[R-SIG-Finance] Rugarch for EWMA/VaR

Fri May 4 00:05:08 CEST 2012

 Hello Alexios,

thank you for your quick reply. 

I'm going to download the newest version from R-Forge.

Concerning 4) I still have problems. 
I tried something like:
forc2=ugarchforecast(spec=spec2, data=sp500returns, n.ahead=1,
out.sample=500, calculate.VaR=TRUE, VaR.alpha=c(0.01, 0.05))  
and
forc2=ugarchforecast(spec=spec2, data=sp500returns, n.ahead=1,
out.sample=500)
what didn't work for me. 

I would like to be able to generate VaR-Backtesting Results and
VaR-exceedence-graphs like I did with the fitted model. 
Could you please help me out, maybe with some lines of code? I already
checked the example codes I could find on how to combine forecasting with
fixed parameters and getting the VaR-Output and still didn't manage to do
it.

Thomas

alexios wrote
> 
> Thomas,
> 
> 1. The problem with the iGARCH model (related to a bad default setting) 
> was fixed some time ago in the r-forge version (will be available on 
> CRAN when I next update it there...since it is frowned upon to update a 
> CRAN package more than 12 times in a year, you should look to r-forge 
> for the 'newest' version at any time).
> You have 2 options:
> 1. Download the latest version from r-forge OR,
> 2. In ugarchfit set the option "fit.control=list(stationarity=FALSE)"
> 
> 2. That's definitely a problem in the summary method. Will investigate 
> when I get a moment.
> 
> 3. The error with the ugarchfilter C function, related to the newest 
> version of R, was also fixed in the r-forge release.
> 
> 4. You can't roll when all the parameters are fixed. It does not even 
> make sense! Roll implies that you are fitting, forecasting, re-fitting.
> Since you have all parameters fixed then just use the filter method on 
> the data you want to forecast 1-ahead, else use the forecast method with 
> a spec object and out of sample specified.
> 
> -Alexios
> 
> On 03/05/2012 21:23, ThomasF wrote:
>> Dear R-Users, dear Alexios,
>>
>> I am trying to use rugarch to forecast volatiliy and do VaR-backtests
>> using
>> the Exponentially weighted moving average.
>> To achieve this I used the iGarch, set omega to 0 and either estimate or
>> fix
>> alpha1 and beta1. Example code is provided at the end of my post.
>>
>> Some issues came up which I'd be glad to solve with your help:
>>
>> 1) Whenever i try to fit alpha1 and beta1, I always get 0.5 as result. I
>> tried this for different underlyings and time horizons. Any idea what the
>> reason could be?
>>
>> 2) I also get an error (I translated the error message to english) after
>> the
>> ARCH LM Test output:
>>
>> ARCH LM Tests
>> ------------------------------------
>>               Statistic DoF P-Value
>> ARCH Lag[2]      4.653   2 0.09765
>> ARCH Lag[5]      7.991   5 0.15672
>> ARCH Lag[10]    12.082  10 0.27959
>> Error in names(ans) = c("10%", "5%", "1%") :
>>    Attribut 'names' [3] must have same length as the vector [0]
>>
>> Is there something wrong with my code or the way i put the data into the
>> model? The other part of the outputs seems legit.
>>
>> 3) when I tried to do the same calculations with fixed alpha i get the
>> following error when i try to filter the model:
>>
>> 1: In doTryCatch(return(expr), name, parentenv, handler) :
>>    passing an object of type 'NULL' to .C (arg 8) is deprecated
>> 2: In doTryCatch(return(expr), name, parentenv, handler) :
>>    passing an object of type 'NULL' to .C (arg 9) is deprecated
>>
>> The ugarchfilter output seems reasonable again.
>>
>> 4)  I am not able to generate a rolling forecast and VaR Output when i
>> fixed
>> alpha1. Could you please give me a hint on how to do this with filtered
>> data?
>>
>> 5) Does any of those issues arise of the use of the data as "zoo" object?
>>
>>
>> Thanks in advance for your effort to help me
>>
>> Sincerely
>>
>> Thomas
>>
>>
>> -----------------Code----------
>>
>>
>> ################
>> ###Download Data&  Compute Returns
>> ################
>>
>> library ("tseries")
>>
>> #adjusted close price of sp500
>> sp500price=get.hist.quote(instrument="^gspc", start="2006-01-01",
>> end="2011-12-31", quote="AdjClose")
>>
>> # returns
>> sp500returns=diff(log(sp500price))
>>
>>
>> library("rugarch")
>>
>>
>>
>> ##############
>> ### Use iGarch for EWMA - fitted alpha and beta
>> ##############
>>
>>
>> ### Model spec
>> spec1= ugarchspec (variance.model = list(model = "iGARCH", garchOrder =
>> c(1,
>> 1)), mean.model=list(armaOrder=c(0,0),include.mean=FALSE),
>> distribution.model = "norm", fixed.pars = list(omega=0))
>>
>> ###Fit Model
>> fit1= ugarchfit(spec=spec1, data=sp500returns, out.sample=0,
>> solver="solnp",
>> solver.control=list(trace=0))
>> show(fit1)
>>
>> #### rolling forecast und VaR
>> roll1=ugarchroll(spec=spec1, data=sp500returns, n.ahead=1,
>> forecast.length=500, refit.every=25, refit.window="recursive",
>> solver="solnp", solver.control=list(tol=1e-05, delta=1e-06, trace=0),
>> calculate.VaR=TRUE, VaR.alpha=c(0.01, 0.05))
>>
>> report(roll1, type="VaR", n.ahead=1, VaR.alpha=0.01, conf.level=0.95)
>> report(roll1, type="fpm")
>> plot(roll1, which="all")
>>
>>
>>
>>
>> ##############
>> ### Use iGarch for EWMA - fixed alpha and beta with lambda=0.94
>> ##############
>>
>>
>> ### Model spec
>> spec2= ugarchspec (variance.model = list(model = "iGARCH", garchOrder =
>> c(1,
>> 1)), mean.model=list(armaOrder=c(0,0),include.mean=FALSE),
>> distribution.model = "norm",  fixed.pars = list(omega=0, alpha1=0.06))
>>
>> ###Fit Model???
>>
>> fit2= ugarchfit(spec=spec2, data=sp500returns, out.sample=0,
>> solver="solnp",
>> solver.control=list(trace=0))
>> show(fit2)
>>
>> ### filter model
>> filt2= ugarchfilter(spec=spec2, data=sp500returns)
>> show(filt2)
>>
>> #### rolling forecast und VaR
>>
>> roll2=ugarchroll(spec=spec2, data=sp500returns, n.ahead=1,
>> forecast.length=500,   calculate.VaR=TRUE, VaR.alpha=c(0.01, 0.05))
>>
>> report(roll2, type="VaR", n.ahead=1, VaR.alpha=0.01, conf.level=0.95)
>>
>> report(roll2, type="fpm")
>> plot(roll2, which="all")
>>
>> --
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