[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.
你好 最近我也在做这个，问个菜鸟问题，我是在网上下的ucsd_garch工具包里面有个dcc_mvgarch的m文件，输入一个500×2的矩阵，数据是上证指数和一只个股的对数收益率，有500期。
但是结果出错，我也不知道是错在哪里了，请教一下,输出结果如下:
dcc_mvgarch(B,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 algorithm. To run trust-region-reflective, set
LargeScale = 'on'. To run active-set without this warning, use Algorithm = 'active-set'.
> In fmincon at 445
In fattailed_garch at 198
In dcc_mvgarch at 82
??? Invalid MEX-file 'E:\MATLAB\R2010b\toolbox\ucsd_garch\5.3 win32 binaries\garchcore.dll': E:\MATLAB\R2010b\toolbox\ucsd_garch\5.3
win32 binaries\garchcore.dll 不是有效的 Win32 应用程序。
.
Error in ==> fattailed_garchlikelihood at 61
h=garchcore(data,parameters,stdEstimate^2,p,q,m,T);
Error in ==> fmincon at 574
initVals.f = feval(funfcn{3},X,varargin{:});
Error in ==> fattailed_garch at 198
[parameters, LLF, EXITFLAG, OUTPUT, LAMBDA, GRAD] = fmincon('fattailed_garchlikelihood', startingvals ,sumA , sumB ,[] , [] , LB ,
UB,[],options, data, p , q, errortype, stdEstimate^2, T);
Error in ==> dcc_mvgarch at 82
[univariate{i}.parameters, univariate{i}.likelihood, univariate{i}.stderrors, univariate{i}.robustSE, univariate{i}.ht,
univariate{i}.scores] ...
Caused by:
Failure in initial user-supplied objective function evaluation. FMINCON cannot continue.
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