ESG Volatility Prediction Using GARCH and LSTM Models

• Autorzy: Akshay Kumar Mishra , Rahul Kumar , Debi Prasad Bal
• Języki publikacji: EN

Abstrakty

EN

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. (original abstract)

Słowa kluczowe

EN

Variability; Forecasting; GARCH model; Long-short term memory (LSTM)

PL

Zmienność; Prognozowanie; Model GARCH; Długa pamięć krótkotrwała (LSTM)

• Czasopismo: e-Finanse
• Rocznik: 2023
• Tom: 19
• Numer: nr 4
• Strony: 97--114

Twórcy

autor

Akshay Kumar Mishra

• Jaipuria Institute of Management, India

autor

Rahul Kumar

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

autor

Debi Prasad Bal

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

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Identyfikatory

DOI

10.2478/fiqf-2023-0029