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The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models

Author

Listed:
  • Natalia Roszyk

    (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group)

  • Robert Ślepaczuk

    (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance and Machine Learning)

Abstract

Predicting the S&P 500 index's volatility is crucial for investors and financial analysts as it helps in assessing market risk and making informed investment decisions. Volatility represents the level of uncertainty or risk related to the size of changes in a security's value, making it an essential indicator for financial planning. This study explores four methods to improve the accuracy of volatility forecasts for the S&P 500: the established GARCH model, known for capturing historical volatility patterns; an LSTM network that utilizes past volatility and log returns; a hybrid LSTM-GARCH model that combines the strengths of both approaches; and an advanced version of the hybrid model that also factors in the VIX index to gauge market sentiment. This analysis is based on a daily dataset that includes data for S&P 500 and VIX index, covering the period from January 3, 2000, to December 21, 2023. Through rigorous testing and comparison, we found that machine learning approaches, particularly the hybrid LSTM models, significantly outperform the traditional GARCH model. The inclusion of the VIX index in the hybrid model further enhances its forecasting ability by incorporating real-time market sentiment. The results of this study offer valuable insights for achieving more accurate volatility predictions, enabling better risk management and strategic investment decisions in the volatile environment of the S&P 500.

Suggested Citation

  • Natalia Roszyk & Robert Ślepaczuk, 2024. "The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models," Working Papers 2024-13, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2024-13
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    References listed on IDEAS

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    More about this item

    Keywords

    volatility forecasting; LSTM-GARCH; S&P 500 index; hybrid forecasting models; VIX index; machine learning; financial time series analysis; walk-forward process; hyperparameters tuning; deep learning; recurrent neural networks;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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