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Correct specification for modelling a AR(p)-GJR GARCH(1, 1) - skewed t using fGARCH

2 messages · bonjourbc9

bonjourbc9
# 
Hi folks, 
I am trying to model a fit for FTSE100 daily log returns. As a first step I
obtain the daily log returns using LN ( Pt / Pt-1) . Next suppose I define x
as the vector of log return series ; I used the fGARCH to model the AR(5) -
GJR GARCH(1,1) - skewed t . Can someone advise whether the following entry
is correct?
delta = 2, skew = 1,
shape = 4, cond.dist = c("sstd"),
include.mean = TRUE, include.delta = FALSE, include.skew = NULL,
include.shape = NULL, leverage = NULL, trace = TRUE,
algorithm = c( "nlminb"),
control = list(), title = NULL, description = NULL)

I managed to obtain some results using this command. However the skewness
parameter returned from this command doesn't seems odd ( it is positive
value when my data exhibits negative skewness)

Can anyone help me with this code?Thanks.
bonjourbc9
# 
Dear All,
while waiting for a reply I tried to tidy up my codes abit and this is what
I used to model a AR(1)-GARCH(1,1) with skewed student t distribution for
the residuals.
,trace=FALSE) 

This is what the fGARCH code returned;

Error Analysis:
        Estimate  Std. Error  t value Pr(>|t|)    
mu       0.08031     0.01902    4.223 2.41e-05 ***
ar1      0.09528     0.01835    5.194 2.06e-07 ***
omega    0.03102     0.00890    3.486  0.00049 ***
alpha1   0.10835     0.01399    7.745 9.55e-15 ***
beta1    0.87862     0.01519   57.848  < 2e-16 ***
skew     0.88764     0.02320   38.261  < 2e-16 ***
shape    7.37774     0.90894    8.117 4.44e-16 ***

My question is what is this skew parameter for ?Is it the skewness of the
residuals? or is it the skewness of the standardized residuals?? 

I tried to extract both the residuals and standardized residuals using the
following code;
When I copy the residuals into excel and calculate its skewness , both
return me negative skewness of -0.5573 ( skew of standardized res) and
-0.85492 (skew of res). So what exactly is the skewness of 0.88764?? I
assume that the shape refers to the shape of the standardized errors?