IDEAS home Printed from https://ideas.repec.org/a/vrs/finiqu/v19y2023i4p97-114n3.html
   My bibliography  Save this article

ESG Volatility Prediction Using GARCH and LSTM Models

Author

Listed:
  • Mishra Akshay Kumar

    (1 Jaipuria Institute of Management, India)

  • Kumar Rahul

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

  • Bal Debi Prasad

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

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.

Suggested Citation

  • Mishra Akshay Kumar & Kumar Rahul & Bal Debi Prasad, 2023. "ESG Volatility Prediction Using GARCH and LSTM Models," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 19(4), pages 97-114, December.
    • Handle: RePEc:vrs:finiqu:v:19:y:2023:i:4:p:97-114:n:3
    DOI: 10.2478/fiqf-2023-0029
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/fiqf-2023-0029
    Download Restriction: no
    File URL: https://libkey.io/10.2478/fiqf-2023-0029?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ESG Volatility; GARCH; LSTM model;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • G34 - Financial Economics - - Corporate Finance and Governance - - - Mergers; Acquisitions; Restructuring; Corporate Governance
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:finiqu:v:19:y:2023:i:4:p:97-114:n:3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.