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1、多元GARCH模型预测的Matlab程序function[parameters,loglikelihood,Ht,likelihoods,stdresid,stderrors,A,B,scores]=full_bekk_mvgarch(data,p,q,BEKKoptions);%PURPOSE:%ToEstimateafullBEKKmultivariateGARCHmodel.%%%USAGE:%[parameters,loglikelihood,Ht,likelihoods,stdresid,stderrors,A,B,scores]=full_bekk_mvgarch
2、(data,p,q,options);%%%INPUTS:%data-Atbykmatrixofzeromeanresiduals%p-Thelaglengthoftheinnovationprocess%q-ThelaglengthoftheARprocess%options-(optional)Optionsfortheoptimization(fminunc)%%OUTPUTS:%parameters-A(k*(k+1))/2+p*k^2+q*k^2vectorofestimatedparameteters.F%oranyk^2setofInnovationorARpa
3、rametersX,%reshape(X,k,k)willgivethecorrectmatrix%TorecoverC,useivech(parmaeters(1:(k*(k+1))/2)%loglikelihood-Theloglikelihoodofthefunctionattheoptimum%Ht-Akxkxt3dimensionmatrixofconditionalcovariances%likelihoods-Atby1vectorofindividuallikelihoods%stdresid-Atbykmatrixofmultivariatestandard
4、izedresiduals%stderrors-AnumParams^2squarematrixofrobustStandadErrors(A^(-1)*B*A^(-1)*t^(-1))%A-Theestimatedinverseofthenon-robustStandarderrors%B-Theestimatedcovarianceoftehscores%scores-AtbynumParamsmatrixofindividualscores%needtotryandgetsomesmartstartginvaluesifsize(data,2)>size(data,1)
5、data=data';end[tk]=size(data);k2=k*(k+1)/2;scalaropt=optimset('fminunc');scalaropt=optimset(scalaropt,'TolFun',1e-1,'Display','iter','Diagnostics','on','DiffMaxChange',1e-2);startingparameters=scalar_bekk_mvgarch(data,p,q,scalaropt);CChol=startingparameters(1:(k*(k+1))/2);C=ivech(startingpa
6、rameters(1:(k*(k+1))/2))*ivech(startingparameters(1:(k*(k+1))/2))';newA=[];newB=[];fori=1:pnewA=[newAdiag(ones(k,1))*startingparameters(((k*(k+1))/2)+i)];endfori=1:qnewB=[newBdiag(ones(k,1))*startingparameters(((k*(k+1))/2)+i+p)];endnewA=reshape(newA,k*k*p,1);newB=reshape(newB,k*k*q,1);star
7、tingparameters=[CChol;newA;newB];ifnargin<=3
8、isempty(BEKKoptions)options=optimset('fminunc');options.Display='iter';options.Diagnostics='on';options.TolX=1e-4;options.TolFun=1e-4;options.MaxFunEvals=5000*length(startingparameters);options.MaxIter=5000*le
