GARCH models which capture volatility clustering and, therefore, are appropriate to analyse financial market data. Models …

Volatility (SV) and Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models which are both extended to include … outperforms the GARCH model. …

This paper attempts to fit the best Generalized Autoregressive Conditional Heteroscedastic (GARCH) model for All Share … Index (ASI) of Nigerian Stock Exchange (NSE) returns. A search is made on various GARCH variants specified on the … and non-normality of GARCH innovations, with models and forecasts evaluated using information criteria and loss functions …

(GARCH) models, of differing lag and parameter terms, to forecast the variance of the market used in the denominator of the … squared forecast error (MSE) were used to compare the forecasting ability of the ex-ante GARCH models, Artificial Neural …

Index World (D.J.S.I.-World). By using the model of Generalized Autoregressive Conditional Heteroskedasticity (GARCH), the …

Volatility (SV) and Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models which are both extended to include … outperforms the GARCH model. …

(GARCH) models, of differing lag and parameter terms, to forecast the variance of the market used in the denominator of the … squared forecast error (MSE) were used to compare the forecasting ability of the ex-ante GARCH models, Artificial Neural …

This paper attempts to fit the best Generalized Autoregressive Conditional Heteroscedastic (GARCH) model for All Share … Index (ASI) of Nigerian Stock Exchange (NSE) returns. A search is made on various GARCH variants specified on the … and non-normality of GARCH innovations, with models and forecasts evaluated using information criteria and loss functions …

use a collection of ARCH models (GARCH, EGARCH and TARCH) based on three distributional assumptions (Normal, Student-T and … Skewed Student-T), while we combine the Extreme Value Theory with a volatility updating technique (via GARCH type …