-Generalized Autoregressive Conditional Heteroskedasticity (STAR-GARCH) can be problematic due to computational difficulties. Conventional … makes Quasi-Maximum Likelihood Estimator (QMLE) difficult to obtain for STAR-GARCH models in practice. Curiously, there has … STAR-GARCH using QMLE. The aim of the paper is to investigate the nature of the numerical difficulties using Monte Carlo …

delayed response is derived. A special case of continuous-time version of GARCH is considered. The results are compared with …

the symmetric AR(1)-GARCH(1, 1), the asymmetric AR(1)-GJR(1, 1), and asymmetric AR(1)-EGARCH(1, 1). Of these, the …

predicting static VaR, CVaR or expected shortfall (ES) and expected return level and also daily VaR using a GARCH(1,1) and EVT …

. Econometrics 91 (1999) 113–144] has suggested that the examination of the unit root hypothesis in series exhibiting GARCH behaviour … should proceed via joint maximum likelihood (ML) estimation of the unit root testing equation and GARCH process. The results … empirical research. In particular, the influences of sample size, alternative values of the parameters of the GARCH process and …

Conventional GARCH modeling formulates an additive-error mean equation for daily return and an autoregressive moving …’s multiplicative-error model (MEM) formulation, range-based volatility is proposed as an intraday proxy for several GARCH frameworks …, are studied and compared. The impact of significant changes in intraday data has been found to reflect in the MEM-GARCH …

volatility model like a GARCH (or one of its many incarnations) followed by application of standard extreme value models to the …

-varying conditional volatility, jumps and the component factors of volatility. It adopts a component-GARCH-Jump, which can efficiently …