GARCH models which capture volatility clustering and, therefore, are appropriate to analyse financial market data. Models …

We proposed a method to estimate extreme conditional quantiles by combining quantile GARCH model of Xiao and Koenker …

used for the period January 1990 to December 2007. The study applies GARCH (1, 1) and T-GARCH (1, 1) to examine the …

constructed by (1) GARCH, (2) TGARCH, (3) Risk metrics and (4) Historical volatility. Volatility forecasts suggest that TGARCH … performs relatively best in term of MSPE, followed by GARCH, Risk metrics and historical volatility. In terms of VaR, we test …

, a RiskMetrics system and a variance-covariance approach using GARCH forecasts. Based on the backtesting results it can …

, a RiskMetrics system and a variance-covariance approach using GARCH forecasts. Based on the backtesting results it can …

average (EWMA), or generalized autoregressive conditional heteroskedasticity (GARCH) models. After estimating the volatility … delta normal VaR; the quadratic probability score (QPS) shows that the EWMA is inclined to underestimate the VaR for a … single series, and GARCH shows no difference from GARCH t and GARCH generalized error distribution (GED) for a multivariate …

violation backtesting for one-day VaR, to compare the efficiency of the SV, GARCH and EWMA volatility models (suggested by … RiskMetrics). The results suggest that VaR calculated considering EWMA was less violated than when considering SV and GARCH for a …This paper explores three models to estimate volatility: exponential weighted moving average (EWMA), generalized …

volatility prediction models is to accurately estimate volatility. In this study, MA, EWMA, GARCH (1,1) and IGARCH models have … results, it has been determined that EWMA model has yielded better estimates than GARCH(1,1) and IGARCH models in terms of …

emplean el EWMA y el TGARCH, se desempeñan bien por lo general, sin embargo, modelos tienen un comportamiento pobre cuando el …