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 …

, namely, GARCH (1,1), GJR (1,1) and EGARCH (1,1), are used to estimate the abnormal rate of change in the number of tourists …

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

cryptocurrencies, i.e. Bitcoin, Ethereum, Ripple and Litecoin. More than 1,000 GARCH models are fitted to the log returns of the … well as using a Model Confidence Set (MCS) procedure for their loss functions. The results imply that using standard GARCH …

trading as a policy measure. Applying combined jump GARCH models yields strong evidence of conditional jump behavior. This …

GARCH in order to characterize the behavior of daily, weekly as well as monthly oil prices. Secondly, it relates its … characterized by GARCH as well as conditional jump behavior and that a considerable portion of the total variance is triggered by …

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

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 …

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 …