of ARCH effect has been tried to predict with conditional variance models such as ARCH (1), ARCH (2), ARCH (3), GARCH (1 …,1), GARCH (1,2), GARCH (1,3), GARCH (2,1), GARCH (2,2), EGARCH (1,1) and EGARCH (1,2). While the obtained findings indicate that … the best model is in the direction of GARCH (1,1) according to Akaike info criterion, it was found that GARCH (1,1) model …

Heteroskedasticity (ARCH) effects existence. For this reason Generalized GARCH models are estimated. Two approaches are followed. The … higher seasonality in volatility rather on average returns. For this reason the Periodic-GARCH (1,1) is estimated. The …

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 …

This paper examines exchange-rate volatility with GARCH models using monthly exchange-rate return series from 1985:1 to … compare estimates of variants of GARCH models with break in respect of the US dollar rates with exogenously determined break … estimation of volatility models with breaks as against those of GARCH models without volatility breaks and that the introduction …

(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 …

validate this result. The last twenty eight days out-of-sample forecast adjudged Power-GARCH (1, 1, 1) in student's t error …

Smooth Transition GARCH and the Markov-Switching GARCH models. Simulation experiments reveal that information criteria and …

Estimation of GARCH models can be simplified by augmenting quasi-maximum likelihood (QML) estimation with variance … volatility equation and corresponding value-at-risk predictions. We find that most GARCH coefficients and associated predictions …

-Maximum Likelihood Estimator for a general nonlinear conditional mean model with first-order GARCH errors. …

for main indices from stock exchanges was conducted. The VaR forecasts from GARCH(1,1), GARCH-t(1,1), GARCH-st(1,1), QML-GARCH … volatility trend. However, GARCH-st (1,1) and QML-GARCH(1,1) were found to be the most robust models in the different volatility …