如何画DCC_MVGARCH 中动态相关图

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

请问，当我们在做完DCCGARCH估计后，得到一些估计结果，但在看别人的文章时，都有画一张动态相关图，请问这个动态相关图是如何画出来的？其是指估计结果的哪一部分？是指下面的scores = The estimated scores of the likelihood t by length(parameters)   吗？还是指其他的？


  OUTPUTS:
       parameters    = A vector of parameters estimated form the model of the form
                           [GarchParams(1) GarchParams(2) ... GarchParams(k) DCCParams]
                           where the garch parameters from each estimation are of the form
                           [omega(i) alpha(i1) alpha(i2) ... alpha(ip(i)) beta(i1) beta(i2) ... beta(iq(i))]
       loglikelihood = The log likelihood evaluated at the optimum
       Ht            = A k by k by t array of conditional variances
       Qt            = A k by k by t array of Qt elements
       likelihoods   = the estimated likelihoods t by 1
       stderrors     = A length(parameters)^2 matrix of estimated correct standard errors
       A             = The estimated A form the rebust standard errors
       B             = The estimated B from the standard errors
       scores        = The estimated scores of the likelihood t by length(parameters)

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏4 回帖

关键词：mvgarch VGARCH GARCH 动态相关图 ARCH 动态 DCC mvgarch

DCCGARCH  过时了   线性相关系数 有太大局限性    用动态copula模型

[parameters, loglikelihood, Ht,...]=dcc_mvgarch(r,1,1,1,1);
var1 = squeeze(Ht(1,1,:));  %  column vector of variances
var2 = squeeze(Ht(2,2,:));  %  column vector of variances
cov12 = squeeze(Ht(1,2,:)); %  column vector of covariances
corr12 = cov12./sqrt(var1.*var2);

[DCC_parameters,DCC_LL,DCC_Ht]=dcc_mvgarch(data,1,1,1,1);
Estimating GARCH model for Series 1
Warning: Options LargeScale = 'off' and Algorithm = 'trust-region-reflective' conflict.
Ignoring Algorithm and running active-set method. To run trust-region-reflective, set
LargeScale = 'on'. To run active-set without this warning, use Algorithm = 'active-set'.
> In fmincon at 412
  In fattailed_garch at 198
  In dcc_mvgarch at 82
Estimating GARCH model for Series 2
Warning: Options LargeScale = 'off' and Algorithm = 'trust-region-reflective' conflict.
Ignoring Algorithm and running active-set method. To run trust-region-reflective, set
LargeScale = 'on'. To run active-set without this warning, use Algorithm = 'active-set'.
> In fmincon at 412
  In fattailed_garch at 198
  In dcc_mvgarch at 82


Estimating the DCC model
Warning: Options LargeScale = 'off' and Algorithm = 'trust-region-reflective' conflict.
Ignoring Algorithm and running active-set method. To run trust-region-reflective, set
LargeScale = 'on'. To run active-set without this warning, use Algorithm = 'active-set'.
> In fmincon at 412
  In dcc_mvgarch at 98

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   Diagnostic Information

Number of variables: 2

Functions
Objective:                            dcc_mvgarch_likelihood
Gradient:                             finite-differencing
Hessian:                              finite-differencing (or Quasi-Newton)

Constraints
Nonlinear constraints:             do not exist

Number of linear inequality constraints:    1
Number of linear equality constraints:      0
Number of lower bound constraints:          2
Number of upper bound constraints:          0

Algorithm selected
   medium-scale: SQP, Quasi-Newton, line-search


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
End diagnostic information


                                Max     Line search  Directional  First-order
Iter F-count        f(x)   constraint   steplength   derivative   optimality Procedure
    0      3       2092.4    -0.009998                                         
    1     11      2088.75     -0.01937       0.0313         -702    1.76e+003   
    2     14       2088.6            0            1          -75    1.67e+003   
    3     18      2081.78     -0.00467          0.5         -771          599   
    4     22      2081.24    -0.002335          0.5         -123    1.37e+003   
    5     25      2080.53   -1.11e-016            1   -1.89e+003     1.6e+003   
    6     36      2080.53   -0.0006755      0.00391         -107          686   
    7     39      2080.11   -0.0007109            1         -202           99   
    8     42      2080.11   -0.0007696            1        -55.3         8.15   
    9     45      2080.11   -0.0007977            1        -7.97         1.19   
   10     48      2080.11   -0.0007944            1        -1.02       0.0392   

Local minimum possible. Constraints satisfied.

fmincon stopped because the size of the current search direction is less than
twice the default value of the step size tolerance and constraints were
satisfied to within the selected value of the constraint tolerance.

<stopping criteria details>

No active inequalities.




从估计的过程来看，dcc——mvgarch命令是不需要做单变量garch估计的吧，因为从上面的结果来看该命令首先做了一个garch模型，然后再做dcc估计的，不知道是不是这样的？做过的来讨论一下！谢谢！