Many static and dynamic models exist to forecast Value-at-Risk other quantile-related metrics used in financial risk management. Industry practice favours simpler, such as historical simulation or its variants. Most academic research focuses on the GARCH family. While numerous studies examine accuracy of multivariate for forecasting metrics, there is little accurately predicting entire distribu...

This paper investigates the forecasting ability of five different versions of GARCH models. The five GARCH models applied are bivariate GARCH, GARCH-ECM, BEKK GARCH, GARCH-X and GARCH-GJR. Forecast errors based on four emerging stock futures portfolio return (based on forecasted hedge ratio) forecasts are employed to evaluate out-ofsample forecasting ability of the five GARCH models. Daily data...

Dynamic data detection is one of the main concerns in statistical process control (SPC) field. Here we focus on monitoring parametric multivariate dynamic streams using ARMAX-GARCH model, which reflects both influence exogenous variables mean vector and heterogeneity covariance matrix. A quasi maximum likelihood estimator used to estimate parameter a process, top-r scheme proposed monitor param...

Crude oil and natural gas are crucial to the Russian economy. Therefore, this study examined interconnections between crude price, economic policy uncertainty (EPU) over period 1994–2019 using multivariate DCC-MGARCH models. The findings show that there strong (co-movement) energy prices EPU in Russia, it might be misleading assume independence or neutrality variables. Although Russia is also a...