美赛常见参考代码;多元GARCH模型预测的Matlab程序.zip

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);

% 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

% 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','DiffMaxC

hange',1e-2);

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.MaxIter=5000*length(startingparameters);