mtvgarch {tvgarch} | R Documentation |
Equation by equation estimation of a multivariate multiplicative TV-GARCH-X model with dnamic conditional correlations. For each variance equation, the long-term or unconditional component (TV) and the short-term or conditional variance component (GARCH-X) are estimated separately using maximization by parts, where the iterative algorithm proceeds until convergence. Conditional on the variance estimates, the dynamic conditional correlations are estimated by maximum likelihood.
mtvgarch(y, order.g = c(1, 1), order.h = NULL, order.x = NULL,
initial.values = list(), xtv = NULL, xreg = NULL, opt = 2, upper.speed = NULL,
tvgarch = FALSE, dcc = FALSE, turbo = TRUE, trace = FALSE)
y |
numeric matrix, time series or |
order.g |
integer matrix with each row indicating the order.g for each series; number of locations in each transition function of the TV components. |
order.h |
integer matrix with each row indicating the order.h for each
series; the first column controls the GARCH order, the second the ARCH order and
the third the asymmetry order of the GARCH-X components. If |
order.x |
|
initial.values |
a list containing the initial parameter values passed on
to the optimisation routines (constrOptim for the TV component and
nlminb for the GARCH-X component). If list(), the default, then the
values are chosen automatically. TV component: |
xtv |
|
xreg |
numeric vector, time series or zoo object to include as covariates in the GARCH-X component. |
opt |
integer indicating whether the speed parameter in the TV component should be scaled. If 0, no scaling; if 1, speed/sd(xtv); if 2, exp(speed). |
upper.speed |
|
tvgarch |
|
dcc |
logical. If |
turbo |
logical. If |
trace |
logical. If |
An object of class 'mtvgarch'.
Susana Campos-Martins
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.
Christian Francq and Jean-Michel Zakoian (2016) Estimating multivariate volatility models equation by equation, J. R. Stat. Soc. Ser. B Stat. Methodol 78, 613-635.
Robert F. Engle (2002) Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models, Journal of Business and Economic Statistics 20, 339-350.
tvgarch
,
garchx
,
nlminb
,
constrOptim
set.seed(12345)
## Simulate from a bivariate CCC-TV(1)-GARCH(1,1) model (default):
mySim <- mtvgarchSim(n = 1000)
## Estimate a CCC-TV(1)-GARCH(1,1) model (default):
myEst <- mtvgarch(y = mySim)
## Print estimation results:
print(myEst)
## Extract coefficients:
coef(myEst)
## Plot conditional volatilities:
plot(myEst)
## Generate predictions:
predict(myEst)