Ole,
The email you've sent is really badly formatted and all over the place.
1. Try to send a well structured text-only email (with no special
characters).
2. Make an effort to send a minimally reproducible example.
Telling us that your problem took 8 hours to run is not the way to go.
Take a small subsample of your data which generates the behavior you
want to highlight (try to find one that does), and use that so that we
can investigate with minimal fuss.
-Alexios
On 21/08/2014 17:50, Ole Bueker wrote:
> Hello,
> I am trying to use the R packages rugarch and VineCopula for simulating
> returns of 112 companies for a time period of 25 days with daily
> re-estimations. After the simulation, I wish to calculate the 99% and 95%
> value-at-risk and compare them to the actual returns.I use a moving window of
> 250 days and 1000 simulations per iteration.
> (This estimation is quite time-intensive as 112 companies might be too many
> for VineCopula..)
> The overall loop takes around 8 hours on my home computer, so I wouldn�t
> recommend to actually run the code. My problem is that some of my calculated
> value at risk forecasts seem to be positive � this is not a �just invert the
> VaR� kind of problem (at least I don�t think so).
> To summarize the code:
> 1. Fit GARCH models to each series.2. Extract standardized returns (and
> shape parameters)3. Transform standardized returns to uniform marginals using
> parametric method (IFM by Joe, 1987).4. Fit vine copulas5. Generate a 1000 x
> 112 matrix (1000 1-day ahead forecasts for all 112 companies)6. Reverse
> transform the simulated values.7. Use these transformed forecasts in ugarchsim
> 8. Extract forecasted values & sigmas.9. Calculate Value-at-Risk.
> Anyway, here�s my code so far:# Load Data and define variables
> returns <- read.zoo("E:/Dropbox/my own/Programming/R/returns.csv",
> header=TRUE, sep=",",
> format="%d-%m-%y")model<-ugarchspec(variance.model=list(model="sGARCH",garchOrder=c(1,1)),mean.model=list(armaOrder=c(1,0),include.mean=FALSE),distribution.model="ged")times
> <- as.data.frame(time(returns))windows <- matrix(0, 112, 250)familyset <-
> c(1:5, 7, 10, 13, 14, 17, 20) # The vine copulas to be
> testedsim <- array(0, dim = c(1000, 112))residuals2 <- array(0, dim = c(1000,
> 112))rvine_fitted <- array(0, dim = c(25,1000,112))rvine_sigma <- array(0,
> dim = c(25,1000,112))VaR01 = VaR05 = array(0, dim = c(25,1000,112))
>
> #Main calculation
> for(i in 1:25){ print(i) windows <- window(returns_crisis,
> start=times[376-250-24+i,1], end=times[376-25+i,1]) #Define the
> moving window fit <- lapply(windows, ugarchfit, spec=model, solver="hybrid")
> #Fit the garch
> models print("rugarch fitting done") residuals <- sapply(fit, residuals,
> standardize=TRUE)
> #Extract residuals & shape parameters shape <-
> sapply(fit, coef) shape <- shape[5,] UniformResiduals <- pged(residuals, nu
> = shape)
> #Transform residuals into uniform marginals
> if(any(UniformResiduals > 0.99999)) { ix = which(UniformResiduals >
> 0.99999) UniformResiduals [ix] = 0.99999 } if(any(UniformResiduals <
> .Machine$double.eps)) { ix = which(UniformResiduals <
> (1.5*.Machine$double.eps)) UniformResiduals [ix] =
.Machine$double.eps } rvine <- RVineStructureSelect(UniformResiduals,
indeptest=TRUE, familyset=familyset) #Fit the Vine copulas
print(paste(i,"RVine fitting done")) for(j in 1:1000)
#Simulate 1000 1-day
ahead using VineCopula { sim[j,] <- RVineSim(1, rvine)
# 1000 x 112 matrix of forecasts
}print(paste(i,"RVine simulation done")) for(k in 1:112)
#Next:
ugarchsimulation for all 112 companies {
residuals2[,] <- qged(sim[,], nu = shape[k])
# 1000 x 112 matrix of standardized
residuals residuals_temp <- residuals2[,k]
# 1000 x 1 vector of standardized residuals for individual company
rvine_sim <- ugarchsim(fit[[k]], n.sim=1, m.sim=1000, custom.dist =
list(name=NA, distfit=residuals_temp)) #1000 simulations using the
standardized residuals from Vine copula models for ugarchfit
rvine_fitted[i,,k] <- fitted(rvine_sim)
#Extract forecasted
values - 25 x 1000 x 112 rvine_sigma[i,,k] <- sigma(rvine_sim)
#Extract forecasted sigmas - 25 x 1000 x 112
for(j in 1:1000)
#Next: Value at risk {
VaR01[,j,k] <- rvine_fitted[,j,k] + rvine_sigma[,j,k] * qdist('ged', 0.01,
mu=0, sigma=1, shape = shape[k]) #Value at risk for 99% quantile
VaR05[,j,k] <- rvine_fitted[,j,k] + rvine_sigma[,j,k] * qdist('ged', 0.05,
mu=0, sigma=1, shape = shape[k]) #Value at risk for 95% quantile
} }}remove(i, j, k)
#Cleanupremove(windows, fit, residuals,
shape, residuals2, residuals_temp, rvine, sim, rvine_sim) #Cleanup
Hope I didn�t make any mistakes in my approach, but it seems like this is the
�standard� copula + rugarch approach � if anyone is familiar with this, I am
open to suggestions on how to speed up the si
mulations.
> So far, so good � the problem I am facing now:
> Some (only a few) of my value at risk values are positive..I have manually
> checked and it seems like the fitted value is much larger than the sigma, so
> Value at Risk is positive � which doesn�t really make any economic sense to
> me.
> Here�s a dropbox link to the returns.csv, in case anyone is interested in
> running my code: https://www.dropbox.com/s/69i5959f3h4kweb/returns.csv
>
> Best Regards,
> Ole
> [[alternative HTML version deleted]]
>
>
>
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