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 …

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

A popular risk measure, conditional value-at-risk (CVaR), is called expected shortfall (ES) in financial applications …. The research presented involved developing algorithms for the implementation of linear regression for estimating CVaR as a … function of some factors. Such regression is called CVaR (superquantile) regression. The main statement of this paper is: CVaR …

with those from an alternative bivariate multifractal model proposed by Calvet et al. (2006) and the bivariate CC-GARCH of … Bollerslev (1990). As it turns out, the multifractal models provide much more reliable results than CC-GARCH, and our new model …

Correlation models, such as Constant Conditional Correlation (CCC) GARCH model or Dynamic Conditional Correlation (DCC …) GARCH model, play a crucial role in forecasting Value-at-Risk (VaR) or Expected Shortfall (ES). The additional inclusion of …Korrelationsmodelle, z.B. GARCH-Modell der konstanten bedingten Korrelation (CCC) oder das GARCH-Modell der dynamischen …