Firstly I am sorry to post a silly question here. I am really confused now as I am very new in R and econometric modelling. I have done the dccfit using the 'rmgarch' package and below is the output.
*---------------------------------*
* 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). Any suggestion?
Thanks in advance.
Yes they are p-values, however the insignificant p-values do not mean it is a bad model. In information criteria at the bottom tell more about the performance of the overall model compared to other formulations.
That said, to tell if a model is 'good' you have to specify what you are trying to do with the model. Are you trying to forecast volatility with the model? Then the model is evaluated by a measure of the out-of-sample deviation of your forecast from actual values. Are you trying to find variables which affect volatility? Then the sign, size, and p-values of particular coefficients become important (not simply the p-values of all coefficients).
As mentioned in comment, you'll likely get a better answer by asking how to evaluate your model's performance (specifying the goal of your model) on Cross Validated.