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* Estimate an AR(3)-GARCH(1,1) model for excess returns of SP500
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all 0 792:1
open data sp500.dat
*
data(org=obs) / rt
*
set h = 0.0
*
nonlin p0 p1 p2 p3 a0 a1 b1 
*
frml at = rt(t) - p0 - p1*rt{1} - p2*rt{2} - p3*rt{3}
frml gvar = a0 + a1*at(t-1)**2+b1*h(t-1)
frml garchlog = -0.5*log(h(t)=gvar(t))-0.5*at(t)**2/h(t)
*
smpl 5 792
*
* initial values
*
compute p0 =0.01, p1 = 0.1, p2 = 0.01,p3 = 0.01
compute a0 = 0.01, a1 = 0.1, b1 = 0.7
*
maximize(method=bhhh,recursive,iterations=150) garchlog
*
set resi = at(t)
set fv = gvar(t)                                                      
set resid = at(t)/sqrt(fv(t)) 
set residsq = resid(t)*resid(t) 
table / resid
*** 
*** Checking standardized residuals ***
cor(qstats,number=20,span=10) resid 
*** Checking squared standardized residuals ***
cor(qstats,number=20,span=10) residsq
*** print the last few data for forecasting
print 788 792 rt resi fv