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

-day periods are considered. It is studies whether the same GARCH type model can be applied for the whole period, or whether the …

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

Linear GARCH(1,1) and threshold GARCH(1,1) processes are established as regularly varying, meaning their heavy tails … considered a stylized fact for many financial returns assumed to follow GARCH-type processes. The result in this note aids in … establishing the asymptotic properties of certain GARCH estimators proposed in the literature …

investigated the sources of heavy tails by estimating autoregressive models in which innovations can be subject to GARCH effects … both within- and out-of-sample. We found strong evidence in favour of modelling both GARCH effects and non …

/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 …

such as Generalized Autoregressive Conditional Heteroskedastic (GARCH), Generalized Autoregressive Score (GAS), and … inclusion of exogenous variables is beneficial for GARCH-type models while offering only a marginal improvement for GAS and SV …