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

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).