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Volatility Forecasting using Hybrid GARCH Neural Network Models: The Case of the Italian Stock Market

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  • Dimitrios Kartsonakis Mademlis

    (University of Macedonia, Egnatia 156, Thessaloniki 546 36, Greece,)

  • Nikolaos Dritsakis

    (University of Macedonia, Egnatia 156, Thessaloniki 546 36, Greece)

Abstract

In several financial applications, it is extremely useful to predict volatility with the highest precision. Neural Networks alongside GARCH-type models have been extensively employed in the last decades for estimating volatility of financial indices. The motivation of this survey is to decide whether combining different types of models can improve the return volatility forecasts. Thus, two hybrid models are utilized and compared with an asymmetric GARCH model and a Neural Network in terms of their ability to predict the volatility of the FTSE MIB index. The conclusions reveal that the hybrid model, which is based on a Neural Network having as inputs the returns and its historical values as well as the estimates of conditional volatility obtained by an EGARCH model, provides the best predictive power. Moreover, the dominance of this hybrid model is such that it forecast encompasses the remaining models. Finally, it is demonstrated that there are significant leverage effects in the Italian stock market.

Suggested Citation

  • Dimitrios Kartsonakis Mademlis & Nikolaos Dritsakis, 2021. "Volatility Forecasting using Hybrid GARCH Neural Network Models: The Case of the Italian Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 11(1), pages 49-60.
    • Handle: RePEc:eco:journ1:2021-01-5
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    References listed on IDEAS

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    Cited by:

    1. Shusheng Ding & Tianxiang Cui & Yongmin Zhang & Jiawei Li, 2021. "Liquidity effects on oil volatility forecasting: From fintech perspective," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-21, November.
    2. Virginie Terraza & Aslı Boru İpek & Mohammad Mahdi Rounaghi, 2024. "The nexus between the volatility of Bitcoin, gold, and American stock markets during the COVID-19 pandemic: evidence from VAR-DCC-EGARCH and ANN models," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-34, December.
    3. Fernando Moreno-Pino & Stefan Zohren, 2022. "DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions," Papers 2210.04797, arXiv.org, revised Aug 2024.

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    More about this item

    Keywords

    Artificial Neural Network; Forecast Encompassing; GARCH Models; Realized volatility; Stock Market; Volatility Forecast;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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