Таблица № 3.7
Статистика при построении ARMA и GARCH-моделей выборки №2
|
Dependent Variable: Y |
|||||
|
Method: ML - ARCH (Marquardt) - Normal distribution |
|||||
|
Date: 03/30/19 Time: 20:07 |
|||||
|
Sample (adjusted): 11 500 |
|||||
|
Included observations: 490 after adjustments |
|||||
|
Convergence achieved after 23 iterations |
|||||
|
MA Backcast: 1 10 |
|||||
|
Presample variance: backcast (parameter = 0.7) |
|||||
|
GARCH = C(4) + C(5)*RESID(-1)^2 + C(6)*GARCH(-1) |
|||||
|
Variable |
Coefficient |
Std. Error |
z-Statistic |
Prob. |
|
|
AR(10) |
-0.897740 |
0.011859 |
-75.70246 |
0.0000 |
|
|
MA(5) |
0.044114 |
0.010391 |
4.245197 |
0.0000 |
|
|
MA(10) |
0.961122 |
0.008188 |
117.3852 |
0.0000 |
|
|
Variance Equation |
|||||
|
C |
2.80E-05 |
8.12E-06 |
3.448743 |
0.0006 |
|
|
RESID(-1)^2 |
0.166957 |
0.022458 |
7.434321 |
0.0000 |
|
|
GARCH(-1) |
0.836913 |
0.015817 |
52.91234 |
0.0000 |
|
|
R-squared |
0.042983 |
Mean dependent var |
0.004982 |
||
|
Adjusted R-squared |
0.039053 |
S.D. dependent var |
0.037723 |
||
|
S.E. of regression |
0.036979 |
Akaike info criterion |
-3.933614 |
||
|
Sum squared resid |
0.665956 |
Schwarz criterion |
-3.882254 |
||
|
Log likelihood |
969.7353 |
Hannan-Quinn criter. |
-3.913443 |
||
|
Durbin-Watson stat |
1.881436 |
Модель ARMA(10;5,10) для выборки №2 имеет вид (3.6):
Модель GARCH(1;1) для выборки №2 имеет вид (3.7):
Рисунок 3.2 Волатильность доходности выборки №2 в %-годовых
Построение GARCH-модели для выборки №3.
Таблица № 3.8
Проверка на стационарность выборки №3 (тест Дики-Фуллера)
|
Null Hypothesis: Y has a unit root |
|||||
|
Exogenous: Constant |
|||||
|
Lag Length: 0 (Automatic - based on SIC, maxlag=16) |
|||||
|
t-Statistic |
Prob.* |
||||
|
Augmented Dickey-Fuller test statistic |
-18.19135 |
0.0000 |
|||
|
Test critical values: |
1% level |
-3.448889 |
|||
|
5% level |
-2.869605 |
||||
|
10% level |
-2.571135 |
||||
|
*MacKinnon (1996) one-sided p-values. |
|||||
|
Augmented Dickey-Fuller Test Equation |
|||||
|
Dependent Variable: D(Y) |
|||||
|
Method: Least Squares |
|||||
|
Date: 04/06/19 Time: 18:17 |
|||||
|
Sample (adjusted): 2 350 |
|||||
|
Included observations: 349 after adjustments |
|||||
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
|
Y(-1) |
-0.975100 |
0.053602 |
-18.19135 |
0.0000 |
|
|
C |
0.000919 |
0.002777 |
0.330783 |
0.7410 |
|
|
R-squared |
0.488144 |
Mean dependent var |
-0.000255 |
||
|
Adjusted R-squared |
0.486669 |
S.D. dependent var |
0.072400 |
||
|
S.E. of regression |
0.051873 |
Akaike info criterion |
-3.074341 |
||
|
Sum squared resid |
0.933693 |
Schwarz criterion |
-3.052249 |
||
|
Log likelihood |
538.4725 |
Hannan-Quinn criter. |
-3.065547 |
||
|
F-statistic |
330.9251 |
Durbin-Watson stat |
1.997986 |
||
|
Prob(F-statistic) |
0.000000 |
Наблюдаемая статистика Дики-Фуллера меньше критических значений, что свидетельствует о стационарности ряда данных.
Таблица № 3.9 Коррелограмма доходностей выборки №3
|
Date: 04/06/19 Time: 18:18 |
|||||||
|
Sample: 1 350 |
|||||||
|
Included observations: 350 |
|||||||
|
Autocorrelation |
Partial Correlation |
AC |
PAC |
Q-Stat |
Prob |
||
|
.|. | |
.|. | |
1 |
0.025 |
0.025 |
0.2183 |
0.640 |
|
|
.|. | |
.|. | |
2 |
0.051 |
0.050 |
1.1322 |
0.568 |
|
|
.|* | |
.|* | |
3 |
0.088 |
0.086 |
3.8651 |
0.276 |
|
|
.|. | |
.|. | |
4 |
0.068 |
0.063 |
5.5332 |
0.237 |
|
|
.|. | |
.|. | |
5 |
0.035 |
0.025 |
5.9808 |
0.308 |
|
|
.|. | |
.|. | |
6 |
0.037 |
0.023 |
6.4805 |
0.372 |
|
|
.|. | |
.|. | |
7 |
0.049 |
0.035 |
7.3484 |
0.394 |
|
|
.|. | |
.|. | |
8 |
0.036 |
0.023 |
7.8155 |
0.452 |
|
|
.|. | |
.|. | |
9 |
-0.021 |
-0.034 |
7.9702 |
0.537 |
|
|
.|. | |
.|. | |
10 |
0.005 |
-0.008 |
7.9796 |
0.631 |
|
|
.|. | |
.|. | |
11 |
-0.009 |
-0.018 |
8.0073 |
0.713 |
|
|
.|* | |
.|* | |
12 |
0.075 |
0.075 |
10.069 |
0.610 |
|
|
.|. | |
.|. | |
13 |
0.060 |
0.060 |
11.376 |
0.579 |
|
|
.|* | |
.|. | |
14 |
0.074 |
0.070 |
13.408 |
0.495 |
|
|
.|. | |
.|. | |
15 |
0.018 |
0.001 |
13.529 |
0.561 |
|
|
.|. | |
.|. | |
16 |
0.008 |
-0.015 |
13.552 |
0.632 |
|
|
*|. | |
*|. | |
17 |
-0.108 |
-0.134 |
17.899 |
0.395 |
|
|
.|. | |
.|. | |
18 |
0.002 |
-0.015 |
17.901 |
0.462 |
|
|
*|. | |
*|. | |
19 |
-0.071 |
-0.079 |
19.784 |
0.408 |
|
|
.|. | |
.|. | |
20 |
-0.024 |
-0.017 |
19.996 |
0.458 |
|
|
.|. | |
.|. | |
21 |
-0.013 |
0.004 |
20.064 |
0.517 |
|
|
.|. | |
.|. | |
22 |
-0.029 |
-0.005 |
20.375 |
0.560 |
|
|
.|. | |
.|. | |
23 |
0.017 |
0.050 |
20.485 |
0.613 |
|
|
.|. | |
.|. | |
24 |
-0.047 |
-0.030 |
21.335 |
0.619 |
|
|
.|. | |
.|. | |
25 |
-0.009 |
-0.002 |
21.368 |
0.672 |
|
|
.|. | |
.|. | |
26 |
0.021 |
0.005 |
21.530 |
0.714 |
|
|
.|. | |
.|. | |
27 |
0.048 |
0.046 |
22.411 |
0.716 |
|
|
.|. | |
*|. | |
28 |
-0.051 |
-0.067 |
23.417 |
0.712 |
|
|
.|. | |
.|. | |
29 |
0.016 |
0.029 |
23.517 |
0.752 |
|
|
.|. | |
.|. | |
30 |
0.046 |
0.055 |
24.317 |
0.758 |
|
|
*|. | |
.|. | |
31 |
-0.073 |
-0.044 |
26.373 |
0.703 |
|
|
.|. | |
.|. | |
32 |
-0.020 |
-0.002 |
26.524 |
0.740 |
|
|
.|. | |
.|. | |
33 |
0.021 |
0.028 |
26.691 |
0.773 |
|
|
.|. | |
.|. | |
34 |
-0.019 |
-0.024 |
26.830 |
0.804 |
|
|
.|. | |
.|. | |
35 |
-0.015 |
-0.013 |
26.919 |
0.834 |
|
|
.|. | |
.|. | |
36 |
-0.012 |
-0.022 |
26.979 |
0.862 |
Таблица № 3.10
Статистика при построении ARMA и GARCH-моделей выборки №3
|
Dependent Variable: Y |
|||||
|
Method: ML - ARCH (Marquardt) - Normal distribution |
|||||
|
Date: 03/30/19 Time: 20:35 |
|||||
|
Sample (adjusted): 20 350 |
|||||
|
Included observations: 331 after adjustments |
|||||
|
Convergence achieved after 26 iterations |
|||||
|
MA Backcast: 1 19 |
|||||
|
Presample variance: backcast (parameter = 0.7) |
|||||
|
GARCH = C(4) + C(5)*RESID(-1)^2 + C(6)*GARCH(-1) |
|||||
|
Variable |
Coefficient |
Std. Error |
z-Statistic |
Prob. |
|
|
C |
-0.003629 |
0.001327 |
-2.735792 |
0.0062 |
|
|
AR(19) |
0.704550 |
0.028519 |
24.70423 |
0.0000 |
|
|
MA(19) |
-0.960948 |
0.005736 |
-167.5249 |
0.0000 |
|
|
Variance Equation |
|||||
|
C |
5.76E-05 |
2.06E-05 |
2.790609 |
0.0053 |
|
|
RESID(-1)^2 |
0.107908 |
0.026869 |
4.016152 |
0.0001 |
|
|
GARCH(-1) |
0.870394 |
0.029249 |
29.75811 |
0.0000 |
|
|
R-squared |
0.160120 |
Mean dependent var |
8.01E-05 |
||
|
Adjusted R-squared |
0.154998 |
S.D. dependent var |
0.052136 |
||
|
S.E. of regression |
0.047926 |
Akaike info criterion |
-3.383351 |
||
|
Sum squared resid |
0.753369 |
Schwarz criterion |
-3.314430 |
||
|
Log likelihood |
565.9446 |
Hannan-Quinn criter. |
-3.355862 |
||
|
Durbin-Watson stat |
2.016732 |
Модель ARMA(19;19) для выборки №3 имеет вид (3.8):
Модель GARCH(1;1) для выборки №3 имеет вид (3.9):
Рисунок 3.3 Волатильность доходности выборки №3 в %-годовых
Построение GARCH-модели для выборки №4.
Таблица № 3.11
Проверка на стационарность выборки №4 (тест Дики-Фуллера)
|
Null Hypothesis: Y has a unit root |
|||||
|
Exogenous: Constant |
|||||
|
Lag Length: 0 (Automatic - based on SIC, maxlag=17) |
|||||
|
t-Statistic |
Prob.* |
||||
|
Augmented Dickey-Fuller test statistic |
-19.19165 |
0.0000 |
|||
|
Test critical values: |
1% level |
-3.443228 |
|||
|
5% level |
-2.867112 |
||||
|
10% level |
-2.569800 |
||||
|
*MacKinnon (1996) one-sided p-values. |
|||||
|
Augmented Dickey-Fuller Test Equation |
|||||
|
Dependent Variable: D(Y) |
|||||
|
Method: Least Squares |
|||||
|
Date: 05/05/19 Time: 16:49 |
|||||
|
Sample (adjusted): 2 500 |
|||||
|
Included observations: 499 after adjustments |
|||||
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
|
Y(-1) |
-0.850862 |
0.044335 |
-19.19165 |
0.0000 |
|
|
C |
0.000346 |
0.000273 |
1.269491 |
0.2049 |
|
|
R-squared |
0.425646 |
Mean dependent var |
-4.30E-06 |
||
|
Adjusted R-squared |
0.424490 |
S.D. dependent var |
0.008017 |
||
|
S.E. of regression |
0.006082 |
Akaike info criterion |
-7.363047 |
||
|
Sum squared resid |
0.018383 |
Schwarz criterion |
-7.346163 |
||
|
Log likelihood |
1839.080 |
Hannan-Quinn criter. |
-7.356421 |
||
|
F-statistic |
368.3195 |
Durbin-Watson stat |
2.008223 |
||
|
Prob(F-statistic) |
0.000000 |
Наблюдаемая статистика Дики-Фуллера меньше критических значений, что свидетельствует о стационарности ряда данных.
Таблица № 3.12 Коррелограмма доходностей выборки №4
|
Date: 05/06/19 Time: 17:04 |
|||||||
|
Sample: 1 500 |
|||||||
|
Included observations: 500 |
|||||||
|
Autocorrelation |
Partial Correlation |
AC |
PAC |
Q-Stat |
Prob |
||
|
.|* | |
.|* | |
1 |
0.149 |
0.149 |
11.180 |
0.001 |
|
|
.|. | |
.|. | |
2 |
0.074 |
0.053 |
13.912 |
0.001 |
|
|
.|. | |
.|. | |
3 |
-0.001 |
-0.020 |
13.912 |
0.003 |
|
|
.|. | |
.|. | |
4 |
-0.058 |
-0.061 |
15.614 |
0.004 |
|
|
.|. | |
.|. | |
5 |
-0.015 |
0.003 |
15.730 |
0.008 |
|
|
.|. | |
.|. | |
6 |
-0.002 |
0.008 |
15.731 |
0.015 |
|
|
.|. | |
.|. | |
7 |
0.002 |
0.001 |
15.734 |
0.028 |
|
|
.|. | |
.|. | |
8 |
0.021 |
0.017 |
15.955 |
0.043 |
|
|
*|. | |
*|. | |
9 |
-0.096 |
-0.105 |
20.642 |
0.014 |
|
|
*|. | |
.|. | |
10 |
-0.067 |
-0.043 |
22.948 |
0.011 |
|
|
.|. | |
.|. | |
11 |
-0.034 |
-0.005 |
23.547 |
0.015 |
|
|
*|. | |
*|. | |
12 |
-0.122 |
-0.112 |
31.220 |
0.002 |
|
|
.|. | |
.|. | |
13 |
-0.057 |
-0.036 |
32.882 |
0.002 |
|
|
.|. | |
.|. | |
14 |
-0.030 |
-0.012 |
33.361 |
0.003 |
|
|
.|. | |
.|. | |
15 |
-0.014 |
-0.008 |
33.462 |
0.004 |
|
|
.|. | |
.|. | |
16 |
0.057 |
0.051 |
35.141 |
0.004 |
|
|
.|. | |
.|. | |
17 |
0.022 |
0.007 |
35.397 |
0.006 |
|
|
.|. | |
.|. | |
18 |
-0.017 |
-0.040 |
35.551 |
0.008 |
|
|
.|. | |
.|. | |
19 |
-0.042 |
-0.048 |
36.457 |
0.009 |
|
|
.|. | |
.|. | |
20 |
-0.029 |
-0.007 |
36.900 |
0.012 |
|
|
.|. | |
.|. | |
21 |
-0.017 |
-0.026 |
37.047 |
0.017 |
|
|
.|. | |
.|. | |
22 |
-0.028 |
-0.046 |
37.445 |
0.021 |
|
|
.|. | |
.|. | |
23 |
0.014 |
0.010 |
37.551 |
0.028 |
|
|
.|. | |
.|. | |
24 |
-0.014 |
-0.039 |
37.651 |
0.038 |
|
|
.|. | |
.|. | |
25 |
-0.052 |
-0.056 |
39.080 |
0.036 |
|
|
.|. | |
.|. | |
26 |
0.006 |
0.024 |
39.100 |
0.048 |
|
|
.|. | |
*|. | |
27 |
-0.059 |
-0.067 |
40.919 |
0.042 |
|
|
.|. | |
.|. | |
28 |
-0.054 |
-0.049 |
42.458 |
0.039 |
|
|
.|. | |
.|. | |
29 |
-0.010 |
0.004 |
42.513 |
0.050 |
|
|
*|. | |
*|. | |
30 |
-0.077 |
-0.086 |
45.646 |
0.034 |
|
|
.|. | |
.|. | |
31 |
0.025 |
0.017 |
45.973 |
0.041 |
|
|
.|. | |
*|. | |
32 |
-0.065 |
-0.088 |
48.269 |
0.032 |
|
|
.|. | |
.|. | |
33 |
-0.032 |
-0.038 |
48.813 |
0.037 |
|
|
.|. | |
.|. | |
34 |
0.020 |
0.003 |
49.024 |
0.046 |
|
|
.|. | |
.|. | |
35 |
-0.041 |
-0.047 |
49.951 |
0.049 |
|
|
.|. | |
.|. | |
36 |
-0.032 |
-0.053 |
50.505 |
0.055 |
Таблица № 3.13
Статистика при построении ARMA и GARCH-моделей выборки №4
|
Dependent Variable: Y |
|||||
|
Method: ML - ARCH |
|||||
|
Date: 03/30/19 Time: 18:54 |
|||||
|
Sample (adjusted): 13 500 |
|||||
|
Included observations: 488 after adjustments |
|||||
|
Convergence achieved after 42 iterations |
|||||
|
MA Backcast: 4 12 |
|||||
|
Presample variance: backcast (parameter = 0.7) |
|||||
|
GARCH = C(9) + C(10)*RESID(-1)^2 + C(11)*GARCH(-1) |
|||||
|
Variable |
Coefficient |
Std. Error |
z-Statistic |
Prob. |
|
|
C |
0.000519 |
0.000109 |
4.766359 |
0.0000 |
|
|
AR(1) |
0.581371 |
0.037356 |
15.56294 |
0.0000 |
|
|
AR(4) |
-0.197749 |
0.053961 |
-3.664675 |
0.0002 |
|
|
AR(9) |
0.335508 |
0.058027 |
5.781955 |
0.0000 |
|
|
AR(12) |
-0.104271 |
0.029735 |
-3.506626 |
0.0005 |
|
|
MA(1) |
-0.497902 |
0.026095 |
-19.08003 |
0.0000 |
|
|
MA(4) |
0.150315 |
0.036092 |
4.164781 |
0.0000 |
|
|
MA(9) |
-0.524412 |
0.040045 |
-13.09570 |
0.0000 |
|
|
Variance Equation |
|||||
|
C |
3.74E-06 |
1.58E-06 |
2.358862 |
0.0183 |
|
|
RESID(-1)^2 |
0.103658 |
0.037832 |
2.739943 |
0.0061 |
|
|
GARCH(-1) |
0.789464 |
0.067419 |
11.70975 |
0.0000 |
|
|
R-squared |
0.086942 |
Mean dependent var |
0.000369 |
||
|
Adjusted R-squared |
0.073626 |
S.D. dependent var |
0.006166 |
||
|
S.E. of regression |
0.005935 |
Akaike info criterion |
-7.434911 |
||
|
Sum squared resid |
0.016906 |
Schwarz criterion |
-7.340457 |
||
|
Log likelihood |
1825.118 |
Hannan-Quinn criter. |
-7.397809 |
||
|
Durbin-Watson stat |
1.932775 |
Модель ARMA(1,4,9,12;1,4,9) для выборки №4 имеет вид (3.10):
| 03. ПФ альтерации |
| 08. Стресс |
| 1-28вопрос -1 |
| 1365 |
| 14 |
| 16 Антианг+ |
| 3767 |
| 4 модуль |
| 5180 |
| 751 |