, such as GARCH models, are investigated, to determine if they are more appropriate for predicting future return volatility …

(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 …

error distribution were incorporated in the GARCH (2,1) and EGARCH (2,1) models. Result reveals that day-of-the-week effects …

nonlinear volatility models (symmetric and asymmetric Generalized AutoRegressive Conditional Heteroskedasticity [GARCH …

heteroscedasticity GARCH (1, 1) model. Also, to control if political uncertainty before the elections influences the return of MBI 10, a …

/methodology/approach - The copula-based GARCH (1,1) and minimum spanning tree models are used in this study to analyze 14 series of stock market … pandemic. Moreover, the study fills the literature gap by combining the copula-based GARCH and the minimum spanning tree models …

, Hong Kong and Taiwan), Japan and US. The originality of the paper is the use of STAR-GARCH models, instead of standard … correlation-cointegration techniques. For each country in the Pacific Basin region, we find statistically adequate STAR-GARCH …

stock market. We investigate its volatility dynamics through the GARCH model with three types of heavy-tailed distributions …