variables, interest rates, and inflation rate on two Islamic stock market indices. Using time series analysis such as GARCH the …

price volatility, and compares their forecasting performance to the standard GARCH, fractionally integrated GARCH (FIGARCH …) and the two-state Markov-switching GARCH (MS-GARCH) models via three loss functions (the mean squared error, the mean … criteria and forecast horizons, while MS-GARCH mostly comes out as the least successful model. Applying various VaR backtesting …

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 investigates the forecasting performance of three popular variants of the non-linear GARCH models, namely VS-GARCH …, GJR-GARCH and Q-GARCH, with the symmetric GARCH(1,1) model as a benchmark. The application involves ten European stock … price indexes. Forecasts produced by each non-linear GARCH model and each index are evaluated using a common set of …

heteroskedasticity (GARCH) models capture extreme events in stock market returns. We estimate Hill's tail indexes for individual S&P 500 … stock market returns ranging from 1995-2014 and compare these to the tail indexes produced by simulating GARCH models. Our … results suggest that actual and simulated values differ greatly for GARCH models with normal conditional distributions, which …

generalized autoregressive conditional heteroscedasticity (GARCH) models are applied. Macroeconomic shocks (GDP, ZEW, IFO, factory …