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Volatility in the stock market: ANN versus parametric models

Author

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  • Rita Laura D’Ecclesia

    (Sapienza University of Rome)

  • Daniele Clementi

    (Sapienza University of Rome)

Abstract

Forecasting and adequately measuring equity returns volatility is crucial for portfolio selection and trading strategies. Implied volatility is often considered to be informationally superior to the realized volatility. When available, implied volatility is largely used by practitioners and investors to forecast future volatility. To this extent we want to identify the best approach to track equity returns implied volatility using parametric and ANN approaches. Using daily equity prices and stock market indices traded on major international Exchanges we estimate time varying volatility using the E-GARCH approach, the Heston model and a novel ANN framework to replicate the corresponding implied volatility. Overall the ANN approach results the most accurate to track the equity returns implied volatility.

Suggested Citation

  • Rita Laura D’Ecclesia & Daniele Clementi, 2021. "Volatility in the stock market: ANN versus parametric models," Annals of Operations Research, Springer, vol. 299(1), pages 1101-1127, April.
    • Handle: RePEc:spr:annopr:v:299:y:2021:i:1:d:10.1007_s10479-019-03374-0
    DOI: 10.1007/s10479-019-03374-0
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    2. Guo, Xiaozhu & Huang, Dengshi & Li, Xiafei & Liang, Chao, 2023. "Are categorical EPU indices predictable for carbon futures volatility? Evidence from the machine learning method," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 672-693.
    3. Erdinc Akyildirim & Aurelio F. Bariviera & Duc Khuong Nguyen & Ahmet Sensoy, 2022. "Forecasting high-frequency stock returns: a comparison of alternative methods," Annals of Operations Research, Springer, vol. 313(2), pages 639-690, June.
    4. Shiying Tu & Jiehu Huang & Huailong Mu & Juan Lu & Ying Li, 2024. "Combining Autoregressive Integrated Moving Average Model and Gaussian Process Regression to Improve Stock Price Forecast," Mathematics, MDPI, vol. 12(8), pages 1-15, April.
    5. Avraham Turgeman & Claudiu Botoc & Marilen Pirtea & Octavian Jude, 0000. "Modelling Intraday Realized Volatility: The Role Of Vix, Oil And Gold," Proceedings of Economics and Finance Conferences 14115804, International Institute of Social and Economic Sciences.
    6. Brahmana, Rayenda Khresna, 2022. "Do Machine Learning Approaches Have the Same Accuracy in Forecasting Cryptocurrencies Volatilities?," MPRA Paper 119598, University Library of Munich, Germany.
    7. Lamine Diane & Pradeep Brijlal, 2024. "Forecasting Stock Market Realized Volatility using Random Forest and Artificial Neural Network in South Africa," International Journal of Economics and Financial Issues, Econjournals, vol. 14(2), pages 5-14, March.

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