The extension of GARCH models to the multivariate setting has been fraught with difficulties. In this paper, we suggest … to work with univariate portfolio GARCH models. We show how the multivariate dimension of the portfolio allocation … derive the sensitivity of the univariate portfolio GARCH variance to the portfolio weights, by analytically computing the …

Theory. We utilise the GARCH-EVT approach in combination with a novel algorithm to automatically determine the optimal …

methods. The effects ofseveral modelcharacteristics (unit roots, GARCH, stochastic volatility, heavy taileddisturbance …

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 …

, it is important to model and quantify it. The conditional volatility models from the GARCH family and tail …

forecasting accuracy and risk management efficiency. The results demonstrate that the RNN outperforms GARCH and EWMA in average …

several different multivariate GARCH models (dynamic conditional correlation (DCC), asymmetric DCC (ADCC), generalized … orthogonal GARCH (GO-GARCH)) to estimate minimum variance equity portfolios. Both long and short portfolios are considered. An …

-based Predictive Model (EFPM). Then, we combine it with the Copula-GARCH simulation model and the Mean-Conditional Value at Risk (Mean …

methods. The effects of several model characteristics(unit roots, GARCH, stochastic volatility, heavy tailed …