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

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Advances in Financial Risk Management

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).

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Authors
  1. Leandro Maciel
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Editor information

Editors and Affiliations

  1. Monash University, Melbourne, Australia

    Jonathan A. Batten

  2. Hong Kong University of Science and Technology, Hong Kong

    Peter MacKay

  3. Department of Business and Economics, University of Passau, Germany

    Niklas Wagner

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© 2013 Leandro Maciel

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Maciel, L. (2013). A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting. In: Batten, J.A., MacKay, P., Wagner, N. (eds) Advances in Financial Risk Management. Palgrave Macmillan, London. https://doi.org/10.1057/9781137025098_11

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