Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul Stock Exchange

Forecasting volatility in oil prices with a class of nonlinear volatility models: smooth transition RBF and MLP neural networks augmented GARCH approach

“…NN modeling methodology is applied successfully by Wang et al (2005) for forecasting the value of a stock index. Bildirici and Ersin (2009) modeled NN-GARCH family models to forecast daily stock returns for short-and long-run horizons and they showed that GARCH models under NN architecture provide significant forecasting performance. Chan and McAleer (2002) discussed the STAR-GARCH model that has STAR type nonlinearity in the conditional mean process.…”

“…For details regarding weight decay in learning process, an investigation is given by Gupta and Lam (1998). The algorithm cooperation and early stopping for NN-GARCH processes are given in Bildirici and Ersin (2009). The algorithm used in the study could be taken as estimating neural network models with LSTAR type nonlinear structures with different number of neurons in the hidden layer, this means estimating models with different architecture variations.…”

“…Hutchinson et al (1994) showed that the learning networks could be used efficiently for pricing and hedging in securities markets. Studies such as Stengos (1997, 1998), Gencay and Liu (1997), Kanas (2003), Kanas and Yannopoulos (2001), Shively (2003) and Bildirici and Ersin (2009) applied ANN models to stock market return forecasting and financial analysis. The MLP model is evaluated as an important class of neural network models.…”

“…To obtain parsimony, best model is further selected with respect to the AIC information criterion (see Faraway and Chatfield 1998). For estimating NN-GARCH models with early stopping combined with algorithm corporation, readers can refer to Bildirici and Ersin (2009).…”

Financial Volatility Forecasting by Least Square Support Vector Machine Based on GARCH, EGARCH and GJR Models: Evidence from ASEAN Stock Markets

“…Donaldson and Kamstra (1997) proposed neural network to model volatility based GJR-GARCH; their hybrid approach captured asymmetric effects of new impact well like parametric model and also generated better forecasting accuracy. Bildirici & Ersin(2009) fitted neural network based on nine different models of GARCH family such as NN-GARCH, NN-EGARCH, NN-TGARCH, NN-GJR, NN-SAGARCH, NN-PGARCH, NN-NGARCH, NN-APGARCH, and NN-NPGARCH to forecast Istanbul stock volatility and most of the hybrid models improved forecasting performance. This indicates that the hybrid model is also able to capture the stylized characteristics of return.…”