<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Forecasting realized volatility through financial turbulence and neural networks

International School of Business at HAN University of Applied Sciences

Economics and Business Review

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Forecasting realized volatility through financial turbulence and neural networks"}

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...

Forecasting realized volatility through financial turbulence and neural networks

Data publikacji: 26 lip 2023

Zakres stron: 133 - 159

Otrzymano: 27 lut 2023

Przyjęty: 16 cze 2023

DOI: https://doi.org/10.18559/ebr.2023.2.737

Słowa kluczowe
neural networks, LSTM neural networks, realized volatility prediction, financial turbulence

© 2023 Hugo Gobato Souto et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Forecasting realized volatility through financial turbulence and neural networks

Data publikacji: 26 lip 2023

Zakres stron: 133 - 159

Otrzymano: 27 lut 2023

Przyjęty: 16 cze 2023

DOI: https://doi.org/10.18559/ebr.2023.2.737

Słowa kluczoweneural networks, LSTM neural networks, realized volatility prediction, financial turbulence

© 2023 Hugo Gobato Souto et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
neural networks, LSTM neural networks, realized volatility prediction, financial turbulence