GAS-package: Generalized Autoregressive Score models in R

In GAS: Generalized Autoregressive Score Models

Description

The GAS package allows us to simulate, estimate and forecast using univariate and multivariate Generalized Autoregressive Score (GAS) models (also known as Dynamic Conditional Score (DCS) models), see e.g., Creal et. al. (2013) and Harvey (2013). A detailed implementation of the package functionalities are reported in Ardia et. al. (2018, 2019).

Details

The authors acknowledge Google for financial support via the Google Summer of Code 2016 project "GAS"; see https://summerofcode.withgoogle.com/archive/2016/projects/4537082387103744/.

Current limitations:

• The multivariate GAS model for N>4 does not report the exact update for the correlation parameters since the Jacobian of the hyperspherical coordinates transformation needs to be coded for the case N>4. The Jacobian for N>4 is replaced by the identity matrix.

Note

By using GAS you agree to the following rules:

• You must cite Ardia et al. (2019) in working papers and published papers that use GAS. Use citation("GAS").

• You must place the following URL in a footnote to help others find GAS: https://CRAN.R-project.org/package=GAS.

• You assume all risk for the use of GAS.

Author(s)

Leopoldo Catania [aut,cre], Kris Boudt [ctb], David Ardia [ctb]

Maintainer: Leopoldo Catania <[e-mail]>

References

Ardia D, Boudt K and Catania L (2018). "Downside Risk Evaluation with the R Package GAS." R Journal, 10(2), 410-421. doi: 10.32614/RJ-2018-064.

Ardia D, Boudt K and Catania L (2019). "Generalized Autoregressive Score Models in R: The GAS Package." Journal of Statistical Software, 88(6), 1-28. doi: 10.18637/jss.v088.i06.

Creal D, Koopman SJ, Lucas A (2013). "Generalized Autoregressive Score Models with Applications." Journal of Applied Econometrics, 28(5), 777-795. doi: 10.1002/jae.1279.

Harvey AC (2013). Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series. Cambridge University Press.

GAS documentation built on Feb. 4, 2022, 5:12 p.m.