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

<p>This study aims to predict the ESG (environmental, social, and governance) return volatility based on ESG index data from 26 October 2017 and 31 March 2023 in the case of India. In this study, we utilized GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and LSTM (Long Short-Term Memory) models for forecasting the return of ESG volatility and to evaluate the model’s suitability for prediction. The study’s findings demonstrate the GARCH effect inside the ESG return volatility data, indicating the occurrence of volatility in response to market fluctuations. This study provides insight concerning the suitability of models for volatility predictions. Moreover, based on the analysis of the return volatility of the ESG index, the GARCH model is more appropriate than the LSTM model.</p>
</abstract>

This study aims to predict the ESG (environmental, social, and governance) return volatility based on ESG index data from 26 October 2017 and 31 March 2023 in the case of India. In this study, we utilized GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and LSTM (Long Short-Term Memory) models for forecasting the return of ESG volatility and to evaluate the model’s suitability for prediction. The study’s findings demonstrate the GARCH effect inside the ESG return volatility data, indicating the occurrence of volatility in response to market fluctuations. This study provides insight concerning the suitability of models for volatility predictions. Moreover, based on the analysis of the return volatility of the ESG index, the GARCH model is more appropriate than the LSTM model.

ESG Volatility Prediction Using GARCH and LSTM Models

Financial Internet Quarterly

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"ESG Volatility Prediction Using GARCH and LSTM Models"}

This study aims to predict the ESG (environmental, social, and governance) return volatility based on ESG index data from 26 October 2017 and 31 March 2023 in...

ESG Volatility Prediction Using GARCH and LSTM Models

Online veröffentlicht: 02. Jan. 2024

Seitenbereich: 97 - 114

Eingereicht: 22. Aug. 2023

Akzeptiert: 20. Sept. 2023

DOI: https://doi.org/10.2478/fiqf-2023-0029

Schlüsselwörter
ESG Volatility, GARCH, LSTM model

© 2023 Akshay Kumar Mishra et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

ESG Volatility Prediction Using GARCH and LSTM Models

Online veröffentlicht: 02. Jan. 2024

Seitenbereich: 97 - 114

Eingereicht: 22. Aug. 2023

Akzeptiert: 20. Sept. 2023

DOI: https://doi.org/10.2478/fiqf-2023-0029

SchlüsselwörterESG Volatility, GARCH, LSTM model

© 2023 Akshay Kumar Mishra et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
ESG Volatility, GARCH, LSTM model