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 …

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 …

Estimation of GARCH models can be simplified by augmenting quasi-maximum likelihood (QML) estimation with variance … volatility equation and corresponding value-at-risk predictions. We find that most GARCH coefficients and associated predictions …

We apply the GARCH-MIDAS framework to forecast the daily, weekly, and monthly volatility of five highly capitalized …

(GARCH), asymmetric power ARCH (APARCH), exponential generalized autoregressive conditional heteroscedstic (EGARCH …-of-sample volatility forecasting, AR(2)–GARCH(1, 1) is considered the best. …

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

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

-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 …