We extend recently introduced latent threshold dynamic models to include dependencies among dynamic latent factors underlying multivariate volatility. With an ability to induce time-varying sparsity into factor loadings, these models now also allow time-varying correlations among factors; this may be exploited to improve volatility forecasts. We couple multi-period, out-of-sample forecasting wi...

in this paper we compared multivariate garch models toestimate value-at-risk. we used a portfolio of weekly indexesincluding tedpix, klse, xu100 during ten years. to estimatevalue-at-risk, first we estimated ccc, dcc of engle, dcc of tseand tsui, dynamic equi correlation models by oxmetrics. then,optimum lags were estimated by minimizing the information criteria.to estimate var, the models accu...

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...

Dynamic network data envelopment analysis (DNDEA) has attracted a lot of attention in recent years. On one hand the available models in DNDEA evaluating the performance of a DMU with interrelated processes during specified multiple periods but on the other hand they can only measure the efficiency of dynamic network structure when a supply chain structure present. For example, in the banking in...