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

[alexios galanos]

That's piqued my interest...here is my suggestion (which I have 
successfully tested) for a quick solution:

1. Use variance targeting:

gjrSpec <- ugarchspec(mean.model = list(armaOrder = 
c(1,0)),variance.model = list(model = "gjrGARCH", 
variance.targeting=TRUE),distribution.model = "sstd")

2. remove the NA leftover from the return calculation:
na.omit(spyRets)


Alexios


On 11/28/18 7:39 PM, Ilya Kipnis wrote:
> image.png
> 
> Unfortunately, the gosolnp method does not work.
> 
> Tried implementing fit.control as best I understood it.
> 
> image.png
> 
> Also does not work.
> 
> On Wed, Nov 28, 2018 at 10:33 PM alexios galanos <alexios using 4dscape.com 
> <mailto: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)
>      >
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