Applying a combined fuzzy systems and GARCH model to adaptively forecast stock market volatility

ProfARIMA: A profit-driven order identification algorithm for ARIMA models in sales forecasting

“…Parameter optimization significantly influences the performance of all forecasting techniques, from ARIMA models [49] to neural networks [62], and from Support Vector Regression [14] to GARCH models [30]. The forecast accuracy of these models has been improved by optimizing their input with evolutionary search heuristics, such as Particle Swarm Optimization [4, 32,60,62,63], Genetic Algorithms [22,30,39,42,43,49,54], Simulated Annealing [23,40], Artificial Bee Colony Algorithm [5, 24,47], Differential Evolution [25,57] and Fruit Fly Optimization [38,41].…”

“…The forecast accuracy of these models has been improved by optimizing their input with evolutionary search heuristics, such as Particle Swarm Optimization [4, 32,60,62,63], Genetic Algorithms [22,30,39,42,43,49,54], Simulated Annealing [23,40], Artificial Bee Colony Algorithm [5, 24,47], Differential Evolution [25,57] and Fruit Fly Optimization [38,41]. These hybrid methodologies have been applied to many different fields in forecasting, including tourism flow forecasting [14], electricity demand forecasting [63], rainfall prediction [60], price forecasting [47] and many others.…”

“…Especially the AIC value is often used when manually selecting the parameters of an AR(I)MA model [19,65]. However, the most popular criterion for optimization in forecasting is accuracy, which can take many forms, such as the Mean Squared Error (MSE) [1,4,5,30,34,54,62], the Mean Absolute Percentage Error (MAPE) [14,23,38,39,42,47,57,63] or the Root Mean Squared Error (RMSE) [22,25,27,38]. In this paper, however, we turn to a profit measure for sales forecasting to optimize the order identification of Seasonal ARIMA models.…”

An EGARCH-BPNN system for estimating and predicting stock market volatility in Morocco and Saudi Arabia: The effect of trading volume

“…Indeed, volatility and volume series are nonlinear and it is appropriate to approximate their relationship using nonlinear intelligent techniques such as artificial neural networks. In addition, BPNN has proven its capability to outperform traditional GARCH family models in the prediction of volatility (Hamid & Iqbal, 2004, Roh, 2007, Bildirici & Ersin, 2009Hung, 2011, Wang et al, 2011, Hajizadeh, 2012.…”

“…They concluded that in general the prediction accuracy depends on volatility models and number of neurons in the hidden layer, but are not significantly related to activation functions. Hung (2011) used a fuzzy system to analyze clustering in generalized autoregressive conditional heteroskedasticity (GARCH) models and genetic algorithms to estimate the parameters of the membership functions and the GARCH models. Using data from developed market (Germany, Canada, Japan, and USA) the simulations showed that the proposed method improved the forecasting accuracy in comparison with conventional GARCH and EGARCH model.…”