Value-at-Risk Prediction in R with the GAS Package. 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.
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References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Jiménez, Inés; Mora-Valencia, Andrés; Perote, Javier: Risk quantification and validation for Bitcoin (2020)
- Boudt, Kris; Galanos, Alexios; Payseur, Scott; Zivot, Eric: Multivariate GARCH models for large-scale applications: a survey (2019)
- David Ardia; Keven Bluteau; Kris Boudt; Leopoldo Catania; Denis-Alexandre Trottier: Markov-Switching GARCH Models in R: The MSGARCH Package (2019) not zbMATH
- David Ardia; Kris Boudt; Leopoldo Catania: Generalized Autoregressive Score Models in R: The GAS Package (2019) not zbMATH
- David Ardia, Kris Boudt, Leopoldo Catania: Value-at-Risk Prediction in R with the GAS Package (2016) arXiv