Hi Tom,

I am estimating a VAR-Bekk model.

But diagnostic tests show that there are serial correlation between residuals and squared residuals. I used many lags but the problem still is here.

I don't know what to do and what's the problem with my model. Here is the results and data.

MV-GARCH, BEKK - Estimation by BFGS

Convergence in 86 Iterations. Final criterion was 0.0000080 <= 0.0000100

Usable Observations 1211

Log Likelihood -3719.0235

Variable Coeff Std Error T-Stat Signif

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Mean Model(RTEPIX)

1. RTEPIX{1} 0.356840835 0.025307333 14.10029 0.00000000

2. RTEPIX{2} -0.013958931 0.026616815 -0.52444 0.59997232

3. RCOIN{1} -0.016331690 0.011754705 -1.38937 0.16471883

4. RCOIN{2} 0.018265675 0.012985721 1.40660 0.15954695

5. REX{1} 0.014792658 0.012804938 1.15523 0.24799595

6. REX{2} 0.023228690 0.012417720 1.87061 0.06139939

7. Constant -0.001061212 0.010126336 -0.10480 0.91653675

Mean Model(RCOIN)

8. RTEPIX{1} 0.061440022 0.040571549 1.51436 0.12993399

9. RTEPIX{2} 0.017191004 0.041208453 0.41717 0.67655275

10. RCOIN{1} 0.048180292 0.023319299 2.06611 0.03881787

11. RCOIN{2} -0.084908253 0.025030851 -3.39214 0.00069348

12. REX{1} -0.096842723 0.037185150 -2.60434 0.00920518

13. REX{2} 0.069483173 0.033987836 2.04435 0.04091857

14. Constant 0.033570688 0.024864922 1.35012 0.17697672

Mean Model(REX)

15. RTEPIX{1} 0.000014895 0.013898893 0.00107 0.99914491

16. RTEPIX{2} 0.016894471 0.012303875 1.37310 0.16972074

17. RCOIN{1} 0.008707838 0.009760591 0.89214 0.37231656

18. RCOIN{2} -0.040621795 0.010642314 -3.81701 0.00013508

19. REX{1} -0.024894645 0.024594886 -1.01219 0.31144825

20. REX{2} -0.096227906 0.026927006 -3.57366 0.00035203

21. Constant 0.007144155 0.007756727 0.92103 0.35703629

22. C(1,1) 0.103914068 0.011591412 8.96475 0.00000000

23. C(2,1) -0.070948467 0.020223345 -3.50825 0.00045107

24. C(2,2) 0.035304293 0.042335022 0.83393 0.40432245

25. C(3,1) -0.025324483 0.014714289 -1.72108 0.08523614

26. C(3,2) 0.000132656 0.029473720 0.00450 0.99640889

27. C(3,3) -0.000053435 0.024701834 -0.00216 0.99827401

28. A(1,1) 0.291953794 0.023175837 12.59734 0.00000000

29. A(1,2) 0.028216551 0.027416775 1.02917 0.30339930

30. A(1,3) -0.050407145 0.014019247 -3.59557 0.00032369

31. A(2,1) -0.093510466 0.009153410 -10.21592 0.00000000

32. A(2,2) 0.226824872 0.017036717 13.31388 0.00000000

33. A(2,3) 0.068778361 0.008795413 7.81980 0.00000000

34. A(3,1) 0.020153175 0.016652668 1.21021 0.22619948

35. A(3,2) 0.149401167 0.032214834 4.63765 0.00000352

36. A(3,3) 0.692727451 0.017306683 40.02659 0.00000000

37. B(1,1) 0.930043085 0.008699801 106.90394 0.00000000

38. B(1,2) -0.005327872 0.012446308 -0.42807 0.66860127

39. B(1,3) 0.046867599 0.008303195 5.64453 0.00000002

40. B(2,1) 0.022896938 0.003121553 7.33511 0.00000000

41. B(2,2) 0.976021276 0.004132489 236.18244 0.00000000

42. B(2,3) -0.003287474 0.003990009 -0.82393 0.40998129

43. B(3,1) -0.013286749 0.004941800 -2.68865 0.00717425

44. B(3,2) -0.061202492 0.008838985 -6.92415 0.00000000

45. B(3,3) 0.841698669 0.005393331 156.06287 0.00000000

Multivariate Q Test

Test Run Over 3 to 1213

Lags Tested 10

Degrees of Freedom 78

D of F Correction 12

Q Statistic 232.8999

Signif Level 0.0000

Multivariate ARCH Test

Statistic Degrees Signif

488.38 360 0.00001