with time-varying conditional ranges generated from conditional autoregressive range (CARR) model of Chou (2005). The …

sub-samples corresponding to different stages of the Romanian financial markets evolution. The GARCH models employed in …

, but the data generating process is shown to be non-linear. A non-linear GARCH model is then applied, achieving a good …

clustering and leptokurtosis are well-documented characteristics of such time series. An ARMA (1, 1)-GARCH (1, 1) ap- proach …, the ARMA-GARCH process is run assuming alternatively that the standardized residuals are distributed with Pearson Type IV …, Johnson SU, Manly’s exponential transformation, normal and t-distributions. In the second ap- proach, the ARMA-GARCH process …

– Modified GARCH model (c) Return and Volatility - ARMA-GARCH in mean model – Innovations Model. The findings of the paper …

periods. GARCH (1, 1) was deployed for investigating the possible eventualities of volatilities of stock markets. The findings …, almost for all countries GARCH (1, 1) yielded significant results confirming the existence of volatility of stock markets for …

heteroskedasticity. GARCH models have thus become very popular, given their ability to account for volatility clustering and, implicitly … weaknesses we apply GARCH-type models with alpha-stable innovations. The stable family of distributions constitutes a … GARCH(1,1) and a TGARCH(1,1) with symmetric stable shocks using as auxiliary model a GARCH(1,1) with skew-t innovations …

Union and by the effects of the global crisis. We use GARCH models to identify the monthly seasonality in returns and in …

their forecasting performance of the conditional variance in an out-of-sample setting. Exponential GARCH model of Nelson … best models are the ones that can accommodate a leverage effect. Results from fitting the selected exponential GARCH model …