Rolling-VAR-MGARCH-M
Posted: Sat Sep 01, 2018 10:40 am
The objectives of the code in the attached .RPF file are as follows:
- to estimate VARMA-MGARCH-in-mean model through the rolling windows where the fixed window size is 1250 and increment is 5.
- to generate 5-step ahead volatility forecasts in each window and save these series in hhat11 and hhat22 objects.
- this code also saves some of estimated coefficients from each regression in coef1, coef2, and coef3 objects.
I would be appreciated if anyone could check the code whether everything is OK. I suspect the mean model (i.e., VARMA) and var-cov model (i.e., MGARCH-M) use different sample periods.
Attached is the code file.
Many thanks
- to estimate VARMA-MGARCH-in-mean model through the rolling windows where the fixed window size is 1250 and increment is 5.
- to generate 5-step ahead volatility forecasts in each window and save these series in hhat11 and hhat22 objects.
- this code also saves some of estimated coefficients from each regression in coef1, coef2, and coef3 objects.
I would be appreciated if anyone could check the code whether everything is OK. I suspect the mean model (i.e., VARMA) and var-cov model (i.e., MGARCH-M) use different sample periods.
Attached is the code file.
Many thanks
Code: Select all
*
set rvar1 = 100.0*(log(var1)-log(var1{1}))
set rvar2 = 100.0*(log(var2)-log(var2{1}))
set rvar3 = 100.0*(log(var3)-log(var3{1}))
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source mvgarchfore.src
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compute d=1250
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dec series hhat11
dec series hhat22
dec series hhat33
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dec series hhat12
dec series hhat13
dec series hhat23
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clear hhat11
clear hhat22
clear hhat33
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clear hhat12
clear hhat13
clear hhat23
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clear coef1 coef2 coef3 coef4 coef5
clear tstat1 tstat2 tstat3 tstat4 tstat5
clear conv iter
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dec vect[series] sqrth(3)
dec vect[series] eps(3)
dec symm[series] hhs(3,3)
clear(zeros) hhs
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do regend = 2013:01:09, 2019:05:31, 5
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equation oil_eq rvar1
# constant sqrth(1) sqrth(2) sqrth(3) rvar1{1} rvar2{1} rvar3{1}
equation rvar2_eq rvar2
# constant sqrth(1) sqrth(2) sqrth(3) rvar1{1} rvar2{1} rvar3{1}
equation rvar3_eq rvar3
# constant sqrth(1) sqrth(2) sqrth(3) rvar1{1} rvar2{1} rvar3{1}
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group varmah oil_eq rvar2_eq rvar3_eq
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clear(zeros) sqrth eps
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do iters=1,3
estimate(model=varmah,resids=resids)
set eps(1) %regstart() * = resids(1)
set eps(2) %regstart() * = resids(2)
set eps(3) %regstart() * = resids(3)
end do iters
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compute cv0=%sigma
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garch(model=varmah, mv=bekk, robust, distrib=norm, rvectors=rv, hmatrices=hh, mvhseries=hhs, pmethod=simplex, piters=15, cvcrit=0.0001, iters=1000, presample=cv0,$
uadjust=%pt(eps,t,rv(t)),hadjust=%(sqrth(1)=sqrt(hhs(1,1)), sqrth(2)=sqrt(hhs(2,2)), sqrth(3)=sqrt(hhs(3,3)))) regend-d+1 regend rvar1 rvar2 rvar3
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@MVGarchFore(MV=BEKK, steps=5) hh rv
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do i=1,5
compute hhat11(regend+i) = hh(regend+i)(1,1)
compute hhat22(regend+i) = hh(regend+i)(2,2)
compute hhat33(regend+i) = hh(regend+i)(3,3)
end do i
*
compute coef1(regend) = %beta(9)
compute coef2(regend) = %beta(10)
compute coef3(regend) = %beta(11)
compute coef4(regend) = %beta(38)
compute coef5(regend) = %beta(44)
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compute tstat1(regend) = %tstats(9)
compute tstat2(regend) = %tstats(10)
compute tstat3(regend) = %tstats(11)
compute tstat4(regend) = %tstats(38)
compute tstat5(regend) = %tstats(44)
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compute conv(regend) = %converged
compute iter(regend) = %ITERS
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end do regend
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print / coef1 tstat1 coef2 tstat2 coef3 tstat3
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print / coef4 tstat4 coef5 tstat5
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print / hhat11 hhat22 hhat33
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print / conv iter