ESG VOLATILITY PREDICTION USING GARCH AND LSTM MODELS

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.