| tvgarch-package {tvgarch} | R Documentation |
Time Varying GARCH Modelling
Description
Simulation, estimation and inference for univariate and multivariate TV(s)-GARCH(p,q,r)-X models, where s indicates the number and shape of the transition functions, p is the ARCH order, q is the GARCH order, r is the asymmetry order, and 'X' indicates that covariates can be included; see Campos-Martins and Sucarrat (2024) <doi:10.18637/jss.v108.i09>. The TV long-term component, as in the multiplicative TV-GARCH model of Amado and Terasvirta (2013) <doi:10.1016/j.jeconom.2013.03.006>, introduces non-stationarity whereas the GARCH-X short-term component describes conditional heteroscedasticity. Maximisation by parts leads to consistent and asymptotically normal estimates. In the multivariate case, conditional variances are estimated equation by equation and dynamic conditional correlations are allowed.
Details
| Package: | tvgarch |
| Type: | Package |
| Version: | 2.4.2 |
| Date: | 2024-04-03 |
| License: | GPL>=2 |
Author(s)
Susana Campos-Martins, https://sites.google.com/site/susanacamposmartins
Maintainer: Susana Campos-Martins
Contributor: Genaro Sucarrat
References
Cristina Amado and Timo Terasvirta (2013) Modelling volatility by variance decomposition, Journal of Econometrics 175, 142-153.
Susana Campos-Martins and Genaro Sucarrat (2024) Modeling Nonstationary Financial Volatility with the R Package tvgarch, Journal of Statistical Software 108, 1-38.
See Also
tvgarchTest,
tvgarch,
mtvgarch,
tvgarchSim,
mtvgarchSim
Examples
set.seed(123)
## Simulate from a TV(1)-GARCH(1,1) model (default):
ySim <- tvgarchSim(n = 1500)
## Test a GARCH(1,1) model against a TV(1)-GARCH(1,1) model:
yTest <- tvgarchTest(y = ySim)
yTest
## Estimate a TV(1)-GARCH(1,1) model (default):
yEst <- tvgarch(y = ySim)
yEst