GARCH models modification and their applications to oil market volatility.
|IGARCH||Is better equipped to account for this long memory in the volatility due to the integration, of the variance equation coefficients of standard GARCH, over lag variables.||GARCH_M||Allows for the mean of the returns to be a function of the conditional volatility.|
|GARCH (FIGARCH)||It captures long memory shocks effectively and provides a slow decay of shocks over time.||Multivariate GARCH||These models are useful when simultaneously computing the volatility of multiple assets.|
|TGARCH & GJR||Adds another residual term to the standard GARCH to account for asymmetrical behavior in volatility.||CGARCH||It improves the modeling of long-term effects by decomposing the model into long-run and short-run components.|
|EGARCH||Unlike the standard GARCH models often restrict coefficients to be positive, this model removes this restriction.||NN-GARCH||A hybrid model that incorporates neural networks with GARCH estimates the extreme values of volatility more effectively than GARCH models alone.|
|HYGARCH||Is a mixture of standard GARCH, IGARCH and FIGARCH models.|
Source: Matar et al. (2013), pp. 255.