I have done fitted a DCC-GARCH model using the dccfit
function from the "rmgarch" package in R. The output is below:
*---------------------------------*
* DCC GARCH Fit *
*---------------------------------*
Distribution : mvnorm
Model : DCC(1,1)
No. Parameters : 62
[VAR GARCH DCC UncQ] : [0+32+2+28]
No. Series : 8
No. Obs. : 240
Log-Likelihood : 4896.6
Av.Log-Likelihood : 20.4
Optimal Parameters
-----------------------------------
Estimate Std. Error t value Pr(>|t|)
[FTSE100].mu 0.005599 0.003457 1.6195e+00 0.105339
[FTSE100].omega 0.000100 0.000160 6.2312e-01 0.533205
[FTSE100].alpha1 0.176637 0.124341 1.4206e+00 0.155436
[FTSE100].beta1 0.807578 0.072324 1.1166e+01 0.000000
[MSUSAML].mu 0.007760 0.003077 2.5219e+00 0.011673
[MSUSAML].omega 0.000056 0.000053 1.0484e+00 0.294455
[MSUSAML].alpha1 0.092896 0.040348 2.3023e+00 0.021316
[MSUSAML].beta1 0.886704 0.028933 3.0647e+01 0.000000
[MSEXUK.].mu 0.009228 0.003421 2.6976e+00 0.006984
[MSEXUK.].omega 0.000114 0.000189 6.0293e-01 0.546552
[MSEXUK.].alpha1 0.070957 0.046983 1.5103e+00 0.130978
[MSEXUK.].beta1 0.889084 0.091959 9.6682e+00 0.000000
[DAXINDX].mu 0.010099 0.004489 2.2496e+00 0.024474
[DAXINDX].omega 0.001005 0.000794 1.2650e+00 0.205864
[DAXINDX].alpha1 0.191733 0.113491 1.6894e+00 0.091142
[DAXINDX].beta1 0.600585 0.225184 2.6671e+00 0.007651
[BMUK10Y].mu 0.001496 0.001295 1.1548e+00 0.248181
[BMUK10Y].omega 0.000000 0.000027 0.0000e+00 1.000000
[BMUK10Y].alpha1 0.025774 0.174068 1.4807e-01 0.882287
[BMUK10Y].beta1 0.969964 0.178467 5.4350e+00 0.000000
[BMUS10Y].mu 0.001069 0.001481 7.2147e-01 0.470623
[BMUS10Y].omega 0.000021 0.000014 1.4980e+00 0.134123
[BMUS10Y].alpha1 0.025983 0.024924 1.0425e+00 0.297181
[BMUS10Y].beta1 0.928892 0.037850 2.4542e+01 0.000000
[BMBD10Y].mu 0.000893 0.001088 8.2098e-01 0.411657
[BMBD10Y].omega 0.000000 0.000000 1.2974e-01 0.896774
[BMBD10Y].alpha1 0.000000 0.000089 7.8000e-05 0.999938
[BMBD10Y].beta1 0.999000 0.000075 1.3363e+04 0.000000
[LHUSTRY].mu 0.000170 0.000950 1.7931e-01 0.857694
[LHUSTRY].omega 0.000007 0.000000 2.2820e+01 0.000000
[LHUSTRY].alpha1 0.024463 0.001250 1.9571e+01 0.000000
[LHUSTRY].beta1 0.941022 0.005656 1.6638e+02 0.000000
[Joint]dcca1 0.017443 0.005703 3.0584e+00 0.002225
[Joint]dccb1 0.942324 0.012105 7.7843e+01 0.000000
Information Criteria
---------------------
Akaike -40.288
Bayes -39.389
Shibata -40.388
Hannan-Quinn -39.926
Can someone tell me what is the meaning of Pr(>|t|)
? Is it the p value for the parameter? If it is, then I have lots of insignificant parameters which indicates a very bad model I have there. I have tried run examples from the rmgarch.tests
folder as well but the Pr(>|t|)
values for the example are also big (greater than 0.05). What can I do here?
Best Answer
Yes, the column
Pr(>|t|)
are the $p$-values.You should mostly care about the joint significance of (1)
alpha1
andbeta1
for each of the series and (2) the joint significance ofdcca1
anddccb1
.alpha1
andbeta1
are jointly insignificant, you may be better off using constant conditional variance rather than GARCH(1,1).dcca1
anddccb1
are jointly insignificant, you may be better off using a constant conditional correlation model rather than DCC(1,1).You may not care that much about the significance of
mu
; it is the intercept of the conditional mean model, and there are reasons (not specific to GARCH modelling) for keeping the intercept in even though it is not significant.Meanwhile, you want to keep
omega
in the model regardless of its significance unlessalpha1+beta1=1
, otherwise the absence ofomega
generates funny patterns in conditional variance -- see this answer for details.