R help - Aug 2018 - RuGarch issue

Sup guys,

Got an interesting issue with the rugarch package.

I noticed that when I changed the order of the external regressors, there
are different values for the robust coefficient matrix. The values should be
the same (according to the ordering of the variables). However, I am getting
drastically different results. At that time the model was arma(2,2) +
garch(1,0).

Is this considered a normal behavior of the rugarch package? I assume that
when you change the ordering of the external regressors the output should be
exactly the same....digit by digit.

I confirmed this issue by creating a generic script that can be tested by
anyone. Has anybody faced this issue before or is there post that describes
the issue that I am facing?

  Maybe I am going insane...for now I will look further into the
documentation that our Alexios has provided

Thanks!
  library(rugarch)


set.seed(1)

x1 <- rnorm(1000,5,1)
x2 <- rnorm(1000,3,3)

y    <- .5*(x1*x2) + rnorm(1000,1,3)
dat  <- data.frame(x1,x2,y)

var1 <- c("x1","x2")
var2 <- c("x2","x1")

# setbounds(spec)<-list(vxreg1=c(-1,1))
model_maker <- function(x_name){
  temp <- dat[,c("y",x_name)]

  spec <- ugarchspec(variance.model      = list(model = "sGARCH",
                                                garchOrder = c(1,0)),

                     mean.model          = list(armaOrder = c(2,2),
                                                external.regressors
as.matrix(temp[,x_name]),
                                                include.mean= T),

                     distribution.model  = "std")

  fit         <- ugarchfit(spec = spec, data = as.matrix(temp$y),solver
"hybrid")
  return(fit at fit$robust.matcoef)}

model_maker(var1)
model_maker(var2)

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this post has been submitted to r-sig-finance

Galib Khan
Rutgers Business School '18
Business Analytics and Information Technology
(609) 412-3654

On Fri, Aug 17, 2018 at 2:27 PM, GALIB KHAN <ghk18 at
scarletmail.rutgers.edu>
wrote:
> Sup guys,
>
> Got an interesting issue with the rugarch package.
>
> I noticed that when I changed the order of the external regressors, there
> are different values for the robust coefficient matrix. The values should
> be
> the same (according to the ordering of the variables). However, I am
> getting
> drastically different results. At that time the model was arma(2,2) +
> garch(1,0).
>
> Is this considered a normal behavior of the rugarch package? I assume that
> when you change the ordering of the external regressors the output should
> be
> exactly the same....digit by digit.
>
> I confirmed this issue by creating a generic script that can be tested by
> anyone. Has anybody faced this issue before or is there post that describes
> the issue that I am facing?
>
>   Maybe I am going insane...for now I will look further into the
> documentation that our Alexios has provided
>
> Thanks!
>   library(rugarch)
>
>
> set.seed(1)
>
> x1 <- rnorm(1000,5,1)
> x2 <- rnorm(1000,3,3)
>
> y    <- .5*(x1*x2) + rnorm(1000,1,3)
> dat  <- data.frame(x1,x2,y)
>
> var1 <- c("x1","x2")
> var2 <- c("x2","x1")
>
> # setbounds(spec)<-list(vxreg1=c(-1,1))
> model_maker <- function(x_name){
>   temp <- dat[,c("y",x_name)]
>
>   spec <- ugarchspec(variance.model      = list(model =
"sGARCH",
>                                                 garchOrder = c(1,0)),
>
>                      mean.model          = list(armaOrder = c(2,2),
>                                                 external.regressors >
as.matrix(temp[,x_name]),
>                                                 include.mean= T),
>
>                      distribution.model  = "std")
>
>   fit         <- ugarchfit(spec = spec, data = as.matrix(temp$y),solver
> "hybrid")
>   return(fit at fit$robust.matcoef)}
>
> model_maker(var1)
> model_maker(var2)
>
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