T-Copula Fitting

The COPULA Procedure

Model Fit Summary
Number of Observations 253
Missing Values 1
Data Set WORK.RETURNS
Model T
Log Likelihood 715.18233
Maximum Absolute Gradient 4.1698E-6
Number of Iterations 9
Optimization Method Newton-Raphson
AIC -1428
SBC -1425

Algorithm converged.

Parameter Estimates
Parameter Estimate Standard Error t Value Approx
Pr > |t|
DF 8.620732 1.254636 6.87 <.0001

Scatter Plot Matrix

Scatter Plot Matrix



Original Distribution

The MEANS Procedure

Variable Mean Std Dev Skewness Kurtosis Minimum Maximum
aapl
xom
ibm
msft
cvx
ge
t
jnj
pg
wfc
0.0012540
0.0013478
0.0012483
-0.000201229
0.0015241
0.000850669
0.0011979
0.000513559
0.000453874
-0.000150688
0.0139578
0.0118114
0.0104793
0.0132285
0.0123282
0.0153705
0.0093739
0.0081717
0.0079550
0.0192479
-0.1315770
0.1165074
-0.1572975
0.1468262
0.0504381
0.0934311
0.0336303
0.4186915
-0.4129574
0.2538361
0.8209095
1.0044094
1.4061188
1.7367372
0.9285478
2.0800735
0.1414501
2.8696298
1.9540882
0.8759096
-0.0462645
-0.0362002
-0.0385557
-0.0419766
-0.0363302
-0.0515838
-0.0257367
-0.0299347
-0.0346775
-0.0579209
0.0402939
0.0391713
0.0329325
0.0513152
0.0467051
0.0689551
0.0264856
0.0361946
0.0246619
0.0622713



Simulated Distribution

The MEANS Procedure

Variable Mean Std Dev Skewness Kurtosis Minimum Maximum
aapl
xom
ibm
msft
cvx
ge
t
jnj
pg
wfc
0.0013222
0.0016045
0.0013700
0.000046560
0.0017860
0.0012425
0.0013591
0.000606679
0.000576737
0.000444696
0.0140784
0.0121162
0.0104836
0.0134732
0.0125711
0.0156400
0.0094487
0.0085014
0.0080087
0.0194727
-0.1543604
0.1157938
-0.1348073
0.1659763
0.1035316
0.1732826
0.0316741
0.4090692
-0.3831694
0.2500957
0.7815440
0.9608347
1.4404157
1.8262408
0.9404673
2.2192372
0.2108219
2.9363093
2.0407337
0.9157536
-0.0462645
-0.0362002
-0.0385557
-0.0419766
-0.0363302
-0.0515838
-0.0257367
-0.0299347
-0.0346775
-0.0579209
0.0402939
0.0391713
0.0329325
0.0513152
0.0467051
0.0689551
0.0264856
0.0361946
0.0246619
0.0622713