the Vector Error Correction Model (VECM) , Multivariate VAR (p), Multivariate-VARX (p) and Multivariate VAR (p)-GARCH (q …

at Risk (VaR) calculation entails a numerically indirectprocedure. The Quantile Regression (QR) estimation is an …-KF) based on the QR approach thatcan be used to obtain robust SV model parameter estimates as well as VaR estimates. TheRQMM is … existing Nonlinear Filtering (NF) scheme.This approach is used in likelihood and VaR computations. This algorithm provides …

This paper develops a structural VAR model to measure how a shock to one country can affect the GDP of other countries …

Extracting the principles associated with complexity theory and swarm intelligence has offered practical solutions for …

markets for the two classes of shares. Value-at-Risk (VaR) threshold forecasts are used to analyse the importance of … information flow on the risk evaluation of a diversified portfolio. The competing VaR forecasts are analysed using the …

business decisions. A relatively new technique for performing valuation under uncertainty, Value at Risk (VaR), has been … developed in the financial world. VaR is a method of evaluating the probability of a gain or loss by a complex venture, by … examining the stochastic behavior of its components. We believe that combining quantitative risk assessment techniques with VaR …

explizit ökonomische Theorie zur Datenanalyse verwendet. Zur Identifikation makroökonomischer Schocks verwende ich sowohl … explicitly involves economic theory in the estimation procedure. To identify shocks I employ both sign restrictions derived from … technology shocks. The suggested approach results in a factor generalization of the DSGE-VAR methodology of Del Negro and …