多元GARCH的Matlab程序

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:%to estimatea full bekk multivariate garch model.% usage:%parameters,loglikelihood, ht, likelihoods, stdresid,stderrors,a, b, scores= full_bek

2、k_mvgarch(data,p,q,options);% inputs:%data- a t by k matrix of zeromeanresiduals%p- the lag lengthof the innovation process%q- the lag lengthof the ar process%options- (optional) options for the optimization(fminunc)% outputs:%parameters- a (k*(k+1)/2+p*k2+q*k2vector of estimatedparameteters.f%or an

3、yk2 setof innovation or ar parametersx,%reshape(x,k,k)will give thecorrect matrix%to recoverc, useivech(parmaeters(1:(k*(k+1)/2)%loglikelihood - the loglikelihood of thefunction atthe optimum%ht- a k x k x t 3 dimension matrix of conditional covariances%likelihoods- a t by 1 vector of individual lik

4、elihoods%stdresid- a t by k matrix of multivariate standardizedresiduals%stderrors- a numparams2 square matrix of robust standaderrors(a(-1)*b*a(-1)*t(-1)%a- theestimatedinverseof thenon-robuststandarderrors%b- the estimatedcovarianceof tehscores%scores- a t by numparamsmatrix of individual scores%

5、needto try andgetsomesmartstartgin valuesif size(data,2) size(data,1)data=data;endt k=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=starting

6、parameters(1:(k*(k+1)/2);c=ivech(startingparameters(1:(k*(k+1)/2)*ivech(startingparameters(1:(k*(k+1)/2);newa=;newb=;for i=1:pnewa=newa diag(ones(k,1)*startingparameters(k*(k+1)/2)+i);endfor i=1:qnewb=newb diag(ones(k,1)*startingparameters(k*(k+1)/2)+i+p);endnewa=reshape(newa,k*k*p,1);newb=reshape(n

7、ewb,k*k*q,1);startingparameters=cchol;newa;newb;if nargin=6a=hessian_2sided(full_bekk_mvgarch_likelihood,parameters,data,p,q,k,k2,t);h=max(abs(parameters/2),1e-2)*eps(1/3);hplus=parameters+h;hminus=parameters-h;likelihoodsplus=zeros(t,length(parameters);likelihoodsminus=zeros(t,length(parameters);fo

8、r i=1:length(parameters)hparameters=parameters;hparameters(i)=hplus(i);holder,indivlike=full_bekk_mvgarch_likelihood(hparameters,data,p,q,k,k2,t);likelihoodsplus(:,i)=indivlike;endfor i=1:length(parameters)hparameters=parameters;hparameters(i)=hminus(i);holder,indivlike=full_bekk_mvgarch_likelihood(hparameters,data,p,q,k,k2,t);likelihoodsminus(:,i