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 …

In this paper we compared two distinct volatility forecasting approaches. GARCH models were contrasted to the models …

processes with up to two lags and variance with one of GARCH, EGARCH or TARCH processes with up to two lags. The models are … separately. The primary result of the paper is that the volatility is best modelled using a GARCH process and that an ARMA …

quarterly basis - for commonly used GARCH models in a large-scale study, using more than twelve years (2000-2012) of daily … following conclusions. First, updating the parameter estimates of the GARCH equation on a daily frequency improves only … overlap, reflecting that the performance is not significantly different. Second, the asymmetric GARCH model with non …

-correction method can improve the n-GARCH and n-EGARCH VaR forecasts so much that the acquired VaR predictions are different from the … distribution instead of GARCH improves the performance of the bias-correction method in forecasting the VaR for almost all …

for conditional heteroskedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH/GARCH …