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Esg Volatility Prediction Using Garch And Lstm Models

Author

Listed:
  • AKSHAY KUMAR MISHRA

    (Jaipuria Institute of Management, India)

  • RAHUL KUMAR

    (Birla Institute of Technology and Science–Pilani (BITS–Pilani), India)

  • DEBI PRASAD BAL

    (Birla Institute of Technology and Science–Pilani (BITS–Pilani), India)

Abstract

This study aims to predict the ESG (environmental, social, and governance) return volatilitybased on ESG index data from 26 October 2017 and 31 March 2023 in the case of India. In thisstudy, we utilized GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and LSTM(Long Short-Term Memory) models for forecasting the return of ESG volatility and to evaluatethe model’s suitability for prediction. The study's findings demonstrate the GARCH effect insidethe 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 ismore appropriate than the LSTM model.

Suggested Citation

Handle: RePEc:wsz:fiq000:v:19:y:2023:i:4:id:459
DOI: 10.2478/fiqf-2023-0029
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