---------------------------------------------------------------------------------------- . * Osa 1, DCC mudeli hindamine . mgarch dcc (rt = L.rt L.rh L.rn) (rh = L.rt L.rh L.rn) (rn = L.rt L.rh L.rn), arch(1/1) garch(1/1) distribution(t) Calculating starting values.... Optimizing log likelihood (setting technique to bhhh) Iteration 0: Log likelihood = -13186.874 Iteration 1: Log likelihood = -12983.257 Iteration 2: Log likelihood = -12822.383 Iteration 3: Log likelihood = -12773.943 Iteration 4: Log likelihood = -12770.012 Iteration 5: Log likelihood = -12739.433 Iteration 6: Log likelihood = -12735.733 Iteration 7: Log likelihood = -12734.403 Iteration 8: Log likelihood = -12733.855 Iteration 9: Log likelihood = -12733.709 (switching technique to nr) Iteration 10: Log likelihood = -12733.648 Iteration 11: Log likelihood = -12733.607 Iteration 12: Log likelihood = -12733.607 Refining estimates Iteration 0: Log likelihood = -12733.607 Iteration 1: Log likelihood = -12733.607 (backed up) Dynamic conditional correlation MGARCH model Sample: 05jan2012 thru 15mar2023 Number of obs = 2,816 Distribution: t Wald chi2(9) = 29.35 Log likelihood = -12733.61 Prob > chi2 = 0.0006 ------------------------------------------------------------------------------ | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- rt | rt | L1. | -.0001789 .0259538 -0.01 0.994 -.0510473 .0506895 | rh | L1. | -.0408255 .0234572 -1.74 0.082 -.0868007 .0051498 | rn | L1. | .0035264 .0162349 0.22 0.828 -.0282934 .0353462 | _cons | .0232877 .0200994 1.16 0.247 -.0161064 .0626817 -------------+---------------------------------------------------------------- ARCH_rt | arch | L1. | .0515946 .010574 4.88 0.000 .0308699 .0723192 | garch | L1. | .9170586 .0192026 47.76 0.000 .8794223 .9546949 | _cons | .0622094 .0200626 3.10 0.002 .0228875 .1015313 -------------+---------------------------------------------------------------- rh | rt | L1. | -.0105487 .0280614 -0.38 0.707 -.065548 .0444506 | rh | L1. | -.0085852 .0268135 -0.32 0.749 -.0611388 .0439683 | rn | L1. | -.0102419 .0190379 -0.54 0.591 -.0475555 .0270716 | _cons | .0040188 .0227015 0.18 0.859 -.0404754 .0485129 -------------+---------------------------------------------------------------- ARCH_rh | arch | L1. | .0286762 .0055381 5.18 0.000 .0178216 .0395307 | garch | L1. | .9552724 .0092809 102.93 0.000 .9370821 .9734627 | _cons | .0402334 .0119828 3.36 0.001 .0167475 .0637193 -------------+---------------------------------------------------------------- rn | rt | L1. | .0751785 .0298827 2.52 0.012 .0166095 .1337474 | rh | L1. | -.0097894 .0277972 -0.35 0.725 -.0642708 .0446921 | rn | L1. | -.0682447 .0220833 -3.09 0.002 -.1115271 -.0249623 | _cons | .0003751 .0236576 0.02 0.987 -.0459928 .0467431 -------------+---------------------------------------------------------------- ARCH_rn | arch | L1. | .0295239 .0062571 4.72 0.000 .0172602 .0417876 | garch | L1. | .9646682 .0078573 122.77 0.000 .9492683 .9800681 | _cons | .0176425 .0068386 2.58 0.010 .004239 .031046 -------------+---------------------------------------------------------------- corr(rt,rh)| .7508237 .0112829 66.55 0.000 .7287096 .7729377 corr(rt,rn)| .6039091 .0163326 36.98 0.000 .5718979 .6359203 corr(rh,rn)| .6292684 .0155115 40.57 0.000 .5988665 .6596703 -------------+---------------------------------------------------------------- /Adjustment | lambda1 | .0337692 .0075042 4.50 0.000 .0190613 .0484771 lambda2 | .8256672 .0446211 18.50 0.000 .7382115 .913123 -------------+---------------------------------------------------------------- /df | 5.00622 .2720748 18.40 0.000 4.472963 5.539477 ------------------------------------------------------------------------------ . estat ic Akaike's information criterion and Bayesian information criterion ----------------------------------------------------------------------------- Model | N ll(null) ll(model) df AIC BIC -------------+--------------------------------------------------------------- . | 2,816 . -12733.61 27 25521.21 25681.68 ----------------------------------------------------------------------------- Note: BIC uses N = number of observations. See [R] IC note. . estimates store dcc . * Osa 2, VCC mudeli hindamine . mgarch vcc (rt = L.rt L.rh L.rn) (rh = L.rt L.rh L.rn) (rn = L.rt L.rh L.rn), arch(1/1) garch(1/1) distribution(t) Calculating starting values.... Optimizing log likelihood (setting technique to bhhh) Iteration 0: Log likelihood = -13213.438 Iteration 1: Log likelihood = -12968.94 Iteration 2: Log likelihood = -12830.68 Iteration 3: Log likelihood = -12793.445 Iteration 4: Log likelihood = -12778.819 Iteration 5: Log likelihood = -12754.144 Iteration 6: Log likelihood = -12749.798 Iteration 7: Log likelihood = -12747.097 Iteration 8: Log likelihood = -12746.107 Iteration 9: Log likelihood = -12745.827 (switching technique to nr) Iteration 10: Log likelihood = -12745.727 Iteration 11: Log likelihood = -12745.67 Iteration 12: Log likelihood = -12745.669 Refining estimates Iteration 0: Log likelihood = -12745.669 Iteration 1: Log likelihood = -12745.669 (backed up) Varying conditional correlation MGARCH model Sample: 05jan2012 thru 15mar2023 Number of obs = 2,816 Distribution: t Wald chi2(9) = 28.52 Log likelihood = -12745.67 Prob > chi2 = 0.0008 ------------------------------------------------------------------------------ | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- rt | rt | L1. | -.0047238 .025988 -0.18 0.856 -.0556593 .0462118 | rh | L1. | -.0308887 .023064 -1.34 0.180 -.0760933 .014316 | rn | L1. | -.0024407 .0162331 -0.15 0.880 -.0342569 .0293755 | _cons | .02273 .0200214 1.14 0.256 -.0165112 .0619712 -------------+---------------------------------------------------------------- ARCH_rt | arch | L1. | .0424905 .0088147 4.82 0.000 .025214 .0597669 | garch | L1. | .9379268 .0141077 66.48 0.000 .9102762 .9655774 | _cons | .039777 .0133086 2.99 0.003 .0136925 .0658614 -------------+---------------------------------------------------------------- rh | rt | L1. | -.0144767 .028211 -0.51 0.608 -.0697693 .0408159 | rh | L1. | -.0035458 .0264957 -0.13 0.894 -.0554765 .0483848 | rn | L1. | -.0139025 .0191669 -0.73 0.468 -.0514688 .0236639 | _cons | .0070461 .0226172 0.31 0.755 -.0372828 .0513751 -------------+---------------------------------------------------------------- ARCH_rh | arch | L1. | .0264845 .0050028 5.29 0.000 .0166792 .0362897 | garch | L1. | .9623077 .0076412 125.94 0.000 .9473313 .9772841 | _cons | .0291065 .0092394 3.15 0.002 .0109977 .0472153 -------------+---------------------------------------------------------------- rn | rt | L1. | .0685037 .0300706 2.28 0.023 .0095664 .127441 | rh | L1. | .0005105 .0274565 0.02 0.985 -.0533032 .0543242 | rn | L1. | -.074197 .0222373 -3.34 0.001 -.1177813 -.0306127 | _cons | -.0016536 .0236246 -0.07 0.944 -.0479569 .0446498 -------------+---------------------------------------------------------------- ARCH_rn | arch | L1. | .0287844 .0059398 4.85 0.000 .0171426 .0404263 | garch | L1. | .9668699 .0071186 135.82 0.000 .9529177 .9808221 | _cons | .0141294 .0058535 2.41 0.016 .0026568 .0256019 -------------+---------------------------------------------------------------- corr(rt,rh)| .7775033 .0161678 48.09 0.000 .745815 .8091916 corr(rt,rn)| .6343487 .0220521 28.77 0.000 .5911273 .6775701 corr(rh,rn)| .6613006 .0210079 31.48 0.000 .6201259 .7024754 -------------+---------------------------------------------------------------- /Adjustment | lambda1 | .0058667 .0019234 3.05 0.002 .0020968 .0096365 lambda2 | .980098 .0075811 129.28 0.000 .9652393 .9949566 -------------+---------------------------------------------------------------- /df | 4.978243 .2688895 18.51 0.000 4.45123 5.505257 ------------------------------------------------------------------------------ . estat ic Akaike's information criterion and Bayesian information criterion ----------------------------------------------------------------------------- Model | N ll(null) ll(model) df AIC BIC -------------+--------------------------------------------------------------- . | 2,816 . -12745.67 27 25545.34 25705.8 ----------------------------------------------------------------------------- Note: BIC uses N = number of observations. See [R] IC note. . * Osa 3: DCC mudeli aruande uuesti kuvamine . estimates replay dcc ---------------------------------------------------------------------------------------------------------------------------------- Model dcc ---------------------------------------------------------------------------------------------------------------------------------- Dynamic conditional correlation MGARCH model Sample: 05jan2012 thru 15mar2023 Number of obs = 2,816 Distribution: t Wald chi2(9) = 29.35 Log likelihood = -12733.61 Prob > chi2 = 0.0006 ------------------------------------------------------------------------------ | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- rt | rt | L1. | -.0001789 .0259538 -0.01 0.994 -.0510473 .0506895 | rh | L1. | -.0408255 .0234572 -1.74 0.082 -.0868007 .0051498 | rn | L1. | .0035264 .0162349 0.22 0.828 -.0282934 .0353462 | _cons | .0232877 .0200994 1.16 0.247 -.0161064 .0626817 -------------+---------------------------------------------------------------- ARCH_rt | arch | L1. | .0515946 .010574 4.88 0.000 .0308699 .0723192 | garch | L1. | .9170586 .0192026 47.76 0.000 .8794223 .9546949 | _cons | .0622094 .0200626 3.10 0.002 .0228875 .1015313 -------------+---------------------------------------------------------------- rh | rt | L1. | -.0105487 .0280614 -0.38 0.707 -.065548 .0444506 | rh | L1. | -.0085852 .0268135 -0.32 0.749 -.0611388 .0439683 | rn | L1. | -.0102419 .0190379 -0.54 0.591 -.0475555 .0270716 | _cons | .0040188 .0227015 0.18 0.859 -.0404754 .0485129 -------------+---------------------------------------------------------------- ARCH_rh | arch | L1. | .0286762 .0055381 5.18 0.000 .0178216 .0395307 | garch | L1. | .9552724 .0092809 102.93 0.000 .9370821 .9734627 | _cons | .0402334 .0119828 3.36 0.001 .0167475 .0637193 -------------+---------------------------------------------------------------- rn | rt | L1. | .0751785 .0298827 2.52 0.012 .0166095 .1337474 | rh | L1. | -.0097894 .0277972 -0.35 0.725 -.0642708 .0446921 | rn | L1. | -.0682447 .0220833 -3.09 0.002 -.1115271 -.0249623 | _cons | .0003751 .0236576 0.02 0.987 -.0459928 .0467431 -------------+---------------------------------------------------------------- ARCH_rn | arch | L1. | .0295239 .0062571 4.72 0.000 .0172602 .0417876 | garch | L1. | .9646682 .0078573 122.77 0.000 .9492683 .9800681 | _cons | .0176425 .0068386 2.58 0.010 .004239 .031046 -------------+---------------------------------------------------------------- corr(rt,rh)| .7508237 .0112829 66.55 0.000 .7287096 .7729377 corr(rt,rn)| .6039091 .0163326 36.98 0.000 .5718979 .6359203 corr(rh,rn)| .6292684 .0155115 40.57 0.000 .5988665 .6596703 -------------+---------------------------------------------------------------- /Adjustment | lambda1 | .0337692 .0075042 4.50 0.000 .0190613 .0484771 lambda2 | .8256672 .0446211 18.50 0.000 .7382115 .913123 -------------+---------------------------------------------------------------- /df | 5.00622 .2720748 18.40 0.000 4.472963 5.539477 ------------------------------------------------------------------------------ . * Osa 3. Järgneb mitteoluliste tunnuste samm-sammuline eemaldamine . * 1. samm . mgarch dcc (rt = L.rh L.rn) (rh = L.rt L.rh L.rn, noconstant) (rn = L.rt L.rh L.rn, no > constant), arch(1/1) garch(1/1) distribution(t) Calculating starting values.... Optimizing log likelihood (setting technique to bhhh) Iteration 0: Log likelihood = -13185.986 Iteration 1: Log likelihood = -12981.517 Iteration 2: Log likelihood = -12821.663 Iteration 3: Log likelihood = -12773.513 Iteration 4: Log likelihood = -12768.438 Iteration 5: Log likelihood = -12739.188 Iteration 6: Log likelihood = -12735.683 Iteration 7: Log likelihood = -12734.338 Iteration 8: Log likelihood = -12733.854 Iteration 9: Log likelihood = -12733.72 (switching technique to nr) Iteration 10: Log likelihood = -12733.665 Iteration 11: Log likelihood = -12733.629 Iteration 12: Log likelihood = -12733.629 Refining estimates Iteration 0: Log likelihood = -12733.629 Iteration 1: Log likelihood = -12733.629 (backed up) Dynamic conditional correlation MGARCH model Sample: 05jan2012 thru 15mar2023 Number of obs = 2,816 Distribution: t Wald chi2(8) = 29.34 Log likelihood = -12733.63 Prob > chi2 = 0.0003 ------------------------------------------------------------------------------ | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- rt | rh | L1. | -.0408635 .0192856 -2.12 0.034 -.0786626 -.0030643 | rn | L1. | .003451 .0158079 0.22 0.827 -.0275318 .0344339 | _cons | .0210724 .0133289 1.58 0.114 -.0050518 .0471966 -------------+---------------------------------------------------------------- ARCH_rt | arch | L1. | .0516288 .0105774 4.88 0.000 .0308975 .0723601 | garch | L1. | .9169852 .0192101 47.73 0.000 .8793342 .9546363 | _cons | .0622887 .0200748 3.10 0.002 .0229428 .1016347 -------------+---------------------------------------------------------------- rh | rt | L1. | -.0102699 .0197365 -0.52 0.603 -.0489527 .028413 | rh | L1. | -.0086553 .0248128 -0.35 0.727 -.0572875 .0399769 | rn | L1. | -.0103661 .0187767 -0.55 0.581 -.0471678 .0264356 -------------+---------------------------------------------------------------- ARCH_rh | arch | L1. | .0287226 .0055431 5.18 0.000 .0178583 .0395868 | garch | L1. | .9552056 .0092874 102.85 0.000 .9370028 .9734085 | _cons | .0402901 .0119924 3.36 0.001 .0167854 .0637948 -------------+---------------------------------------------------------------- rn | rt | L1. | .0752209 .0246397 3.05 0.002 .0269279 .1235138 | rh | L1. | -.0097596 .0263795 -0.37 0.711 -.0614625 .0419433 | rn | L1. | -.0682616 .0219499 -3.11 0.002 -.1112826 -.0252407 -------------+---------------------------------------------------------------- ARCH_rn | arch | L1. | .0295256 .0062528 4.72 0.000 .0172704 .0417808 | garch | L1. | .9646703 .0078482 122.92 0.000 .9492881 .9800525 | _cons | .0176351 .0068274 2.58 0.010 .0042537 .0310165 -------------+---------------------------------------------------------------- corr(rt,rh)| .7508417 .0112818 66.55 0.000 .7287298 .7729536 corr(rt,rn)| .6039336 .0163313 36.98 0.000 .5719248 .6359424 corr(rh,rn)| .6292914 .0155103 40.57 0.000 .5988918 .659691 -------------+---------------------------------------------------------------- /Adjustment | lambda1 | .0337743 .0074971 4.50 0.000 .0190803 .0484684 lambda2 | .8256719 .0445504 18.53 0.000 .7383547 .9129891 -------------+---------------------------------------------------------------- /df | 5.006613 .2719056 18.41 0.000 4.473688 5.539539 ------------------------------------------------------------------------------ .* Osa 3, 2. samm . mgarch dcc (rt = L.rh) (rh = L.rt L.rn, noconstant) (rn = L.rt L.rn, noconstant), arch > (1/1) garch(1/1) distribution(t) Calculating starting values.... Optimizing log likelihood (setting technique to bhhh) Iteration 0: Log likelihood = -13187.564 Iteration 1: Log likelihood = -12985.144 Iteration 2: Log likelihood = -12822.992 Iteration 3: Log likelihood = -12773.596 Iteration 4: Log likelihood = -12763.807 Iteration 5: Log likelihood = -12739.296 Iteration 6: Log likelihood = -12735.809 Iteration 7: Log likelihood = -12734.452 Iteration 8: Log likelihood = -12733.941 Iteration 9: Log likelihood = -12733.807 (switching technique to nr) Iteration 10: Log likelihood = -12733.752 Iteration 11: Log likelihood = -12733.717 Iteration 12: Log likelihood = -12733.716 Refining estimates Iteration 0: Log likelihood = -12733.716 Iteration 1: Log likelihood = -12733.716 (backed up) Dynamic conditional correlation MGARCH model Sample: 05jan2012 thru 15mar2023 Number of obs = 2,816 Distribution: t Wald chi2(5) = 29.15 Log likelihood = -12733.72 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- rt | rh | L1. | -.0351779 .0124378 -2.83 0.005 -.0595556 -.0108003 | _cons | .0209836 .0133257 1.57 0.115 -.0051342 .0471015 -------------+---------------------------------------------------------------- ARCH_rt | arch | L1. | .0514831 .0105186 4.89 0.000 .030867 .0720992 | garch | L1. | .9172943 .0190672 48.11 0.000 .8799234 .9546653 | _cons | .0619525 .0199026 3.11 0.002 .0229442 .1009608 -------------+---------------------------------------------------------------- rh | rt | L1. | -.012746 .0170479 -0.75 0.455 -.0461592 .0206673 | rn | L1. | -.0141263 .0124964 -1.13 0.258 -.0386187 .0103661 -------------+---------------------------------------------------------------- ARCH_rh | arch | L1. | .0287079 .0055275 5.19 0.000 .0178742 .0395417 | garch | L1. | .9552714 .0092435 103.35 0.000 .9371546 .9733883 | _cons | .0401694 .0119257 3.37 0.001 .0167954 .0635435 -------------+---------------------------------------------------------------- rn | rt | L1. | .071606 .0211165 3.39 0.001 .0302184 .1129936 | rn | L1. | -.0721672 .0175739 -4.11 0.000 -.1066114 -.0377231 -------------+---------------------------------------------------------------- ARCH_rn | arch | L1. | .0294711 .0062303 4.73 0.000 .01726 .0416822 | garch | L1. | .9647424 .0078188 123.39 0.000 .9494179 .9800669 | _cons | .017571 .0068001 2.58 0.010 .004243 .0308989 -------------+---------------------------------------------------------------- corr(rt,rh)| .7507938 .011286 66.52 0.000 .7286736 .772914 corr(rt,rn)| .6038776 .0163366 36.96 0.000 .5718584 .6358969 corr(rh,rn)| .6292538 .0155156 40.56 0.000 .5988438 .6596637 -------------+---------------------------------------------------------------- /Adjustment | lambda1 | .0337602 .0074713 4.52 0.000 .0191168 .0484036 lambda2 | .8260092 .0442034 18.69 0.000 .7393721 .9126463 -------------+---------------------------------------------------------------- /df | 5.00889 .2720331 18.41 0.000 4.475715 5.542065 ------------------------------------------------------------------------------ .* Osa 3, 3. samm . mgarch dcc (rt = L.rh, noconstant) (rh = L.rn, noconstant) (rn = L.rt L.rn, noconstant > ), arch(1/1) garch(1/1) distribution(t) Calculating starting values.... Optimizing log likelihood (setting technique to bhhh) Iteration 0: Log likelihood = -13188.167 Iteration 1: Log likelihood = -12987.394 Iteration 2: Log likelihood = -12824.645 Iteration 3: Log likelihood = -12775.491 Iteration 4: Log likelihood = -12764.601 Iteration 5: Log likelihood = -12741.013 Iteration 6: Log likelihood = -12737.421 Iteration 7: Log likelihood = -12736.035 Iteration 8: Log likelihood = -12735.494 Iteration 9: Log likelihood = -12735.354 (switching technique to nr) Iteration 10: Log likelihood = -12735.297 Iteration 11: Log likelihood = -12735.261 Iteration 12: Log likelihood = -12735.261 Refining estimates Iteration 0: Log likelihood = -12735.261 Iteration 1: Log likelihood = -12735.261 (backed up) Dynamic conditional correlation MGARCH model Sample: 05jan2012 thru 15mar2023 Number of obs = 2,816 Distribution: t Wald chi2(4) = 28.41 Log likelihood = -12735.26 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- rt | rh | L1. | -.0316372 .0115293 -2.74 0.006 -.0542341 -.0090403 -------------+---------------------------------------------------------------- ARCH_rt | arch | L1. | .0518194 .0106165 4.88 0.000 .0310115 .0726272 | garch | L1. | .9165096 .019303 47.48 0.000 .8786764 .9543427 | _cons | .0628824 .0202108 3.11 0.002 .0232699 .1024948 -------------+---------------------------------------------------------------- rh | rn | L1. | -.0181595 .0111014 -1.64 0.102 -.0399178 .0035989 -------------+---------------------------------------------------------------- ARCH_rh | arch | L1. | .0286307 .0055294 5.18 0.000 .0177933 .039468 | garch | L1. | .9552806 .0092824 102.91 0.000 .9370874 .9734737 | _cons | .0404069 .0120164 3.36 0.001 .0168551 .0639587 -------------+---------------------------------------------------------------- rn | rt | L1. | .07754 .0194627 3.98 0.000 .0393939 .1156862 | rn | L1. | -.0733013 .0174567 -4.20 0.000 -.1075157 -.0390868 -------------+---------------------------------------------------------------- ARCH_rn | arch | L1. | .0293664 .0062013 4.74 0.000 .0172121 .0415207 | garch | L1. | .9648974 .0077723 124.15 0.000 .949664 .9801309 | _cons | .0174879 .0067611 2.59 0.010 .0042363 .0307395 -------------+---------------------------------------------------------------- corr(rt,rh)| .7508325 .0112834 66.54 0.000 .7287174 .7729475 corr(rt,rn)| .6038322 .0163388 36.96 0.000 .5718087 .6358556 corr(rh,rn)| .629155 .015518 40.54 0.000 .5987404 .6595697 -------------+---------------------------------------------------------------- /Adjustment | lambda1 | .0337799 .0075095 4.50 0.000 .0190616 .0484982 lambda2 | .8258464 .0447348 18.46 0.000 .7381679 .913525 -------------+---------------------------------------------------------------- /df | 4.998959 .2711953 18.43 0.000 4.467425 5.530492 ------------------------------------------------------------------------------ . * Osa 3, 4. samm . mgarch dcc (rt = L.rh, noconstant) (rh =, noconstant) (rn = L.rt L.rn, noconstant), ar > ch(1/1) garch(1/1) distribution(t) Calculating starting values.... Optimizing log likelihood (setting technique to bhhh) Iteration 0: Log likelihood = -13190.457 Iteration 1: Log likelihood = -12995.359 Iteration 2: Log likelihood = -12828.831 Iteration 3: Log likelihood = -12777.669 Iteration 4: Log likelihood = -12761.612 Iteration 5: Log likelihood = -12742.639 Iteration 6: Log likelihood = -12738.865 Iteration 7: Log likelihood = -12737.434 Iteration 8: Log likelihood = -12736.939 Iteration 9: Log likelihood = -12736.726 (switching technique to nr) Iteration 10: Log likelihood = -12736.648 Iteration 11: Log likelihood = -12736.598 Iteration 12: Log likelihood = -12736.598 Refining estimates Iteration 0: Log likelihood = -12736.598 Iteration 1: Log likelihood = -12736.598 (backed up) Dynamic conditional correlation MGARCH model Sample: 05jan2012 thru 15mar2023 Number of obs = 2,816 Distribution: t Wald chi2(3) = 25.75 Log likelihood = -12736.6 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- rt | rh | L1. | -.0228681 .0102048 -2.24 0.025 -.0428691 -.0028671 -------------+---------------------------------------------------------------- ARCH_rt | arch | L1. | .0514449 .010527 4.89 0.000 .0308123 .0720776 | garch | L1. | .917201 .0190853 48.06 0.000 .8797946 .9546075 | _cons | .0622316 .019942 3.12 0.002 .0231459 .1013172 -------------+---------------------------------------------------------------- ARCH_rh | arch | L1. | .0289991 .0056193 5.16 0.000 .0179854 .0400127 | garch | L1. | .9547423 .0094213 101.34 0.000 .936277 .9732076 | _cons | .0409186 .0121759 3.36 0.001 .0170543 .0647829 -------------+---------------------------------------------------------------- rn | rt | L1. | .0786886 .0194498 4.05 0.000 .0405677 .1168096 | rn | L1. | -.0632773 .0163389 -3.87 0.000 -.0953009 -.0312536 -------------+---------------------------------------------------------------- ARCH_rn | arch | L1. | .0292807 .0061572 4.76 0.000 .0172128 .0413486 | garch | L1. | .9650214 .0077096 125.17 0.000 .949911 .9801319 | _cons | .0173843 .0067068 2.59 0.010 .0042392 .0305295 -------------+---------------------------------------------------------------- corr(rt,rh)| .7508089 .0112812 66.55 0.000 .7286981 .7729197 corr(rt,rn)| .6038264 .0163302 36.98 0.000 .5718198 .635833 corr(rh,rn)| .6292351 .015509 40.57 0.000 .5988381 .6596321 -------------+---------------------------------------------------------------- /Adjustment | lambda1 | .0336766 .0074951 4.49 0.000 .0189866 .0483666 lambda2 | .8259307 .0447997 18.44 0.000 .738125 .9137364 -------------+---------------------------------------------------------------- /df | 5.000413 .2713461 18.43 0.000 4.468584 5.532241 ------------------------------------------------------------------------------ .* viimane mudel on lõplik mudel . * Osa 4, kitsenduste testimine lambdade jaoks . test (lambda1 lambda2) ( 1) [/Adjustment]lambda1 = 0 ( 2) [/Adjustment]lambda2 = 0 chi2( 2) = 823.76 Prob > chi2 = 0.0000 . * Osa 5 tinglike dispersioonide ja kovariatsioonide prognoosimine . tsappend, add(100) . predict var*, variance dynamic(2819) . twoway (tsline var_rt_rt) (tsline var_rh_rh) (tsline var_rn_rn) in 2000/2918, legend(p > osition(6)) . * Osa 6 tinglike korrelatsioonikordajate prognoosimine . predict corr*, correlation dynamic(2819) (no lags of dependent variables on rhs; option dynamic() ignored) . twoway (tsline corr_rh_rt) (tsline corr_rn_rt) (tsline corr_rn_rh) in 2000/2918, legen > d(position(6)) ----------------------------------------------------------------------------------------