Dear Alexios,
Thanks a lot! The suggestions work well.
Another issue is that, whenever I insert external variable in the model,
the model fit test (AIC, HQ, etc.) and P value of Q-Statistics on
Standardized Residuals are significantly increase. It seems like the
insertion of external regressor implies serial correlations in the
residual. How to handle this problem?
Best regards,
Dessy
Am 3/21/2014 2:44 PM, schrieb alexios ghalanos:
You are obviously trying to capture a permanent "level" shift in your
rice dataset, but the shift is from a high to a low state (i.e. the
coefficient would need to be negative). Since the bounds on the
coefficients of the external regressors (in the conditional variance
equation) are preset to be positive in order to "guarantee" positivity
of the variance, you need to do one of the following:
1. Reverse the order of the dummy i.e. 1:652 = 1, 652:n.row=0.
2. Set the bounds on vxreg:
setbounds(spec2)<-list(vxreg1=c(-1,1))
3. Use the eGARCH model.
All tested, all working.
And DO NOT USE c(model="fGARCH", submodel="GARCH"). Use directly
model="sGARCH".
-Alexios
On 21/03/2014 13:07, Dessy Anggraeni wrote:
Hi all,
I use rugarch package 1.2-9 to measure rice price volatility volatility
with GARCH model including external regressor in the variance model.
This package works well, however, it seems like the result doesn't
response to change in value of external regressors.
I test 2 external regressors:
1.A matrix of dummy variable containing only 0 and 1
2.Cross-sectional stock level to price return data (attached)
Here is the code that I use. Can anybody advise me what's wrong?
Thanks a lot in advance.
Best Regards,
Dessy
*1.**With dummy variable (0 and 1) as external regressor*
# where the dummy variable is define as:
mat <- matrix(, nrow = n.row, ncol = 1)
for(i in 1:652){
mat[i,] <- 0
}
for(i in 501:n.row){
mat[i,] <- 1
}
spec2 <- ugarchspec(
variance.model =
list(model = "fGARCH", garchOrder = c(1,1), submodel = "GARCH",
external.regressors = mat, variance.targeting = FALSE),
mean.model =
list(armaOrder = c(12,0), external.regressors = NULL),
distribution.model = "norm")
fit = ugarchfit(spec = spec2, data=xz)
*2.**With crosssectional stock level estimation as external regressor*
# matrix m is cross sectional stock level data
m <- matrix(cross)
spec2 <- ugarchspec(
variance.model =
list(model = "fGARCH", garchOrder = c(1,1), submodel = "GARCH",
external.regressors = m, variance.targeting = FALSE),
mean.model =
list(armaOrder = c(12,0), external.regressors = NULL),
distribution.model = "norm")
fit = ugarchfit(spec = spec2, data=xz)
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