{
  "abstract": "Forecasting stock price volatility at the individual stock level is a major challenge in managing financial risks; it has widespread implications for portfolio strategies, pricing derivatives, and assessing regulatory capital adequacy. Although there have been a number of studies using market indices (e.g., VIX and associated methods) to study forecasting stock market volatility, relatively few studies have applied and evaluated a number of different types of machine learning methods to individual stock data in terms of actual (realised) volatility forecasting. This study fills that gap by presenting a comprehensive analysis of the relationship between stock price volatility and various machine learning techniques on a sample of ten well-known US stocks (across three sectors: technology, finance, and energy) based on data from January 2018 to December 2024 (1,759 trading days). We apply five modelling approaches: a classical GARCH(1,1) econometric model, a classical GJR-GARCH(1,1,1) econometric model, a Random Forest regressor, a two-layer Long Short-Term Memory (LSTM) network, and a two-layer Gated Recurrent Unit (GRU) network. For each of the four modelling techniques, we use a rolling 5-day annualised actual (realised) volatility forecast as our final outcomes. Evaluation of the prediction results of each of these models was measured using RMSE, MAE, QLIKE loss, parametric Value-at-Risk (VaR) measures, Expected Shortfall (ES), and the Kupiec backtest. We also performed an analysis of the prevailing market regime through K-Means clustering, created an ablation study using variables as features in the models to assess the importance of those variables, and developed a computational efficiency benchmark for each model. In terms of predicted errors, Random Forest was proven to yield very low predicted error (mean RMSE = 0.0892) in comparison to deep learning methods, plus Random Forest was able to be trained approximately 7 times faster than deep learning methods. GRU was ranked second and outperformed LSTM on most of the metrics used in this evaluation. GARCH models remain a viable option for estimating risk measures and calibrating VaR, especially during period of stability in the financial markets.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India."
  ],
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  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "R V Pragadeeshwaran",
      "orcid": "0009-0009-4932-3250"
    },
    {
      "affs": [
        0
      ],
      "name": "R Tarun Narayanan",
      "orcid": "0009-0000-9761-4700"
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    {
      "affs": [
        0
      ],
      "name": "R M Bhavadharini",
      "orcid": "0000-0002-3096-4885"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0224vzvbnqmka.bib",
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  "doi": "10.1371/journal.pone.0354643",
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  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
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  "native_subjects": [
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  "pdf_url": "https://europepmc.org/articles/PMC13719023?pdf=render",
  "pmcid": "PMC13719023",
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  "published": "2026-10-29",
  "publisher": "Public Library of Science",
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  "title": "Stock-level volatility forecasting using machine learning: A comparative study of GARCH, Random Forest, LSTM, and GRU on US Equities",
  "title_text": "Stock-level volatility forecasting using machine learning: A comparative study of GARCH, Random Forest, LSTM, and GRU on US Equities",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T12:22:56Z",
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