[R-SIG-Finance] Απ: Απ: Question about rugarch

[Andreas Bregiannis]

Yes, that worked great!

Thank you so much for all your assistance!


Best regards,

Andreas

________________________________
Από: Alexios Ghalanos <alexios at 4dscape.com>
Στάλθηκε: Πέμπτη, 1 Μαρτίου 2018 8:08 μμ
Προς: Andreas Bregiannis
Κοιν.: ibrahim ergen; r-sig-finance at r-project.org
Θέμα: Re: [R-SIG-Finance] Απ: Question about rugarch

Andreas, you can just extract the numeric values from the output :
Either coredata() or as.numeric().

rugarch was designed to work with xts as it was the author’s opinion at the time that it was the gold standard for working with timeseries data in the context of GARCH models and greatly simplified design decisions.

Regards,

Alexios

> On Mar 1, 2018, at 10:37 AM, Andreas Bregiannis <bregiannis.andreas at hotmail.com> wrote:
>
> Dear Ibrahim,
>
>
> First of all, I would like to sincerely thank you for your reply!
>
> Indeed, standardized residuals are approx. normally distributed in my model.
>
> If I understood correctly, I should do the following to transform them in uniform: pnorm(residuals(egarcht.fit,standardize=TRUE)). I just did it and I checked through a histogram that indeed they look like uniform.
>
> Thank you very much!
>
> So, do you think that my code, in general, is right? Is it reasonable that in the output of the standardized residuals, I received also the dates? I am asking because I also used fgarch package and in the output I received just the standardized residuals without the dates, so after that I just use the ecdf and I convert them.
>
>
> Best regards,
>
> Andreas
>
>
>
> ________________________________
> Áðü: ibrahim ergen <ibrahimergen at gmail.com>
> ÓôÜëèçêå: ÐÝìðôç, 1 Ìáñôßïõ 2018 6:49 ìì
> Ðñïò: Andreas Bregiannis
> Êïéí.: r-sig-finance at r-project.org
> ÈÝìá: Re: [R-SIG-Finance] Question about rugarch
>
>
> Standardized residuals would be approx. normally distributed. Those are the shocks. Standardization here means filetering of vol clustering. So you get the garch shocks which are approx normal. You can use pnorm function to transform them to uniform distribution.
>
> Best
> Ibrahim
>
> Sent from my iPhone
>
>> On Mar 1, 2018, at 11:32 AM, Andreas Bregiannis <bregiannis.andreas at hotmail.com> wrote:
>>
>> Dear Sir or Madam,
>>
>>
>> I am trying to fit an AR-t-(E)GARCH model in stock log return data using the rugarch package.
>>
>> My code is :
>>
>> install.packages("rugarch")
>> library(rugarch)
>> data <- read.csv2("C:/Users/bregi/Desktop/Thesis/R/euro.csv",header = TRUE,sep=";")
>> lr.AEX<- diff(log(data$AEX))  ## log returns of stock market index AEX
>> egarcht.spec=ugarchspec(variance.model=list(model="eGARCH",garchOrder=c(2,1)),
>>                      mean.model=list(armaOrder=c(1,0)),distribution.model = "std")
>> egarcht.fit = ugarchfit(egarcht.spec, lr.AEX)
>>
>> The problem is that I want to transform my standardized residuals to follow uniform distribution. But when I run the following:
>> res<-residuals(egarcht.fit, standardize=TRUE)
>> ecdf(res)
>> plot.ecdf(res)
>> I realized that they have not transformed. I think that I found the problem but I don't know how to fix it.
>> When I run residuals(egarcht.fit, standardize=TRUE) I get data in the following form:
>> 1970-01-02 01:00:00 -0.3766309  instead of just -0.3766309.
>>
>> After that, I tried to solve this problem by doing:
>>
>> require(xts)
>> time <- date[2:3914]
>> lrAEX.xts <- na.omit(xts(x = lr.AEX, order.by = time))
>>
>> egarcht.spec=ugarchspec(variance.model=list(model="eGARCH",garchOrder=c(2,1)),
>>                      mean.model=list(armaOrder=c(1,0)),distribution.model = "std")
>> egarcht.fit = ugarchfit(egarcht.spec, data=lrAEX.xts)
>>
>> residuals(egarcht.fit,standardize=TRUE)[1]
>>
>> But the only improvement is that I receive the real dates of my data
>>
>>                [,1]
>> 2001-01-02 -0.3766309
>>
>> However, my goal is to receive only the residuals ( so only -0.3766309).
>>
>>
>> Could you please tell me if you know how I can achieve this?
>>
>> Thank you in advance.
>>
>> Kind regards,
>> Andreas
>>
>>
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>>
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