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| *
* Replication file for Diebold and Yilmaz(2009), "Measuring Financial
* Asset Return and Volatility Spillovers, with Application to Global
* Equity Markets," Economic Journal, vol. 119, no. 534, 158-171.
*
* Analysis of returns data
*
* If you make usegirf=1, this will use the generalized spillover
* measures proposed in Diebold and Yilmaz(2011), "Better to Give than to
* Receive: Predictive Directional Measurement of Volatility Spillovers",
* IJF, to appear. usegirf=0 does the Choleski factorization used in the
* 2009 paper.
*
compute usegirf=1
*
open data vol1.xls
calendar(d) 2012:07:03
data(format=xls,org=columns,sheet="vol1",top=1,left=1) 2012:07:03 2015:12:30 $
lnaexvol lnaxvol lnftjsevol lnftsevol lnsp500vol
dec vect[int] returns
enter(varying) returns
#lnaexvol lnaxvol lnftjsevol lnftsevol lnsp500vol
dec vect[string] longlabels(%size(returns))
enter longlabels
# "lnaexvol" "lnaxvol" "lnftjsevol" "lnftsevol" "lnsp500vol"
dec vect[string] shortlabels(%size(returns))
enter shortlabels
# "lnaexvol" "lnaxvol" "lnftjsevol" "lnftsevol" "lnsp500vol"
*
* Setup and estimate two lag VAR
*
system(model=returnvar)
variables returns
lags 1 1
det constant
end(system)
*
estimate(noprint)
*
* Save the full estimation range for later use
*
compute rstart=%regstart(),rend=%regend()
*
* Analyze the 5 step responses
*
compute nsteps=5
*********************************************************************
*
* Produce the appropriate "factor" matrix from %sigma
*
function FactorMatrix
type rect FactorMatrix
if usegirf
compute FactorMatrix=%sigma*inv(%diag(%sqrt(%xdiag(%sigma))))
else
compute FactorMatrix=%decomp(%sigma)
end
*********************************************************************
*
compute gfactor=FactorMatrix()
*
errors(model=returnvar,steps=nsteps,factor=gfactor,stderrs=gstderrs,noprint,results=gfevd)
compute gfevdx=%xt(gfevd,nsteps)
*
* These are for computing the contributions from others, to others and
* to others including own for each variable.
*
dec vect tovar(%nvar) fromvar(%nvar) tototal(%nvar)
ewise fromvar(i)=%sum(%xrow(gfevdx,i))-gfevdx(i,i)
ewise tovar(i)=%sum(%xcol(gfevdx,i))-gfevdx(i,i)
ewise tototal(i)=tovar(i)+1-fromvar(i)
compute spillover=100.0*%sum(tovar)/%nvar
*
report(action=define,title="Spillover Table for Industry Returns")
report(atrow=1,atcol=2,align=center,fillby=rows) shortlabels
report(atrow=2,atcol=1,fillby=cols) shortlabels
report(atrow=2,atcol=2) 100.0*gfevdx
report(atrow=%nvar+2,atcol=1,fillby=rows) "Contribution to others" 100.0*tovar
report(atrow=%nvar+3,atcol=1,fillby=rows) "Contribution including own" 100.0*tototal
report(atcol=%nvar+2,atrow=1) "From Others"
report(atcol=%nvar+2,atrow=2,fillby=cols) 100.0*fromvar
report(atrow=%nvar+2,atcol=%nvar+2,fillby=cols) 100.0*%sum(tovar)
report(atrow=%nvar+3,atcol=%nvar+2,align=right) %strval(spillover,"##.#")+"%"
report(atrow=2,atcol=2,torow=%nvar+1,tocol=%nvar+1,action=format,picture="*.#")
report(atrow=%nvar+2,torow=%nvar+3,atcol=1,tocol=%nvar+2,action=format,picture="###")
report(atcol=%nvar+2,atrow=2,torow=%nvar+2,action=format,picture="###")
report(action=show)
*
* Rolling window analysis
*
compute nspan=60
dec series totalspill
dec symm netpairvar(%nvar,%nvar) pafromuvar(%nvar,%nvar) pafromdvar(%nvar,%nvar)
dec vect[series] fromspill(%nvar) tospill(%nvar) netspill(%nvar)
dec symm[series] netpairspill(%nvar,%nvar) pafromuspill(%nvar,%nvar) pafromdspill(%nvar,%nvar)
clear(zeros) fromspill topspill totalspill netspill pafromuspill pafromdspill netpairspill
* set pafromspill rstart+nspan-1 rend = 0.0
do end=rstart+nspan-1,rend
estimate(noprint) end-nspan+1 end
*
* Skip any data points where the rolling VAR has an explosive root.
*
eigen(cvalues=cv) %modelcompanion(returnvar)
if %cabs(cv(1))>=1.0 {
compute totalspill(end)=%na
next
}
compute gfactor=FactorMatrix()
errors(model=returnvar,steps=nsteps,factor=gfactor,stderrs=gstderrs,noprint,results=gfevd)
compute gfevdx=%xt(gfevd,nsteps)
ewise tovar(i)=%sum(%xcol(gfevdx,i))-gfevdx(i,i)
ewise fromvar(i)=%sum(%xrow(gfevdx,i))-gfevdx(i,i)
ewise netpairvar(i,j)=gfevdx(i,j)-gfevdx(j,i)
ewise pafromuvar(i,j)=gfevdx(i,j)
ewise pafromdvar(i,j)=gfevdx(j,i)
compute totalspill(end)=100.0*%sum(tovar)/%nvar
compute %pt(fromspill,end,100.0*fromvar)
compute %pt(tospill,end,100.0*tovar)
compute %pt(netspill,end,100.0*(tovar-fromvar))
compute %pt(netpairspill,end,100.0*netpairvar)
compute %pt(pafromuspill,end,100.0*pafromuvar)
compute %pt(pafromdspill,end,100.0*pafromdvar)
end do end
graph(header="Figure 1. Total Volatility Spillovers, SA And UK,NTLD,US,GER")
# totalspill rstart+nspan-1 rend
*
spgraph(vfields=3,hfields=2,$
header="Figure 3. Directional Volatility Spillovers, FROM SA And UK,NTLD,US,GER")
do i=1,%nvar
graph(header=longlabels(i),style=bar)
# fromspill(i) rstart+nspan-1 rend
end do i
spgraph(done)
*
*
spgraph(vfields=3,hfields=2,$
header="Figure 4. Directional Volatility Spillovers, TO SA And UK,NTLD,US,GER")
do i=1,%nvar
graph(header=longlabels(i),style=bar)
# tospill(i) rstart+nspan-1 rend
end do i
spgraph(done)
*
spgraph(vfields=3,hfields=2,$
header="Figure 5. Net Volatility Spillovers,SA And UK,NTLD,US,GER")
do i=1,%nvar
graph(header=longlabels(i),style=bar)
# netspill(i) rstart+nspan-1 rend
end do i
spgraph(done)
*
spgraph(vfields=5,hfields=2,$
header="Figure 6. up Pairwise Volatility Spillovers")
do i=1,%nvar
do j=i+1,%nvar
graph(header=shortlabels(i)+shortlabels(j),style=bar)
#pafromuspill(i,j)
end do j
end do i
spgraph(done)
*
spgraph(vfields=5,hfields=2,$
header="Figure 7. down Pairwise Volatility Spillovers")
do i=1,%nvar
do j=i+1,%nvar
graph(header=shortlabels(i)+shortlabels(j),style=bar)
#pafromdspill(j,i)
end do j
end do i
spgraph(done)
*
spgraph(vfields=5,hfields=2,$
header="Figure 8. Net Pairwise Volatility Spillovers")
do i=1, %nvar
do j=i+1,%nvar
graph(header=shortlabels(i)+shortlabels(j),style=bar)
#netpairspill(i,j)
end do j
end do i
spgraph(done)
print 2012:07:03 2015:12:30 totalspill tospill fromspill netspill netpairspill pafromuspill pafromdspill |
