[R-SIG-Finance] Just finished Kris Boudt's course, running into errors from non-convergence in rugarch

[Ilya Kipnis]

[image: image.png]

Unfortunately, the gosolnp method does not work.

Tried implementing fit.control as best I understood it.

[image: image.png]

Also does not work.

On Wed, Nov 28, 2018 at 10:33 PM alexios galanos <alexios using 4dscape.com>
wrote:

> Try setting the solver in the resume command to "gosolnp".
> It may also have helped to set fit.control(scale=1) in the ugarchroll,
> but you can set this in resume as well.
>
> Alexios
>
> On 11/28/18 7:22 PM, Ilya Kipnis wrote:
> > I just completed Kris Boudt's datacamp course on GARCH models, and
> > thought I'd give it a spin in a more reasonable setting. I've run into
> > an error that the course didn't cover. I'm using a rolling window of 504
> > trading days to try to fit a GJR-GARCH with AR1 return innovations and a
> > skewed student t distribution and refitting the model every 22 days (so,
> > basically every month) on SPY returns.
> >
> > In the course, it was possible to convert this output into a data frame,
> > with an as.data.frame command.
> >
> > Unfortunately, the course didn't cover what happened when over the
> > course of ~300 model fits, there would be the occasional failure to
> > converge, which throws the following error:
> >
> > image.png
> >
> > Here's my MRE:
> >
> > require(rugarch)
> > require(quantmod)
> >
> > # get SPY data from Yahoo (also tried with Quandl, data isn't the issue)
> > getSymbols("SPY", from = '1990-01-01')
> >
> > spyRets <- Return.calculate(Ad(SPY))
> >
> > # GJR garch with AR1 innovations under a skewed student T distribution
> > for returns
> > gjrSpec <- ugarchspec(mean.model = list(armaOrder = c(1,0)),
> >                        variance.model = list(model = "gjrGARCH"),
> >                        distribution.model = "sstd")
> >
> > # Use rolling window of 504 days, refitting the model every 22 trading
> days
> > t1 <- Sys.time()
> > garchroll <- ugarchroll(gjrSpec, data = spyRets,
> >                          n.start = 504, refit.window = "moving",
> > refit.every = 22)
> > t2 <- Sys.time()
> > print(t2-t1)
> >
> > # try to convert predictions to data frame, as in course -- error thrown
> > regarding non-converged estimation windows
> > garchroll <- as.data.frame(garchroll)
> >
> > With a screenshot for better readability:
> >
> > image.png
> > I also tried the resume command from the following post
> > https://stat.ethz.ch/pipermail/r-sig-finance/2013q2/011720.html, which
> > did not solve my problem.
> >
> > I feel that this is a pretty straightforward application of the rugarch
> > package, and that there is most likely a solution that simply wasn't
> > covered in the course. I'd be greatly appreciative if someone could help
> > me over this hill (albeit at the risk of revealing that I'm not exactly
> > an expert on GARCH models).
> >
> > Thank you so much.
> >
> > Sincerely,
> >
> > Ilya Kipnis (author of Quantstrat TradeR)
> >
> > _______________________________________________
> > R-SIG-Finance using r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-finance
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> > -- Also note that this is not the r-help list where general R questions
> should go.
> >
>
> _______________________________________________
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