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 Estimate a full BEKK multivariate GARCH model. % USAGE:%parameters, loglikelihood, Ht, likelihoods, stdresid, stderrors, A, B, score

2、s= full_bekk_mvgarch(data,p,q,options);% % INPUTS:dataq options- A t by k matrix of zero mean residuals- The lag length of the innovation process- The lag length of the AR process- (optional) Options for the optimization(fminunc)% OUTPUTS:parametersparameteters. F %-A (k*(k+1)/2+p*kA2+q*kA2vector of

3、 estimated- A k x k x t 3 dimension matrix of conditional covariancesor any kA2 set of Innovation or AR parameters X, reshape(X,k,k) will give the correct matrix To recover C, use ivech(parmaeters(1:(k*(k+1)/2) loglikelihood - The loglikelihood of the function at the optimum Htlikelihoods- A t by 1

4、vector of individual likelihoods- A t by k matrix of multivariate standardized residuals- A numParamsA2 square matrix of robust Standad Errors(AA(-1)*B*AA(-1)*tA(-1)stdresidstderrors%- The estimated inverse of the non-robust Standard errorsscores- The estimated covariance of teh scores- A t by numPa

5、rams matrix of individual scores% need to try and get some smart startgin 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'

6、;,'on','DiffMaxC hange',1e-2);startingparameters=scalar_bekk_mvgarch(data,p,q,scalaropt);CChol=startingparameters(1:(k*(k+1)/2);C=ivech(startingparameters(1:(k*(k+1)/2)*ivech(startingparameters(1:(k*(k+1)/2)'JnewA=;newB=;for i=1:pnewA=newA diag(ones(k,1)*startingparameters(k*(k+1

7、)/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(newB,k*k*q,1); startingparameters=CChol;newA;newB;if nargin<=3 | isempty(BEKKoptions)options=optimset('fminunc');options.Display='iter'options.Diagnostics=

8、9;on'options.TolX=1e-4;options.TolFun=1e-4; options.MaxFunEvals=5000*length(startingparameters); options.MaxIter=5000*length(startingparameters);elseoptions=BEKKoptions;end parameters=fminunc('full_bekk_mvgarch_likelihood',startingparameters,options,data, p,q,k,k2,t);loglikelihood,likeli

9、hoods,Ht=full_bekk_mvgarch_likelihood(parameters,data,p,q,k,k2,t); loglikelihood=-loglikelihood; likelihoods=-likelihoods;% Standardized residuals stdresid=zeros(size(data);for i=1:t stdresid(i,:)=data(i,:)*Ht(:,:,iF(-0.5);end%Std Errorsif nargout>=6A=hessian_2sided('full_bekk_mvgarch_likelih

10、ood',parameters,data,p,q,k,k2,t); h=max(abs(parameters/2),1e-2)*epsA(1/3);hplus=parameters+h;hminus=parameters-h; likelihoodsplus=zeros(t,length(parameters); likelihoodsminus=zeros(t,length(parameters);for 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