From: Diethelm Wuertz <wuertz_at_itp.phys.ethz.ch>

Date: Wed 14 Dec 2005 - 08:43:52 EST

x = 100* as.vector(eps)

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Dec 14 10:10:53 2005

Date: Wed 14 Dec 2005 - 08:43:52 EST

Braesch Lucas wrote:

>I'm trying to use garchFit from fSeries, with Student or Skewed Student conditionnal distribution. Let's say that eps (vector) is my series of daily log-returns:

*>
**>data(EuStockMarkets)
**>eps = diff(log(EuStockMarkets[,"CAC"]))
**>
**>library(fSeries)
**>g = garchFit(series = eps, formula.var = ~garch(2,2), cond.dist = "dstd")
**>s = g@fit$series
**>
**>All the coefficients are ok (checked with SAS 9.1) except nu (degrees of freedom of the student) and the log-likelyhood. I've really checked everything and can't find the estimated series sigma (volatility) and eta, such that eps = sigma * eta and eta is centered and reduced... I've tryed combinations of all s$x,s$h,s$z and nothing looks looks correct.
**>
**>Also, is it possible to fit EGARCH and TGARCH with R ?
**>
**>If anyone ever managed to make it work, i'd be grateful ;-)
**>
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Do you think, that SAS is right? - Please can you post the results from SAS? This is a good example which shows what can go wrong in GARCH Modelling!!!

First simulate with Rmetrics:

data(EuStockMarkets)

eps = as.vector(diff(log(EuStockMarkets[,"CAC"])))
var(x)

# Important - Maybe you have a scale problem in optimization because # your variance paramater is so small compared with the other parameters! # Thus, multiply with 100:

x = 100* as.vector(eps)

# Rmetrics:

garchFit(formula.mean = ~arma(0,0), formula.var = ~garch(2,2), cond.dist
= "dstd")

# mu omega alpha1 alpha2 beta1 beta2
shape

# 0.0523284 0.0421556 0.0455789 0.0000010 0.8678519 0.0523520
7.9870453

# Now I simulated with Ox and S-Plus, in both cases I found convergence
problems.

# The reason may be that your model is not a GARCH(2,2) it's a
GARCH(1,2) model!

# Now Try:

garchFit(formula.mean = ~arma(0,0), formula.var = ~garch(1,2), cond.dist
= "dstd")

# mu omega alpha1 beta1 beta2 shape # 0.0523284 0.0421547 0.0455790 0.8678688 0.0523368 7.9870458 # Great, we get the same result! # Now, try Ox/G@RCH, the result is: Coefficient Std.Error t-value t-prob Cst(M) 0.052328 0.023772 2.201 0.0278 Cst(V) 0.042139 0.027597 1.527 0.1269 ARCH(Alpha1) 0.045604 0.025377 1.797 0.0725 GARCH(Beta1) 0.867664 0.64808 1.339 0.1808 GARCH(Beta2) 0.052555 0.60865 0.08635 0.9312 Student(DF) 7.983069 1.1553 6.910 0.0000

# Now try S-Plus/Finmetrics, the result is:

Conditional Distribution: t

with estimated parameter 7.937377 and standard error 1.148712

Value Std.Error t value Pr(>|t|) C 0.05311 0.02377 2.2344 0.01279 A 0.04355 0.02818 1.5455 0.06120 ARCH(1) 0.04653 0.02553 1.8230 0.03423 GARCH(1) 0.85512 0.64209 1.3318 0.09155 GARCH(2) 0.06303 0.60239 0.1046 0.45834 # So Rmetrics, Ox, and S-Plus are in agreement!!!# What is with SAS? Please give us the results for GARCH(1,2) # and GARCH(2,2)!

# Please note, garchFit() from Rmetrics is still in # testing phase. An updated version is just under preparation.

Diethelm Wuertz

*>
**>
**>
*

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Dec 14 10:10:53 2005

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