

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 GJRGARCH 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:
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 = '19900101')
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(t2t1)
# try to convert predictions to data frame, as in course  error thrown regarding nonconverged estimation windows garchroll < as.data.frame(garchroll)
With a screenshot for better readability: I also tried the resume command from the following post https://stat.ethz.ch/pipermail/rsigfinance/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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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 GJRGARCH 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 = '19900101')
>
> 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(t2t1)
>
> # try to convert predictions to data frame, as in course  error thrown
> regarding nonconverged 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/rsigfinance/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)
>
> _______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/rsigfinance>  Subscriberposting only. If you want to post, subscribe first.
>  Also note that this is not the rhelp list where general R questions should go.
>
_______________________________________________
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https://stat.ethz.ch/mailman/listinfo/rsigfinance Subscriberposting only. If you want to post, subscribe first.
 Also note that this is not the rhelp list where general R questions should go.


Unfortunately, the gosolnp method does not work. Tried implementing fit.control as best I understood it. Also does not work. On Wed, Nov 28, 2018 at 10:33 PM alexios galanos < [hidden email]> 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 GJRGARCH 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 = '19900101')
>
> 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(t2t1)
>
> # try to convert predictions to data frame, as in course  error thrown
> regarding nonconverged 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/rsigfinance/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)
>
> _______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/rsigfinance
>  Subscriberposting only. If you want to post, subscribe first.
>  Also note that this is not the rhelp list where general R questions should go.
>
_______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rsigfinance
 Subscriberposting only. If you want to post, subscribe first.
 Also note that this is not the rhelp list where general R questions should go.
_______________________________________________
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https://stat.ethz.ch/mailman/listinfo/rsigfinance Subscriberposting only. If you want to post, subscribe first.
 Also note that this is not the rhelp list where general R questions should go.


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 < [hidden email]
> <mailto: [hidden email]>> 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 GJRGARCH 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 = '19900101')
> >
> > 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(t2t1)
> >
> > # try to convert predictions to data frame, as in course  error
> thrown
> > regarding nonconverged 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/rsigfinance/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)
> >
> > _______________________________________________
> > [hidden email] <mailto: [hidden email]>
> mailing list
> > https://stat.ethz.ch/mailman/listinfo/rsigfinance> >  Subscriberposting only. If you want to post, subscribe first.
> >  Also note that this is not the rhelp list where general R
> questions should go.
> >
>
> _______________________________________________
> [hidden email] <mailto: [hidden email]>
> mailing list
> https://stat.ethz.ch/mailman/listinfo/rsigfinance>  Subscriberposting only. If you want to post, subscribe first.
>  Also note that this is not the rhelp list where general R
> questions should go.
>
_______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rsigfinance Subscriberposting only. If you want to post, subscribe first.
 Also note that this is not the rhelp list where general R questions should go.


The variance.targeting does work, but it does impose an assumption on the
nature of the variance of the S&P 500 returns, that the variance is
naturally meanreverting, and I'm not sure that's the case. While the VIX
certainly does tend to meanrevert more often than not, the times it
*doesn't* are the times that all of the volatility meanreverting guys just
get absolutely blown up, whether it's 2008, when vol went up and up, or Feb
4Feb 5 of 2018, when anyone that thought Feb 5 was a nice time to revert
the Feb 4 spike got blown up in 20 minutes after hours.
Will go with this solution for now in terms of trying to see if there's a
new VIX trading strategy, but I'm hoping I can relax the assumption in the
near future.
Thanks again.
On Wed, Nov 28, 2018 at 10:57 PM alexios galanos < [hidden email]>
wrote:
> 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 < [hidden email]
> > <mailto: [hidden email]>> 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 GJRGARCH 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 = '19900101')
> > >
> > > 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(t2t1)
> > >
> > > # try to convert predictions to data frame, as in course  error
> > thrown
> > > regarding nonconverged 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/rsigfinance/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)
> > >
> > > _______________________________________________
> > > [hidden email] <mailto: [hidden email]>
> > mailing list
> > > https://stat.ethz.ch/mailman/listinfo/rsigfinance> > >  Subscriberposting only. If you want to post, subscribe first.
> > >  Also note that this is not the rhelp list where general R
> > questions should go.
> > >
> >
> > _______________________________________________
> > [hidden email] <mailto: [hidden email]>
> > mailing list
> > https://stat.ethz.ch/mailman/listinfo/rsigfinance> >  Subscriberposting only. If you want to post, subscribe first.
> >  Also note that this is not the rhelp list where general R
> > questions should go.
> >
>
[[alternative HTML version deleted]]
_______________________________________________
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 Also note that this is not the rhelp list where general R questions should go.


End to end examples to extract data objects from results and resolve errors in eric zivot’s presentations on DCC correlations at https://faculty.washington.edu/ezivot/econ589/DCCgarchPowerpoint.pdf Rmgarch is just rugarch for more than one series at the same time making it richer in functionality. Also make sure input data does not have NAs using na.omit. Also instead of as.data.frame try converting into xts objects as well. Easier with some rugarch extensions. (These two last methods take care of all errors in between) There is a corresponding one on copulas later also by zivot for his uw advanced econ class Best regards Amit From: RSIGFinance <[hidden email]> On Behalf Of Ilya Kipnis Sent: Thursday, November 29, 2018 8:53 AM To: [hidden email] Subject: [RSIGFinance] Just finished Kris Boudt's course, running into errors from nonconvergence in rugarch 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 GJRGARCH 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: Here's my MRE:
# get SPY data from Yahoo (also tried with Quandl, data isn't the issue) getSymbols("SPY", from = '19900101') 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 garchroll < ugarchroll(gjrSpec, data = spyRets, n.start = 504, refit.window = "moving", refit.every = 22) # try to convert predictions to data frame, as in course  error thrown regarding nonconverged estimation windows garchroll < as.data.frame(garchroll) With a screenshot for better readability: I also tried the resume command from the following post https://stat.ethz.ch/pipermail/rsigfinance/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) _______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rsigfinance Subscriberposting only. If you want to post, subscribe first.
 Also note that this is not the rhelp list where general R questions should go.


Check the rugarch vignettes, it covers each included models with detailed
sub options
Best regards
Amit
Original Message
From: RSIGFinance < [hidden email]> On Behalf Of Ilya
Kipnis
Sent: Thursday, November 29, 2018 9:42 AM
To: Alexios G < [hidden email]>
Cc: [hidden email]
Subject: Re: [RSIGFinance] Just finished Kris Boudt's course, running into
errors from nonconvergence in rugarch
The variance.targeting does work, but it does impose an assumption on the
nature of the variance of the S&P 500 returns, that the variance is
naturally meanreverting, and I'm not sure that's the case. While the VIX
certainly does tend to meanrevert more often than not, the times it
*doesn't* are the times that all of the volatility meanreverting guys just
get absolutely blown up, whether it's 2008, when vol went up and up, or Feb
4Feb 5 of 2018, when anyone that thought Feb 5 was a nice time to revert
the Feb 4 spike got blown up in 20 minutes after hours.
Will go with this solution for now in terms of trying to see if there's a
new VIX trading strategy, but I'm hoping I can relax the assumption in the
near future.
Thanks again.
On Wed, Nov 28, 2018 at 10:57 PM alexios galanos < [hidden email]>
wrote:
> 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
> > < [hidden email] <mailto: [hidden email]>> 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 GJRGARCH 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 = '19900101')
> > >
> > > 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(t2t1)
> > >
> > > # try to convert predictions to data frame, as in course  error
> > thrown
> > > regarding nonconverged 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/rsigfinance/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)
> > >
> > > _______________________________________________
> > > [hidden email] <mailto: [hidden email]>
> > mailing list
> > > https://stat.ethz.ch/mailman/listinfo/rsigfinance> > >  Subscriberposting only. If you want to post, subscribe first.
> > >  Also note that this is not the rhelp list where general R
> > questions should go.
> > >
> >
> > _______________________________________________
> > [hidden email] <mailto: [hidden email]>
> > mailing list
> > https://stat.ethz.ch/mailman/listinfo/rsigfinance> >  Subscriberposting only. If you want to post, subscribe first.
> >  Also note that this is not the rhelp list where general R
> > questions should go.
> >
>
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If you look to warnings(), it is said that
Warning in .makefitmodel(garchmodel = "gjrGARCH", f = .gjrgarchLLH, T = T, :
rugarch>warning: failed to invert hessian
The 6 available solvers got the same message.
F.
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