-based Predictive Model (EFPM). Then, we combine it with the Copula-GARCH simulation model and the Mean-Conditional Value at Risk (Mean-CVaR …, Sharpe ratio, maximum drawdown, and 99% CVaR. …

The GARCH(1,1) model and its extensions have become a standard econometric tool for modeling volatility dynamics of … financial returns and port-folio risk. In this paper, we propose an adjustment of GARCH implied conditional value-at-risk and … comparisons for a set of 18 stock market indices. In total, four competing copula-GARCH models are contrasted against each other …

This paper proposes efficient estimators of risk measures in a semiparametric GARCH model defined through moment …

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 …

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 …

application to VaR and ES forecasts for daily FTSE 100 index returns as generated by AR-GARCH, AR-GJR-GARCH, and AR-HEAVY models …

Volatility (SV) and Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models which are both extended to include … outperforms the GARCH model. …

implied conditional variance from a GARCH model as a measure of volatility. Although treating the volatility measure as either …