GARCH模型在Matlab中的实现

多GARCH模型预测的Matlab程序function parameters，loglikelihood，Ht，lilihooks，stdresid，stderrors，a，b，scores =full _ bekk _ mv GARCH (数据，p，q，bekk选项)；% PURPOSE:% toestimateafullbekkmultivariategarchmodel.% USAGE:% 参数，日志链接，Ht，lilihooks，stdresid，STD错误，a，b，scores =full _ bekk _ mv图形(数据，p，q，选项)；% INPUTS:a-atbykmatrixofzeromeanresiduals% p-thelaglengthoftheinnovationprocess% q - The lag length of the AR process% options-(可选) optionsforthesetoptimization (FMI nunc )% OUTPUTS:% parameters-a (k * (k1) )/2 p * k 2q * k 2vectorofestimatedparameters.f% Oran yk 2setofinnovationoraparametersx% reshape(X，k，k) will give the correct matrix% To recover C，useivech (参数(1: (k * (k1) )/2 )% log likelihood-theloglikelihoodofthefunctionattheoptimum% ht-AK kxt 3维护协议分析程序(dimensitionmationofconditionalcovariances )% Lili hoods-ATB y1 vectorofindividuallihoods% STD resid-atbykmetrityofmultiveritionstandiredrequiless% STD错误- num参数2squarematrixofrobuststandarderrors (a (-1 ) * b * a (-1 ) * t (-1 ) )% a-theestimatedsinverseufthenon-robuststandarderrors% b-theestimatedcovarianceoftehsscores% scores-atbynumparamsmatrixofindividualscores% needtotryandgetsomeonsmartstartginvaluesif size(data，2) size(data，1 )data=data；结束 tk =size (数据)k2=k*(k 1)/2；scalaropt=optimset(fminunc )scalaropt=optimset(scalaropt，TolFun，1e-1，Display，iter，诊断，on，DiffMaxChange，1e-2 )；起始参数=scalar _ bekk _ mv GARCH (数据，p，q，比例因子)CCC lol=启动参数(1: (k * (k1 ) )/2 )c=开始参数(1: (k * (k1) )/2 ) *开始参数(1: (k * (k1) )/2 )；newA=；newB=；for i=1:pnewa= newa诊断(ones (k，1 ) ) *启动参数(k * (k1 ) )/2 ) I ) ；结束for i=1:qnewb= newb诊断(ones (k，1 ) ) *启动参数(k * (k1 ) )/2 ) ITP ) ；结束newA=reshape(newA，k*k*p，1 )newB=reshape(newB，k*k*q，1 )startingparameters=CChol； newA； newB；if nargin=3(后退选项)options=optimset(fminunc )options.Display=iter；options.Diagnostics=on；options.TolX=1e-4；options.TolFun=1e-4；options.maxfunevals=5000 * length (启动参数)options.maxitera=5000 *长度(启动参数)elseoptions=BEKKoptions；结束参数=FMI nunc (full _ bekk _ mv GARCH _ likelihood，启动参数，选项，数据，p，q，k，k，t )；loglikelihood，lililihood，ht =full _ bekk _ mv GARCH _ Lili hood (参数，数据，p，q，k，k，t )；loglilikelihood=-loglilikelihood；lilihood=-lilihood；% Standardized residualsstdresid=zeros(size(data ) )for i=1:tstdresid(i，)=data(i， ) * ht (:i)(-0.5 )；结束% STD错误if nargout=6a=hessian _2sided (full _ bekk _ mv GARCH _ likelihood，参数，数据，p，q，k，k，t )；h=max (ABS (参数/2)，1e-2)*eps(1/3)h plus=参数h；h是=parameters-h；likelihoodsplus=zeros(t，length (参数) )lilihoodsd=zeros(t，length (参数) )for I=1:长度(参数)hparameters=parameters；hparameters(i)=hplus(i )HOLDER，indiv like=full _ bekk _ mv GARCH _ likelihood (参数，数据，p，q，k，k，t )；likelihoodsplus(:i)=indivlike；结束for I=1:长度(参数)hparameters=parameters；具有hpsarameters(i)=h )HOLDER，indiv like=full _ bekk _ mv GARCH _ likelihood (参数，数据，p，q，k，k，t