GARCH模型在Matlab中的实现.docVIP

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)); new