yuima (version 1.1.6)

Diagnostic.Cogarch: Function for checking the statistical properties of the COGARCH(p,q) model

Description

The function check the statistical properties of the COGARCH(p,q) model. We verify if the process has a strict positive stationary variance model.

Usage

Diagnostic.Cogarch(yuima.cogarch, param = list(), matrixS = NULL, mu = 1, display = TRUE)

Arguments

yuima.cogarch

an object of class yuima.cogarch, yuima or a class cogarch.gmm-class

param

a list containing the values of the parameters

matrixS

a Square matrix.

mu

first moment of the Levy measure.

display

a logical variable, if TRUE the function displays the result in the console.

Value

Examples

## Not run: 
# # Definition of the COGARCH(1,1) process driven by a Variance Gamma nois:
# param.VG <- list(a1 = 0.038,  b1 =  0.053,
#                   a0 = 0.04/0.053,lambda = 1, alpha = sqrt(2), beta = 0, mu = 0, 
#                   x01 = 50.33)
# 
# cog.VG <- setCogarch(p = 1, q = 1, work = FALSE,
#                       measure=list("rngamma(z, lambda, alpha, beta, mu)"),
#                       measure.type = "code", 
#                       Cogarch.var = "y",
#                       V.var = "v", Latent.var="x",
#                       XinExpr=TRUE)
# 
# # Verify the stationarity and the positivity of th variance process
# 
# test <- Diagnostic.Cogarch(cog.VG,param=param.VG)
# show(test)
# 
# # Simulate a sample path
# 
# set.seed(210)
# 
# Term=800
# num=24000
# 
# samp.VG <- setSampling(Terminal=Term, n=num)
# 
# sim.VG <- simulate(cog.VG,
#                     true.parameter=param.VG,
#                     sampling=samp.VG,
#                     method="euler")
# plot(sim.VG)
# 
# # Estimate the model
# 
# res.VG <- gmm(sim.VG, start = param.VG, Est.Incr = "IncrPar")
# 
# summary(res.VG)
# 
# #  Check if the estimated COGARCH(1,1) has a positive and stationary variance
# 
# test1<-Diagnostic.Cogarch(res.VG)
# show(test1)
# 
# # Simulate a COGARCH sample path using the estimated COGARCH(1,1) 
# # and the recovered increments of underlying Variance Gamma Noise
# 
# esttraj<-simulate(res.VG)
# plot(esttraj)
# 
# 
# ## End(Not run)