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 the case of India. In this study, we utilized GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and LSTM (Long Short-Term Mem...

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Bibliographic Details
Published inFinancial Internet Quarterly Vol. 19; no. 4; pp. 97 - 114
Main Authors Mishra, Akshay Kumar, Kumar, Rahul, Bal, Debi Prasad
Format Journal Article
LanguageEnglish
Published Sciendo 01.12.2023
Subjects
C53
D53
ESG Volatility
G34
GARCH
LSTM model
O13
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Summary: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.
ISSN:2719-3454
2719-3454
DOI:10.2478/fiqf-2023-0029