Problem with garch (tseries)

These are good points.  But probably a key aspect
is that garch thinks 800 data points is a small sample,
and 400 points (at least for daily data) is likely to yield
parameter estimates that are exceptionally variable.

Patrick Burns
patrick at burns-stat.com
+44 (0)20 8525 0696
http://www.burns-stat.com
(home of S Poetry and "A Guide for the Unwilling S User")

Joe W. Byers wrote:

Micheal,

Physical natural daily prices are a problem with time series models 
especially if the physical asset is storable.  Storable assets means

that the prices are not an easy single model time series process but

a 

multi-variate model ie Supply and Demand.  Another problem is the 
returns of these prices are not normally distributed especially over

short times series. Other things to think about are how liquid are

these 

asset prices, the breath and depth of the markets, are is there a

well 

developed forward market?

These do not mean that your model is not going to work, it may just

not 

be robust next time you estimate it.  I would suggest running some 
outlier tests for spikes in the prices, looking at volatility

clustering 

around these outliers.  Also consider using a GED GARCH model that

is 

normal distribution under restrictions on the parameters and a

negative 

exponential on others.  My collegues and I and U Tulsa have found

this 

works well for some Power prices and Weather Temps.  I am working on

it 

for other prices series as well.

Good Luck
Joe

michael mathews wrote:
 

Hi folks,
I have been playing with garch models to model the volatility in
physical natural prices. 
Here is the issue I have a dataset of 801 daily returns (attached).
If I run

garchall<-garch(hsc)

***** ESTIMATION WITH ANALYTICAL GRADIENT ***** 


   

summary(garchall)
     

Call:
garch(x = hsc)

Model:
GARCH(1,1)

Residuals:
   Min      1Q  Median      3Q     Max 
-4.3424 -0.5734  0.0000  0.6037  4.0501 

Coefficient(s):
   Estimate  Std. Error  t value Pr(>|t|)    
a0 2.507e-05   9.200e-06    2.726  0.00642 ** 
a1 1.218e-01   2.085e-02    5.840 5.21e-09 ***
b1 8.759e-01   1.937e-02   45.212  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Diagnostic Tests:
       Jarque Bera Test

data:  Residuals 
X-squared = 62.7291, df = 2, p-value = 2.387e-14


       Box-Ljung test

data:  Squared.Residuals 
X-squared = 0.0384, df = 1, p-value = 0.8447

Now if we run the same model on a subset say the last 351 days we

get

garch351<-garch(tail(hsc,351))
     

***** ESTIMATION WITH ANALYTICAL GRADIENT ***** 


   

summary(garch351)
     

Call:
garch(x = tail(hsc, 351))

Model:
GARCH(1,1)

Residuals:
     Min        1Q    Median        3Q       Max 
-4.171521 -0.424628  0.008727  0.532158  3.962116 

Coefficient(s):
   Estimate  Std. Error  t value Pr(>|t|)    
a0 2.511e-05   1.589e-05    1.580 0.114167    
a1 1.043e-01   2.950e-02    3.536 0.000406 ***
b1 8.957e-01   2.567e-02   34.896  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Diagnostic Tests:
       Jarque Bera Test

data:  Residuals 
X-squared = 76.3704, df = 2, p-value < 2.2e-16


       Box-Ljung test

data:  Squared.Residuals 
X-squared = 1.2806, df = 1, p-value = 0.2578

still ok. Now finally we get t the point of this email lets look at

352

days of data:

garch352<-garch(tail(hsc,352))

***** ESTIMATION WITH ANALYTICAL GRADIENT ***** 


Warning message:
NaNs produced in: sqrt(pred$e) 
   

summary(garch352)
     

Call:
garch(x = tail(hsc, 352))

Model:
GARCH(1,1)

Residuals:
    Min       1Q   Median       3Q      Max 
-4.16377 -0.58155  0.01454  0.70886 12.41242 

Coefficient(s):
   Estimate  Std. Error  t value Pr(>|t|)    
a0 2.428e-05   1.556e-05    1.561 0.118632    
a1 1.043e-01   2.947e-02    3.540 0.000400 ***
b1 8.962e-01   2.556e-02   35.058  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Diagnostic Tests:
       Jarque Bera Test

data:  Residuals 
X-squared = 10993.57, df = 2, p-value < 2.2e-16


       Box-Ljung test

data:  Squared.Residuals 
X-squared = 0.1831, df = 1, p-value = 0.6687

whats up? Any Ideas. 
I have also tried using garchFit from the fSeries package but it

locks

up completely left it running last night and it was still spinning

this

morning when I got back to the office.

thanks in advance

michael