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(完整word版)GARCH模型在Matlab中的实现-

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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, scores] = full_bekk_mvgarch(data,p,q,options; % %
% INPUTS: % data - A t by k matrix of zero mean residuals % p - The lag length of the innovation process % q
- The lag length of the AR process % options - (optional Options for the optimization(fminunc %
% OUTPUTS: % parameters - A (k*(k+1/2+p*k^2+q*k^2 vector of estimated parameteters. F % or any k^2 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 k x k x t 3 dimension matrix of conditional covariances % likelihoods
- A t by 1 vector of individual likelihoods % stdresid - A t by k matrix of multivariate standardized residuals % stderrors - A numParams^2 square matrix of robust Standad Errors(A^(-1*B*A^(-1*t^(-1 % A - The estimated inverse of the non-robust Standard errors % B - The estimated covariance of teh scores % scores - A t by numParams matrix of individual scores % need to try and get some smart startgin values
if size(data,2 > size(data,1 data=data'; end
[t 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=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:p newA=[newA diag(ones(k,1*startingparameters(((k*(k+1/2+i]; end for i=1:q newB=[newB diag(ones(k,1*startingparameters(((k*(k+1/2+i+p]; end newA=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='on'; options.TolX=1e-4;
options.TolFun=1e-4; options.MaxFunEvals=5000*length(startingparameters; options.MaxIter=5000*length(startingparameters;

else options=BEKKoptions; end parameters=fminunc('full_bekk_mvgarch_likelihood',startingparameters,options,data,p,q,k,k2,t;

[loglikelihood,likelihoods,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(:,:,i^(-0.5; end

%Std Errors if nargout>=6 A=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;