Forecasting realized volatility through financial turbulence and neural networks

Souto Hugo Gobato () and Amir Moradi
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Souto Hugo Gobato: International School of Business at HAN University of Applied Sciences, Ruitenberglaan 31, 6826 CC Arnhem, the Netherlands

Economics and Business Review, 2023, vol. 9, issue 2, 133-159

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.

Keywords: neural networks; LSTM neural networks; realized volatility prediction; financial turbulence (search for similar items in EconPapers)
JEL-codes: C45 C53 G19 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/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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