  GAS models have been recently proposed in time-series econometrics as
valuable tools for signal extraction and prediction. This paper details how
financial risk managers can use GAS models for Value-at-Risk (VaR) prediction
using the novel GAS package for R. Details and code snippets for prediction,
comparison and backtesting with GAS models are presented. An empirical
application considering Dow Jones Index constituents investigates the VaR
forecasting performance of GAS models.
