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Table 1

GARCH models modification and their applications to oil market volatility.

Model Feature Model Feature
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.

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