[R] Bootstrap using ARIMA model

[Ashwini Patil]

Hi David,

here is my code including what i did for the tsboot:
rm(list = ls())
library(boot)
library(tseries)
library(TTR)
library(quantmod)
library(scales)
library(forecast)
library(zoo)
library(TSA)
security<-"NFLX"
startDate<-"2012-06-01"
endDate<-"2016-10-31"
qte_list<-c("AdjClose")

data=get.hist.quote(instrument = security, startDate, endDate, quote =
qte_list,   provider = "yahoo" )

func.ar<- ar(logret)
func.model<-list(order = c(func.ar$order,0,0),ar=func.ar$ar)
func.res<- func.ar$resid[!is.na(func.ar$resid)]
func.res<-func.res - mean()
func<- function(logret,formula){
  d = logret
  return(RSI(exp(logret)))
}
func.sim<-function(res,n.sim,ran.args){
  rg1<- function(n, res) sample(res, n, replace=TRUE)
  ts.orig<-ran.args$ts
  ts.mod<-ran.args$model
  mean(ts.orig)+ts(arima.sim(model=ts.mod,n=n.sim, ran.gen=rg1,
res=as.vestor(res)))
}
myboot<-tsboot(exp(logret),func,R=500,sim="model", ran.gen=func.sim,
ran.args = List(ts=log(data[,1],model=func.sim))


Best,
Ash


On Thu, Dec 1, 2016 at 1:50 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote:

> Just briefly to follow up David's comment, though this is mainly about
> statistics and therefore off topic here...
>
> Bootstrapping time series is a subtle issue that requires familiarity
> with the technical details-- and maybe even current research. The
> tsboot() function gives you several options from which you must choose
> *appropriately* -- or maybe choose something else entirely. The Help
> doc gives you a sense of the difficulties:
>
> ***************
> Model based resampling is very similar to the parametric bootstrap and
> all simulation must be in one of the user specified functions. This
> avoids the complicated problem of choosing the block length but relies
> on an accurate model choice being made.
>
> Phase scrambling is described in Section 8.2.4 of Davison and Hinkley
> (1997). The types of statistic for which this method produces
> reasonable results is very limited and the other methods seem to do
> better in most situations. Other types of resampling in the frequency
> domain can be accomplished using the function boot with the argument
> sim = "parametric".
> ****
>
> Moral: If you don't know what you're doing, seek local expertise to
> help -- remote sites offering suggestions from those who aren't
> familiar with the details of your data and analysis goals (maybe you
> don't need to do this at all!) may lead you to irreproducible
> nonsense.
>
> Cheers,
> Bert
> Bert Gunter
>
> "The trouble with having an open mind is that people keep coming along
> and sticking things into it."
> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
>
>
> On Thu, Dec 1, 2016 at 7:45 AM, Ashwini Patil <ash369ster at gmail.com>
> wrote:
> > Hi,
> >
> > I want to implement a bootstrap method for time series.
> > I am taking the adj close values from yahoo for NFLX and now I need to
> > bootstrap these values using ARIMA model.
> >
> > here is my code so far:
> > rm(list = ls())
> > library(boot)
> > library(tseries)
> > library(TTR)
> > library(quantmod)
> > library(scales)
> > library(forecast)
> > library(zoo)
> > library(TSA)
> > security<-"NFLX"
> > startDate<-"2012-06-01"
> > endDate<-"2016-10-31"
> > qte_list<-c("AdjClose")
> >
> > data=get.hist.quote(instrument = security, startDate, endDate, quote =
> > qte_list,   provider = "yahoo" )
> > logret<-diff(log(data[,1]))
> > fit11<-auto.arima(logret, max.order=10)
> >
> > When i use auto.arima, I get an order of (0,0,0) with non-zero mean.
> After
> > this, I tried to use tsboot function but it is not yielding any answers.
> >
> > Any and all help is appreciated.
> >
> > Thank you!
> >
> >         [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide http://www.R-project.org/
> posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>

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