The standard errors that are generated by using BFGS are only an approximation, and can change (sometimes noticeably) with minor changes to the guess values (which can change from version to version of RATS), convergence criterion, preliminary estimation method, etc.
The first of these is from the first GARCH instruction in the GARCHMV.RPF example, the second is for the same model with CVCRIT reduced to .00000001 and with more accurate numerical derivatives (NLPAR(DERIVES=FOURTH)).
Label Coefficient Standard Error T-Stat Signif
10. A(1,1) 0.105805805 0.006893563 15.34849 0.00000000
11. A(2,1) 0.093930096 0.005858416 16.03336 0.00000000
12. A(2,2) 0.128175482 0.007995778 16.03040 0.00000000
13. A(3,1) 0.088809390 0.005381811 16.50177 0.00000000
14. A(3,2) 0.113629169 0.006960113 16.32577 0.00000000
15. A(3,3) 0.111519241 0.006749644 16.52224 0.00000000
10. A(1,1) 0.105428635 0.007217789 14.60678 0.00000000
11. A(2,1) 0.093479002 0.005563105 16.80339 0.00000000
12. A(2,2) 0.127382082 0.007391909 17.23264 0.00000000
13. A(3,1) 0.088408633 0.005235857 16.88523 0.00000000
14. A(3,2) 0.112950912 0.006166255 18.31759 0.00000000
15. A(3,3) 0.110899113 0.006045944 18.34273 0.00000000
As you can see, the point estimates typically agree to about 3 significant digits (which is probably all you would ever show in publishing the results), but the standard errors are rarely the same beyond the first digit. And if you use BHHH rather than BFGS, you'll get (perhaps quite different) standard errors and the same if you add a ROBUSTERRORS option.