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A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting

In: Advances in Financial Risk Management

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  • Leandro Maciel

Abstract

Accurately measuring and forecasting stock market volatility plays a crucial role for asset and derivative pricing, hedge strategies, portfolio allocation and risk management. Since the 1987 stock market crash, academics, practitioners and regulators have investigated the development of financial time series models with changing variance over time in order to avoid huge investment losses due to their exposure to unexpected market movements (Allen and Morzuch, 2006, Carvalho et al., 2005, Lin et al., 2012). Indeed, volatility, as a measure of financial security prices fluctuation around its expected value, is one of the primary inputs in decision making processes under uncertainty, justifying its growing interest in the financial and economic literature (Kapetanios et al., 2006, Lux and Kaizoji, 2007).

Suggested Citation

  • Leandro Maciel, 2013. "A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting," Palgrave Macmillan Books, in: Jonathan A. Batten & Peter MacKay & Niklas Wagner (ed.), Advances in Financial Risk Management, chapter 11, pages 253-283, Palgrave Macmillan.
    • Handle: RePEc:pal:palchp:978-1-137-02509-8_11
    DOI: 10.1057/9781137025098_11
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    Cited by:

    1. Leandro Maciel & Fernando Gomide & Rosangela Ballini, 2016. "Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 379-398, October.
    2. Fahad Mostafa & Pritam Saha & Mohammad Rafiqul Islam & Nguyet Nguyen, 2021. "GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies," JRFM, MDPI, vol. 14(9), pages 1-22, September.

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    More about this item

    Keywords

    Particle Swarm Optimization; Stock Market; Differential Evolution; Particle Swarm Optimization Algorithm; Differential Evolution Algorithm;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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