Initial Parameter Vector 设定方法如下:
1.作者数据,作者模型
E-views --GARCH(1,1)_Student_T
GARCH = C(1) + C(2)*RESID(-1)^2 + C(3)*GARCH(-1)
Coefficient Std. Error z-Statistic Prob.
Variance Equation
C 0.037810 0.021430 1.764307 0.0777
RESID(-1)^2 0.159378 0.037311 4.271560 0.0000
GARCH(-1) 0.844309 0.025869 32.63757 0.0000
T-DIST. DOF 8.481227 3.553939 2.386430 0.0170
nu=8.481227
df0=-log(27.9/(nu-2.1)-1) %
-1.2156
% Initial Parameter Vector %
b = [.03781 .159378 0.844309
-1.2156 0 0 0 0 0]';
%%%%%%%%
2.你的数据,作者模型
E-views --GARCH(1,1)_Student_T
GARCH = C(1) + C(2)*RESID(-1)^2 + C(3)*GARCH(-1)
Coefficient Std. Error z-Statistic Prob.
Variance Equation
C 0.024609 0.013561 1.814703 0.0696
RESID(-1)^2 0.052365 0.011133 4.703726 0.0000
GARCH(-1) 0.943877 0.011440 82.50787 0.0000
T-DIST. DOF 6.547595 1.116192 5.866012 0.0000
nu=6.547595
df0=-log(27.9/(nu-2.1)-1) % -1.6626
% Initial Parameter Vector %
b = [ 0.024609 0.052365 0.943877 -1.6626 0 0 0 0 0]';
%%%%%%%%
Conditional Mean:
Intercept 0.000000
Garch-M 0.000000
AR lag 0.000000
Squared 0.000000
Conditional Variance:
Arch lags 1.000000
I-Garch 0.000000
Conditional Density:
Student T 1.000000
Lags on residual 1.000000
Lags on squares 1.000000
Skew 1.000000
Lags on residual 1.000000
Lags on squares 1.000000
Estimation:
Conditional Mean
Conditional Variance
0.029909 0.016546 0.018221 0.246121
0.066397 0.014695 0.017421 0.165095
0.999974 0.005852 0.006164 0.221160
Conditional Degrees of Freedom
-1.309376 0.509378 0.739532 0.371092
-1.529608 0.691841 1.031085 0.135354
0.334064 0.201206 0.292686 0.114695
Conditional Skewness
-0.285848 0.080211 0.077108 0.480933
0.025642 0.031978 0.027545 0.151919
-0.004019 0.007499 0.005749 0.122430
Log Likelihood 2950.614378
First four moments of standardized residuals
0.060676 0.025318
0.933738 0.046945
-0.086055 0.171954
4.069604 0.710936
Kolmogorov-Smirnov 1.631745
Cramer-Von Mises 0.667755
Joint 1.874494
var 0.529127
Tail 0.828839
%%%%%%%%%
3. 你的数据,你的模型
E-views --GARCH(2,1)M_Student_T
GARCH = C(6) + C(7)*RESID(-1)^2 + C(8)*RESID(-2)^2 + C(9)*GARCH(-1)
Coefficient Std. Error z-Statistic Prob.
@SQRT(GARCH) 0.001192 0.079046 0.015074 0.9880
C 0.176302 0.135419 1.301906 0.1929
AR(1) 0.025454 0.025894 0.983038 0.3256
AR(2) -0.008270 0.025685 -0.321987 0.7475
AR(3) 0.063738 0.026705 2.386699 0.0170
Variance Equation
C 0.040312 0.017611 2.289053 0.0221
RESID(-1)^2 0.048423 0.030978 1.563175 0.1180
RESID(-2)^2 0.016806 0.032995 0.509362 0.6105
GARCH(-1) 0.928778 0.014169 65.54782 0.0000
T-DIST. DOF 5.836450 0.943289 6.187341 0.0000
nu=5.836450
df0=-log(27.9/(nu-2.1)-1) % -1.8667
% Initial Parameter Vector %
b = [0.176302 0.025454 -0.008270 0.063738 0.001192 0.040312 0.048423 0.016806 0.928778 -1.8667 0 0 0 0 0]'
%%%%%%%
Conditional Mean:
Intercept 1.000000
Garch-M 1.000000
AR lag 1.000000
AR lag2 1.000000
AR lag3 1.000000
Squared 0.000000
Conditional Variance:
Arch lags 1.000000
Arch lags 2.000000
I-Garch 0.000000
Conditional Density:
Student T 1.000000
Lags on residual 1.000000
Lags on squares 1.000000
Skew 1.000000
Lags on residual 1.000000
Lags on squares 1.000000
Estimation:
Conditional Mean
0.102692 0.150343 0.285798 0.420348
0.003032 0.083964 0.151691 0.421528
0.040369 0.030235 0.055271 0.022154
0.053009 0.026496 0.043162 0.054511
0.037182 0.026116 0.031336 0.194621
Conditional Variance
0.040767 0.017821 0.020473 0.180501
0.079541 0.039459 0.077074 0.189339
-0.012050 0.039902 0.065092 0.141730
0.995538 0.006492 0.007094 0.204512
Conditional Degrees of Freedom
-1.864213 0.310374 0.516292 0.219712
-0.324064 0.642873 2.856104 0.117623
0.018633 0.127144 0.573926 0.081345
Conditional Skewness
-0.186658 0.089469 0.127297 0.569725
0.057015 0.035866 0.045282 0.156971
-0.003773 0.007105 0.013044 0.103431
Log Likelihood 2942.873827
First four moments of standardized residuals
-0.007357 0.025855
0.970043 0.049859
-0.267714 0.185596
4.547998 0.758344
Kolmogorov-Smirnov 0.793431
Cramer-Von Mises 0.098775
Joint 2.149224
Mean 0.887511
var 0.652404
Tail 0.306765
newmodel.m
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