The RATS Software Forum

Posted: **Tue Nov 05, 2013 11:51 am**

hi,
I'm Using rats code for two step estimation DCC-GARCH. how and where should i to compute a mean equation r_t=μ+γ_1 r_(t-1)+γ_2 r_(t-1)^us+ε_t
let say if i want to see the DCC-GARCH for US VS ASEAN country.. below are code.

Code: Select all

*
OPEN DATA "C:\Users\user\Desktop\msci rats.xls"
CALENDAR(D) 1993:1:1
DATA(FORMAT=XLS,ORG=COLUMNS,SHEET="pre",LEFT=2) 1993:01:01 1996:12:31 USXUS USXMalaysia USXPhilipines USXSingapore $
 USXThailand USXIndonesia



*
* Convert exchange rates to returns, and place into the VECT[SERIES] X
* that will be used as the dependent variables in the GARCH model.
*
compute n=6
dec vect[series] x(n)
compute i=0
dofor [string] s = "US" "Malaysia" "Philipines" "Singapore" "Thailand" "Indonesia"
   compute xrate="usx"+s,i=i+1
   set x(i) = 100.0*log(%s(xrate)/%s(xrate){1})
end dofor
*

*
dec vect[series] eps(n)
*
* FULLBETA will be the coefficient vector for the full model. This
* includes 4 univariate GARCH coefficients for each of the dependent
* variables, plus the 2 DCC coefficients.
*
dec vect fullbeta(4*n+2)
*
* Do univariate GARCH models. Save the standardized residuals into
* eps(i). Copy the coefficients into the proper slots in the full beta
* matrix.
*
do i=1,n
   garch(p=1,q=1,resids=r,hseries=h) / x(i)
   set eps(i) = r/sqrt(h)
   do j=1,4
      compute fullbeta(n*(j-1)+i)=%beta(j)
   end do j
end do i
*
* Compute the covariance matrix of the standardized residuals
*
vcv(matrix=rr)
# eps
*
* Create the series[symm] uu (outer product of residuals). Make it the
* unconditional value prior to the sample.
*
dec series[symm] uu q
gset uu %regstart() %regend() = %outerxx(%xt(eps,t))
gset uu 1 %regstart()-1 = rr
gset q  = rr
*
* Log likelihood for the DCC phase, taking the residuals as given
*
nonlin a b
dec frml[symm] qf
frml qf   = (qx=(1-a-b)*rr+a*uu{1}+b*q{1})
frml logl = q=qf,%logdensity(%cvtocorr(q),%xt(eps,t))
compute b=.80,a=.10
maximize logl 2 *
*
* Insert the estimates into the final two slots in fullbeta
*
compute fullbeta(4*n+1)=%beta(1),fullbeta(4*n+2)=%beta(2)
*
* Do one iteration of the full model with METHOD=BHHH to get
* the grand covariance matrix.
*
garch(p=1,q=1,mv=dcc,method=bhhh,initial=fullbeta,iters=1) / x

Thank you so much.

Posted: **Tue Nov 05, 2013 7:06 pm**

What do you mean by AR(5,1)?

You would need to create a MODEL for the mean. This is probably somewhat similar to what you're doing, except that it does a one-step DCC (which should work fine as well with a model the size of yours).

Code: Select all

cal(daily) 1990:1:1
open data stock_index_2003.xls
data(org=obs,format=xls) 1990:1:1 2003:3:21
*
compute gstart=1990:1:3 , gend=2003:3:21
*
* Parameters for the regression function
*
dec vect[series] y(10) u(10)
compute fill=0
dofor s = th in mal ko hk jp ph sg tw us
   compute fill=fill+1
   set y(fill) = 100.0*log(s{0}/s{1})
end dofor s
*
dec vect[equation] asiaeq(9)
do i=1,9
   equation asiaeq(i) y(i)
   # constant y(i){1} y(10){1}
end do i
*
equation useq y(10)
# constant y(10){1}
group mod10 asiaeq(1) asiaeq(2) asiaeq(3) asiaeq(4) asiaeq(5) asiaeq(6) asiaeq(7) $
  asiaeq(8) asiaeq(9) useq
*
garch(model=mod10,mv=dcc,hmatrices=hh,rvectors=uu)

Posted: **Wed Nov 06, 2013 3:58 am**

Thank you so much for your help.
1)Let say If i want to use the two step estimation. Is it possible?
2)If its not possible, let say I want to compute the correlation matrix from the code you suggest. how can it be done?

the result that i'm trying to achieve is almost similar to the estimation of this journal http://ideas.repec.org/a/eee/reveco/v20y2011i4p717-732.html

Posted: **Wed Nov 06, 2013 4:10 am**

I run the code you suggest..the mean result is what i wanted earlier. However, the result doesn't seem right. the DCC(2)= 0.0000. such as below:

MV-GARCH, DCC - Estimation by BFGS
Convergence in 82 Iterations. Final criterion was 0.0000099 <= 0.0000100
Daily(5) Data From 1993:01:05 To 1996:12:31
Usable Observations 1041
Log Likelihood -8506.6482

Variable Coeff Std Error T-Stat Signif
************************************************************************************
1. Constant 0.055238686 0.019526413 2.82892 0.00467052
2. Y(1){1} 0.055440327 0.035206869 1.57470 0.11532525
3. Y(6){1} 0.012144613 0.015931929 0.76228 0.44589205
4. Constant 0.087311531 0.034064607 2.56312 0.01037375
5. Y(2){1} 0.043466434 0.024671341 1.76182 0.07809991
6. Y(6){1} 0.005943412 0.027563362 0.21563 0.82927832
7. Constant 0.045667047 0.040256926 1.13439 0.25663107
8. Y(3){1} 0.097434735 0.025387984 3.83783 0.00012413
9. Y(6){1} 0.089486132 0.032482066 2.75494 0.00587030
10. Constant 0.048093725 0.025038842 1.92076 0.05476138
11. Y(4){1} 0.029213504 0.025911554 1.12743 0.25956006
12. Y(6){1} -0.031159525 0.021744623 -1.43298 0.15186466
13. Constant 0.008674690 0.041485635 0.20910 0.83436935
14. Y(5){1} 0.051795482 0.025703731 2.01510 0.04389460
15. Y(6){1} 0.050839743 0.034491399 1.47398 0.14048611
16. Constant 0.052249177 0.033598723 1.55509 0.11992365
17. Y(6){1} 0.168462739 0.032438261 5.19333 0.00000021
18. C(1) 0.011501953 0.005672011 2.02784 0.04257619
19. C(2) 0.040690054 0.012241610 3.32391 0.00088764
20. C(3) 0.022923954 0.009537507 2.40356 0.01623637
21. C(4) 0.118722509 0.033492446 3.54475 0.00039298
22. C(5) 0.054064488 0.023572064 2.29358 0.02181446
23. C(6) 0.516272830 0.074088081 6.96837 0.00000000
24. A(1) 0.041577975 0.012769004 3.25616 0.00112928
25. A(2) 0.066856637 0.011125336 6.00940 0.00000000
26. A(3) 0.042693045 0.010269632 4.15721 0.00003222
27. A(4) 0.121562926 0.027321289 4.44938 0.00000861
28. A(5) 0.059464020 0.014580381 4.07836 0.00004535
29. A(6) 0.338189173 0.020195938 16.74541 0.00000000
30. B(1) 0.927543713 0.023739598 39.07159 0.00000000
31. B(2) 0.908610687 0.015387869 59.04721 0.00000000
32. B(3) 0.944343380 0.012808201 73.72959 0.00000000
33. B(4) 0.743745177 0.057943339 12.83573 0.00000000
34. B(5) 0.915760347 0.022412947 40.85854 0.00000000
35. B(6) 0.317288627 0.047455332 6.68605 0.00000000
36. DCC(1) 0.016089603 0.007604907 2.11569 0.03437144
37. DCC(2) 0.000000000 0.000037915 4.78893e-013 1.00000000

Posted: **Wed Nov 06, 2013 9:50 am**

You would have to post the program and data for me to see what's happening. However, it looks like variable 6 (U.S.?) has very different GARCH dynamics than the other series. DCC really doesn't work well unless all the series are fairly similar since it forces the joint GARCH dynamics to be explained by just 2 parameters.

Posted: **Wed Nov 06, 2013 10:43 am**

hi,

Here is the data and the program.

Posted: **Wed Nov 06, 2013 10:58 am**

1. You have the variables defined wrong. You need US to be at the end of the list since I assume that's the one you want as the 6th variable:

dofor s = MY PH SG TH IND US
compute fill=fill+1
set y(fill) = 100.0*log(s{0}/s{1})
end dofor s

2. You're not going to get a DCC model to fit these data---there just isn't enough similarity. The US data is basically uncorrelated with the other five and even the Asia series are generally only mildly correlated (other than MY and SG). Maybe there are some timing issues with your data?

Posted: **Wed Nov 06, 2013 11:46 am**

Hi thank you so much for your help.. its help a lot.

I have another questions.

from the program, how to Compute the covariance matrix of the standardized residuals? and plot out the graph?

Posted: **Sat Nov 09, 2013 10:14 pm**

HI Tom,

I've another question. how do i apply the ljung box in my data? is it ok if i just use :

garch(model=mod6,mv=dcc,hmatrices=hh,rvectors=rd) / y
set z1 = rd(t)(1)/sqrt(hh(t)(1,1))
set z2 = rd(t)(2)/sqrt(hh(t)(2,2))
set z3 = rd(t)(3)/sqrt(hh(t)(3,3))
set z4 = rd(t)(4)/sqrt(hh(t)(4,4))
set z5 = rd(t)(5)/sqrt(hh(t)(5,5))
set z6 = rd(t)(6)/sqrt(hh(t)(6,6))
@bdindtests(number=40) z1
@bdindtests(number=40) z2
@bdindtests(number=40) z3
@bdindtests(number=40) z4
@bdindtests(number=40) z5
@bdindtests(number=40) z6

*
* Multivariate Q statistic. This requires transforming the residuals to
* eliminate the time-varying correlations.
*
dec vect[series] zu(%nvar)
do time=%regstart(),%regend()
compute %pt(zu,time,%solve(%decomp(hh(time)),rd(time)))
end do time
@mvqstat(lags=40)
# zu

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