Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/329860 
Year of Publication: 
2023
Citation: 
[Journal:] Financial Internet Quarterly [ISSN:] 2719-3454 [Volume:] 19 [Issue:] 4 [Year:] 2023 [Pages:] 97-114
Publisher: 
Sciendo, Warsaw
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.
Subjects: 
ESG Volatility
GARCH
LSTM model
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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