An efficient and accurate approach is proposed for forecasting Value at Risk [VaR] and Expected Shortfall [ES] measures … outperforms several alternative approaches in the sense of more accurate VaR and ES estimates given the same amount of computing …

Out-of-sample forecasting tests of DSGE models against time-series benchmarks such as an unrestricted VAR are … improve forecasts from an unrestricted VAR. In testing forecasting capacity they also have quite weak power, particularly on … improve on VAR forecasts. …

these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the …-data models at 5% and 1% VaR level. Specifically, independently from the data frequency, allowing for jumps in price (or providing … fat-tails) and leverage effects translates in more accurate VaR measure. …

estimationmethods of the DSGE model with the forecasts produced by a VAR and a Bayesian VAR. Second, we propose a new method for … combining DSGE and VAR models (in what we have called Augmented VARDSGE) through the expansion of the variable space where the … VAR operates with artificial series obtained from a DSGE model. The results indicate that the out-of-sample forecasting …

purpose, vector autoregressive (VAR) models are specified and estimated to construct forecasts. As the potential number of … lags included is large, we compare full's specified VAR models with subset models obtained using a Genetic Algorithm …

-used probit approach, but the dynamics of regressors are endogenized using a VAR. The combined model is called a ‘ProbVAR’. At any …