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Forecasting realized volatility through financial turbulence and neural networks

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Forecasting realized volatility through financial turbulence and neural networks

Open access

Samenvatting

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.


OrganisatieHAN University of Applied Sciences
AfdelingAcademie International School of Business
Lectoraten
LectoraatInternational Business
Gepubliceerd inEconomics and Business Review
Datum2023-07-26
TypeArtikel
TaalOnbekend

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