[问答] 求助：向量BEKK－GARCH（1，1）模型参数极大似然估计BHHH算法的程序 [推广有奖]

11楼

hanceland 发表于 2009-5-2 23:56:00

用RATS 7.0做最快。或者你有OXMETRICS 5.0，用GARCH模块做。

12楼

woxiaoyue 发表于 2010-1-7 22:55:29

兄弟，BEKK－GARCH（1，1）模型参数极大似然估计BHHH算法在EViews中，根本不需要编程，直接打勾选上就OK了。

13楼

xubin9676 发表于 2010-1-21 22:19:56

好的东西，谢谢

14楼

fogsnow 发表于 2010-7-7 18:59:03

http://forums.eviews.com/viewtopic.php?f=4&t=1144
Bivariate GARCH-BEKK estimation

'change path to program path
%path = @runpath
cd %path

' load workfile
load data.wf1

' dependent variables of both series must be continues
smpl @all
series y1 = dlog(aa)
series y2 = dlog(bb)

' set sample
' first observation of s1 need to be one or two periods after
' the first observation of s0
sample s0 01/01/1990 11/01/2008
sample s1 03/01/1990 11/01/2008

' initialization of parameters and starting values
' change below only to change the specification of model
smpl s0

'get starting values from univariate GARCH
equation eq1.arch(m=100,c=1e-5) y1 c
equation eq2.arch(m=100,c=1e-5) y2 c

' declare coef vectors to use in bi-variate GARCH model
' see above for details
coef(2) mu
mu(1) = eq1.c(1)
mu(2)= eq2.c(1)

coef(3) omega
omega(1)=0.5
omega(2)=0
omega(3)=0.5

coef(2) alpha
alpha(1) = 0.5
alpha(2) = 0.5

coef(2) beta
beta(1)= 0.5
beta(2)= 0.5

' constant adjustment for log likelihood
!mlog2pi = 2*log(2*@acos(-1))

' use var-cov of sample in "s1" as starting value of variance-covariance matrix
series cov_y1y2 = @cov(y1-mu(1), y2-mu(2))
series var_y1 = @var(y1)
series var_y2 = @var(y2)

series sqres1 = (y1-mu(1))^2
series sqres2 = (y2-mu(2))^2
series res1res2 = (y1-mu(1))*(y2-mu(2))

' ...........................................................
' LOG LIKELIHOOD
' set up the likelihood
' 1) open a new blank likelihood object (L.O.) name bvgarch
' 2) specify the log likelihood model by append
' ...........................................................

logl bvgarch
bvgarch.append @logl logl
bvgarch.append sqres1 = (y1-mu(1))^2
bvgarch.append sqres2 = (y2-mu(2))^2
bvgarch.append res1res2 = (y1-mu(1))*(y2-mu(2))

' calculate the variance and covariance series
bvgarch.append var_y1 = omega(1)^2 + beta(1)^2*var_y1(-1) + alpha(1)^2*sqres1(-1)
bvgarch.append var_y2 = omega(3)^2+omega(2)^2 + beta(2)^2*var_y2(-1) + alpha(2)^2*sqres2(-1)
bvgarch.append cov_y1y2 = omega(1)*omega(2) + beta(2)*beta(1)*cov_y1y2(-1) + alpha(2)*alpha(1)*res1res2(-1)

' determinant of the variance-covariance matrix
bvgarch.append deth = var_y1*var_y2 - cov_y1y2^2

' inverse elements of the variance-covariance matrix
bvgarch.append invh1 = var_y2/deth
bvgarch.append invh3 = var_y1/deth
bvgarch.append invh2 = -cov_y1y2/deth

' log-likelihood series
bvgarch.append logl =-0.5*(!mlog2pi + (invh1*sqres1+2*invh2*res1res2+invh3*sqres2) + log(deth))

' remove some of the intermediary series
' bvgarch.append @temp invh1 invh2 invh3 sqres1 sqres2 res1res2 deth

' estimate the model
smpl s1
bvgarch.ml(showopts, m=100, c=1e-5)

' change below to display different output
show bvgarch.output
graph varcov.line var_y1 var_y2 cov_y1y2
show varcov

' LR statistic for univariate versus bivariate model
scalar lr = -2*( eq1.@logl + eq2.@logl - bvgarch.@logl )
scalar lr_pval = 1 - @cchisq(lr,1)

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15楼

wljine 发表于 2013-8-14 18:08:33

hanceland 发表于 2009-5-2 23:56
用RATS 7.0做最快。或者你有OXMETRICS 5.0，用GARCH模块做。

老师，您好，请问在rats中如何实现bekk——garch的极大似然估计，非常感谢，wljine@gmail.com

16楼

chenxiao403 发表于 2013-8-18 11:38:10

我尝试了一下貌似跟单独做GARCH有点区别，建立纳斯达克日收益率（Rnsdk），和上证日收益率序列（RSZZS），在EVIEWS主界面点击Object-New object-System，第一行输入Rnsdk=C（1），第二行输入 Rszzs=C(2),再点击系统框上的Proc-Etimate 选择ARCH-CONDITION什么什么，后面有许多框框可以选择的，选择你需要的方法进行估计，但是做出来的结果跟你单独用这两个序列做GARCH的有微小的区别，比如系数可能在0.00003-5之间波动，概率也是。

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