BEKK-GARCH模型之Matlab编程

1、BEKK-GARCH 模型之 Matlab 编程function parameters, loglikelihood, Htz likelihoods, stdresid, stderrors, A, B, scores= full_bekk_mvgarch(data,p/q/ BEKKoptions)% PURPOSE:% To Estimate a full BEKK multivariate GARCH model *SEE WARNING AT END OF HELP FILE*% USAGE:% parameters, loglikelihood, Ht, likelihoods,

2、stdresid, stderrors, Az Bz scores = full_bekk_mvgarch(data,p,qzoptions);% 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% op廿ons - (optional) Options for the optimization(fminunc)% OUTPUTS:% parameters A (k*(k+

3、l)/2+p*kA2+q*kA2 vector of estimated parameteters.% For any kA2 set of Innovation or AR parameters X,% reshapefX) will give the correct matrix% To recover C, use ivech(parmaeters(l:(k*(k+l)/2)% loglikelihood The loglikelihood of the function at the optimum%Ht-Akxkxt3 dimensi on matrix of conditional

4、 covaria nces% likelihoods - A t by 1 vector of individual likelihoods% stdresld - A t by k matrix of multivariate standardized residuals% stderrors - A numParamsA2 square matrix of robust Standad Errors(AA(-l)*B*AA(-l)*tA(-l)% A - The estimated in verse of the non-robust Sta ndard errors% B The est

5、imated covarianee of teh scores% scores A t by numParams matrix of individual scores% COMMENTS:% You should multiply the data by a constant so that the min std(data) is at least 10. This will help estima tion% * THIS FUNCTION INVOLVES ESTIMATING MANY PARAMETERS. THE EXACT NUMBER OF PARAMETERS% * NEE

6、DING TO BE ESTIMATED IS (k*(k+l)/2+pkA2+qkA2. FOR A 5 VARIATE (1,1) MODEL THIS% * 65 PARAMETERS. ESTIMATION CAN TAKE A VERY LONG TIME. A 10 ASSET MODEL TOOK 12 % * HOURS ON APIII-700.*京*京*拿*索*車*京*京拿*車*% Author: Kevin Sheppard% kevin.sheppardeconomics.ox.ac.uk% Revision: 2 Date: 1/31/2001% need to tr

7、y and get some smart startgin valuesif size(data,2) size(data,l)data 二 data;endt k=size(data);k2=k*(k+l)/2;scalaropt=optimset(,fminunc,);scalaropt=optimset(scalaropt/,TolFun,/le-l/,Display,/,iter,Diagnostics,/,on7DiffMaxChange,/le-2)/startingparameters=scalar_bekk_mvgarch(data,pzq,scalaropt);CChol=s

8、tartingparameters(l:(k*(k+l)/2)； %C=ivech(startingparameters(l:(k*(k+l)/2)*ivech(startingparameters(l:(k*(k+l)/2)1; newA=);newB=;for i=l:pnewA=newA diag(ones(k/l)*startingparameters(k*(k+l)/2)+i); %#okendfor i=l:qnewB=newB diag(ones(k,l)*startingparameters(k*(k+l)/2)+i+p); %#okendnewA=reshape(newA/k

9、*k*p,l)； newB=reshape(newB/k*k*q/l)； startingparameters=CChol;newA;newB;if nargin=6A=hessian_2sided(,full_bekk_mvgarchikGlihood：parametGrs，data,p,q，k，k2，t); h=max(abs(parameters/2)/le-2)*epsA(l/3);hplus=parameters+h;hminus=parameters-h;likelihoodsplus=zeros(t,le ngth(parameters);likelihoodsminus=zer

10、os(tje ngth(parameters);for i=l:length(parameters)hparamete rs=pa ra mete rs;hparameters(i)=hplus(i);HOLDER, indivlike = full_bekk_mvgarchjikelihood(hparameters/data/p/q/k/k2/t); likelihoodsplus(:/i)=indivlike;endfor i=l:length(parameters)hparamete rs=pa ra meters;hparameters(i)=hmi nus(i);HOLDER, indivlike = full_bekk_mvgarchikQlihood(hparameters，dataaqkk2，t);likelihoodsmi nus(:)=i ndivlike;e