Generalized Autoregressive Score (GAS) models have been recently proposed as valuable tools for signal extraction and prediction of time series processes with time–varying parameters. For financial risk managers, GAS models are useful as they take the non–normal shape of the conditional distribution into account in the specification of the volatility process. Moreover, they lead to a completely specified conditional distribution and thus to a straightforward calculation of the one–step ahead predictive Value–at–Risk (VaR). This paper shows how the novel GAS package for R can be used for Value–at–Risk (VaR) prediction and provides illustration using the series of log–returns of the Dow Jones Industrial Average constituents. Details and code snippets for prediction, comparison and backtesting with GAS models are presented.

Find out more at :

Download the GAS package from CRAN :