trivariate DCC with VAR(1) MEAN EQUATIONS

[xyzh]

Here's an example paper:
http://ideas.repec.org/a/eee/quaeco/v47 ... l#download

Hope u will understand what i said.

Thanks.

Zhang

[xyzh]

Including asymmetry terms is covered in the manuals. See "GARCH" in the Reference Manual and Chapter 12 in the User's Guide.

the manual demonsrates the GJR ,right?
My question is about estimating a ADCC model, am I right?
Tks.

Zhang

[xyzh]

I have a new problem now with the MVGARCH-DCC. After adding "variance=varma" option i got the results like this:

• Variable Coeff Std Error T-Stat Signif
  *******************************************************************************
  1. Mean(1) 0.000145192 0.000298354 0.48664 0.62651077
  2. Mean(2) 0.000452759 0.000251361 1.80123 0.07166673
  3. Mean(3) 0.000307494 0.000043419 7.08208 0.00000000
  4. C(1) -0.000000217 0.000000529 -0.41026 0.68161615
  5. C(2) 0.000000710 0.000000564 1.25993 0.20769582
  6. C(3) 0.000000155 0.000000045 3.42794 0.00060818
  7. A(1,1) 0.057933592 0.016794974 3.44946 0.00056171
  8. A(1,2) -0.065924827 0.025252117 -2.61067 0.00903663
  9. A(1,3) 0.048611966 0.132020640 0.36821 0.71271301
  10. A(2,1) -0.031016545 0.016435385 -1.88718 0.05913600
  11. A(2,2) 0.102465005 0.025064940 4.08798 0.00004351
  12. A(2,3) -0.096694866 0.135042222 -0.71603 0.47397015
  13. A(3,1) 0.005105719 0.005538758 0.92182 0.35662423
  14. A(3,2) 0.009217814 0.006968065 1.32287 0.18588005
  15. A(3,3) 0.098969648 0.028962153 3.41721 0.00063267
  16. B(1,1) 0.904499342 0.023065961 39.21360 0.00000000
  17. B(1,2) 0.196648202 0.052423936 3.75111 0.00017605
  18. B(1,3) -2.498101504 0.993974355 -2.51325 0.01196261
  19. B(2,1) 0.063046957 0.030820503 2.04562 0.04079403
  20. B(2,2) 0.872904440 0.031369087 27.82690 0.00000000
  21. B(2,3) -0.260539758 0.444913701 -0.58560 0.55814692
  22. B(3,1) -0.091754291 0.037002318 -2.47969 0.01314965
  23. B(3,2) 0.020923602 0.030008992 0.69724 0.48564984
  24. B(3,3) 0.851193338 0.033443870 25.45140 0.00000000
  25. DCC(1) 0.009226038 0.003358069 2.74742 0.00600656
  26. DCC(2) 0.988152157 0.006115655 161.57750 0.00000000
  27. Shape 8.273972673 1.325200506 6.24356 0.00000000

Im confused what the non-diagonal elements mean?

[TomDoan]

That's explained in the manual. VARIANCES=VARMA is an extension of the CC/DCC type model allowing each variance to depend upon the other variances and the other lagged squared residuals. Those terms give those off-diagonal coefficients.

[gembala119]

Dear Sir
After we do trivariate or bivariate garch model, how we make graphs of variance and covariance of bivariate or trivariate

thank you very much
kim

[xyzh]

Dear Sir
After we do trivariate or bivariate garch model, how we make graphs of variance and covariance of bivariate or trivariate
kim

you can graph the variance and covariance with RATs by using the command "graph" plus some options or you can print the results and graph it with Excel.

[TomDoan]

Dear Sir
After we do trivariate or bivariate garch model, how we make graphs of variance and covariance of bivariate or trivariate

thank you very much
kim

This is from GARCHMV.PRG which graphs the correlations from a multivariate GARCH, with grid lines at the CC estimates.

Code: Select all

*
* GARCHMV.PRG
* Manual Example 12.2
*
open data g10xrate.xls
data(format=xls,org=columns) 1 6237 usxjpn usxfra usxsui
*
set xjpn = 100.0*log(usxjpn/usxjpn{1})
set xfra = 100.0*log(usxfra/usxfra{1})
set xsui = 100.0*log(usxsui/usxsui{1})
*
garch(p=1,q=1,iters=200,hmatrices=hh) / xjpn xfra xsui
garch(p=1,q=1,mv=bek,method=bfgs,iters=200,pmethod=simplex,piters=10) / xjpn xfra xsui
garch(p=1,q=1,mv=diag,hmatrices=hd,rvectors=rd) / xjpn xfra xsui
garch(p=1,q=1,mv=cc) / xjpn xfra xsui
garch(p=1,q=1,mv=dcc,method=bfgs) / xjpn xfra xsui
*
* Compute the covariance matrix of the standardized residuals from
* the diagonal GARCH
*
set z1 = rd(t)(1)/sqrt(hd(t)(1,1))
set z2 = rd(t)(2)/sqrt(hd(t)(2,2))
set z3 = rd(t)(3)/sqrt(hd(t)(3,3))
vcv(matrix=cc)
# z1 z2 z3
*
* Compute the correlations from the multivariate GARCH
*
set rho12 = hh(t)(1,2)/sqrt(hh(t)(1,1)*hh(t)(2,2))
set rho13 = hh(t)(1,3)/sqrt(hh(t)(1,1)*hh(t)(3,3))
set rho23 = hh(t)(2,3)/sqrt(hh(t)(2,2)*hh(t)(3,3))
graph(header="Correlation of JPN with FRA",vgrid=||cc(1,2)||)
# rho12
graph(header="Correlation of JPN with SUI",vgrid=||cc(1,3)||)
# rho13
graph(header="Correlation of FRA with SUI",vgrid=||cc(2,3)||)
# rho23

You also might want to look at tsayp452.prg from the Tsay textbook examples.