Just finished Kris Boudt's course, running into errors from non-convergence in rugarch

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 at 4dscape.com 
<mailto:alexios at 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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