tvgarch: Time Varying GARCH Modelling

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. In the multivariate case, variances are estimated equation by equation and dynamic conditional correlations are allowed. The TV long-term component of the variance as in the multiplicative TV-GARCH model of Amado and Ter\"{a}svirta (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.

Version: 2.0
Depends: R (≥ 3.5.0), garchx, zoo, matrixStats, numDeriv, hier.part
Published: 2021-04-16
Author: Susana Campos-Martins [aut, cre], Genaro Sucarrat [ctb]
Maintainer: Susana Campos-Martins <susana.martins at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
CRAN checks: tvgarch results


Reference manual: tvgarch.pdf
Package source: tvgarch_2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): tvgarch_2.0.tgz, r-release (x86_64): tvgarch_2.0.tgz, r-oldrel: tvgarch_2.0.tgz
Old sources: tvgarch archive


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