【老生谈算法】多元GARCH模型预测的Matlab程序.doc
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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
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