多元GARCH的Matlab程序

1、多元 GARCH 模型預(yù)測的 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)parametersparameteters. F % OUTPUTS:- A (k*(k+1)/2+p*k2+q*k2 vector of

3、estimated- A k x k x t 3 dimension matrix of conditional covariancesor any k2 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 Ht- A t by 1 vector of ind

4、ividual likelihoods- A t by k matrix of multivariate standardized residuals- A numParams2 square matrix of robust Standad Errors(A(-1)*B*A(-1)*t(-1)likelihoodsstdresidstderrors%- The estimated inverse of the non-robust Standard errors- The estimated covariance of teh scoresscores- A t by numParams m

5、atrix 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,on,DiffMaxC hange,1e-2);startingparameters=scalar_bekk_mvgarch

6、(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)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=re

7、shape(newA,k*k*p,1);newB=reshape(newB,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); likeliho

8、odsminus=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,q,k,k2,t);likelihoodsminus(:,i