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    More about this item

    Keywords

    neural networks; LSTM neural networks; realized volatility prediction; financial turbulence;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G19 - Financial Economics - - General Financial Markets - - - Other

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