如何使用＆amp;使用mGJR（）
我正在使用来自R的mGJR（）命令的双变量GJR模型。
来自“mgarchBEKK”包的说明我输入第一个时间序列，第二个时间序列，等等。我试图使用意外的返回作为我的输入，并需要这些系数。
我以为我需要输入我预先计算的意外回报作为我的第一个时间序列，第二个时间序列等等到我的模型中。
然而，当我运行mGJR（）时，它会输出“$ resid1”和“$ resid2”的输出，它看起来像我一直在寻找的残差（即意外的回报）。
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如果是这样，我是否需要输入退货而不是意外退货到模型中以自动导出意外退货？
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此外，如果我尝试使用从我的输出中导出的系数来描述它，我的双变量GJR GARCH模型如何？ 如何从我下面的长输出中获得我需要的模型系数？ 具体来说，我发现我总共有17个系数，其中一个系数为零。我发现这些系数按4分组，其中最后一个只剩下一个 例如，我发现$ est.params $
1，$ est.params $2，$ est.params $3，$ est.params $4，$ est.params $5其中共有17个参数。 但是，我不确定这些在数学上是如何在正式的双变量GJR GARCH公式中明确表达的。
请注意，这是“双变量”GJR GARCH，而不仅仅是GJR GARCH。因此，我有17个参数，其中我有4个块，每个块有4个系数加一个参数使它总共17个。但是，我不知道哪个参数对应于哪个变量系数。我试图提供尽可能多的信息，但如果需要澄清，请告诉我。
我使用预期回报获得的输出如下：
mGJR（eps1，eps2，order = c（1,1,1））
Warning: initial values for the parameters are set at:
2 0 2 0.4 0.1 0.1 0.4 0.4 0.1 0.1 0.4 0.1 0.1 0.1 0.1 0.5
Starting estimation process via loglikelihood function implemented in C.
Optimization Method is ' BFGS '
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Estimation process completed.
Starting diagnostics...
Calculating estimated:
1. residuals,
2. correlations,
3. standard deviations,
4. eigenvalues.
Diagnostics ended...
Class attributes are ready via following names:
eps1 eps2 series.length estimation.time total.time order estimation aic asy.se.coef est.params cor sd1 sd2 H.estimated eigenvalues uncond.cov.matrix resid1 resid2
$eps1
[1] -0.002605971 0.110882333 -0.148960989 -0.068514869 -0.003755887
[6] 0.010796054 -0.147830267 0.047830346 0.028587561 0.003945359
[11] 0.082094667 -0.027768830 -0.006713995 0.024364330 -0.012109627
[16] -0.018345875 0.025668553 0.004490535 0.017510124 0.027143473
[21] 0.011606530 0.010522457 0.026053738 0.009380949 -0.070996648
[26] 0.020755072 -0.005830603 0.014289265 -0.000418889 0.022697292
[31] 0.023063329 0.005635615 0.049926161 0.013989454 0.019870327
[36] 0.018279627 0.014478743 -0.002177036 0.024635614 0.050726032
[41] -0.004392337 0.001234857 -0.018066777 -0.054437778 0.010428982
[46] -0.082777078 0.127812102 0.008940764 -0.001295593 0.060328122
[51] -0.009104799 -0.007204478 0.045631975 0.023096514 0.010598574
[56] 0.016541977 -0.011387952 -0.038157908 0.010327360 0.044342365
[61] 0.035077460 0.017492338 0.038596692 0.137205423 -0.004735584
[66] 0.104792896 0.036139814 -0.096482047 -0.000561027 -0.002632458
[71] 0.016177144 0.025230196 0.031753168 0.068971843 0.054021759
[76] 0.027263191 -0.025345373 0.033643409 -0.060322431 0.030377924
[81] -0.069716766 -0.089266804
$eps2
[1] -0.002889166 0.003033355 -0.002152031 0.003236581 0.003236581
[6] -0.001602802 0.004961099 -0.003176289 -0.000264979 -0.000264979
[11] -0.000264979 -0.001112752 0.004795299 0.004795299 0.005683859
[16] 0.007793699 0.001613168 -0.000354773 0.001350773 -0.000303199
[21] 0.009337753 0.009337753 0.001886769 -0.001791025 0.005869744
[26] 0.004795546 0.004795546 0.004509183 0.005226653 0.000383686
[31] 0.000207546 0.000207546 0.000207546 0.001570381 0.001669796
[36] 0.000549576 0.000549576 -0.001210093 0.014468461 -0.005345880
[41] 0.000130449 0.000130449 -0.001412638 -0.003304416 0.000117946
[46] 0.002145056 0.002145056 -0.002114632 0.005395410 -0.003153774
[51] 0.001888270 -0.001988031 0.000716514 -0.000331566 -0.000331566
[56] -0.000325350 -0.002882419 -0.006754058 -0.006754058 -0.001131800
[61] -0.017930260 0.002718202 0.006840023 0.006840023 0.002059632
[66] 0.003552300 0.003350965 -0.000126651 -0.000126651 -0.000126651
[71] -0.000990530 0.006430433 0.002933145 0.002933145 -0.002259438
[76] 0.001770744 0.000417412 0.004213458 0.004213458 0.004360485
[81] 0.002158630 -0.000686097
$series.length
[1] 82
$estimation.time
Time difference of 0.109386 secs
$total.time
Time difference of 0.1562669 secs
$order
GARCH component ARCH component HJR component
1 1 1
$estimation
$estimation$par
[1] -3.902944e-02 -2.045331e-05 -4.296356e-03 2.268312e-01 2.111034e+00
[6] 1.350601e-04 1.252329e-01 -3.143425e-01 -1.538355e-02 -5.587068e-03
[11] -1.628474e-04 4.224089e-01 1.025256e-01 -7.414033e-03 -4.869328e-01
[16] -1.102507e+00
$estimation$value
[1] -459.6969
$estimation$counts
function gradient
278 53
$estimation$convergence
[1] 0
$estimation$message
NULL
$estimation$hessian
[,1] [,2] [,3] [,4] [,5]
[1,] 77991.191735 -27033.70607 -1.895287e+03 -655.73521140 -6.727215e+01
[2,] -27033.706072 3337349.78552 -3.369295e+05 -371.07738150 -1.447052e+02
[3,] -1895.286899 -336929.51987 1.109169e+07 -122.26145691 -5.595868e+00
[4,] -655.735211 -371.07738 -1.222615e+02 18.61522485 -1.311354e-02
[5,] -67.272152 -144.70520 -5.595868e+00 -0.01311354 3.109780e-01
[6,] 20.487872 -18111.17773 3.525887e+03 -5.52437237 -8.751496e-02
[7,] -26.898108 -2073.43486 -2.975629e+03 -0.26691407 -3.916406e-01
[8,] 1477.726124 320.50607 -4.807709e+02 -9.98402142 -9.782072e-01
[9,] 9.388141 -27.62368 -5.331019e+01 -0.16106385 -1.537450e-02
[10,] -179.429796 49000.01743 2.023153e+04 7.66772695 1.378254e+00
[11,] 16.757240 -87.91362 2.360375e+03 0.23119576 7.084715e-02
[12,] -317.440585 -56.15303 3.710999e+01 6.57357184 -1.785094e-01
[13,] 3.793978 98.71583 -1.142264e+01 -0.22870343 1.543862e-02
[14,] -146.123961 -9829.15416 -5.196531e+02 -29.62565159 4.260863e-01
[15,] 18.082524 131.52060 3.398486e+03 0.33823287 3.212786e-02
[16,] 11.460530 -240.54059 6.706526e+02 0.32655416 -4.680544e-03
[,6] [,7] [,8] [,9] [,10]
[1,] 2.048787e+01 -26.8981081 1477.7261235 9.38814077 -179.429796
[2,] -1.811118e+04 -2073.4348620 320.5060742 -27.62367781 49000.017430
[3,] 3.525887e+03 -2975.6287124 -480.7709387 -53.31018730 20231.529905
[4,] -5.524372e+00 -0.2669141 -9.9840214 -0.16106385 7.667727
[5,] -8.751496e-02 -0.3916406 -0.9782072 -0.01537450 1.378254
[6,] 4.340038e+03 72.0221887 23.7403796 4.74321851 -479.279271
[7,] 7.202219e+01 22.5064989 -0.6280896 0.21674046 -44.382358
[8,] 2.374038e+01 -0.6280896 123.3928335 2.05555317 -53.354577
[9,] 4.743219e+00 0.2167405 2.0555532 20.53760214 53.165201
[10,] -4.792793e+02 -44.3823578 -53.3545766 53.16520102 17583.612011
[11,] -2.045612e+00 1.0454365 38.9154805 -823.29002882 -1763.407498
[12,] -1.488681e+01 -0.5717977 -6.3888226 -0.05658090 -21.965231
[13,] -4.554201e-01 -0.2556849 0.1795778 0.01041940 1.602574
[14,] 2.372186e+02 -13.7297349 13.5989185 -1.51829772 -127.664692
[15,] -1.372792e+01 -1.3537030 0.4896836 0.05291901 12.398407
[16,] -2.586931e+00 -0.1781386 0.1308570 0.05498165 -7.648387
[,11] [,12] [,13] [,14] [,15]
[1,] 1.675724e+01 -317.4405852 3.79397825 -146.1239612 18.08252377
[2,] -8.791362e+01 -56.1530304 98.71583141 -9829.1541554 131.52059520
[3,] 2.360375e+03 37.1099898 -11.42263544 -519.6531079 3398.48583556
[4,] 2.311958e-01 6.5735718 -0.22870343 -29.6256516 0.33823287
[5,] 7.084715e-02 -0.1785094 0.01543862 0.4260863 0.03212786
[6,] -2.045612e+00 -14.8868094 -0.45542005 237.2185632 -13.72791768
[7,] 1.045436e+00 -0.5717977 -0.25568491 -13.7297349 -1.35370300
[8,] 3.891548e+01 -6.3888226 0.17957777 13.5989185 0.48968359
[9,] -8.232900e+02 -0.0565809 0.01041940 -1.5182977 0.05291901
[10,] -1.763407e+03 -21.9652313 1.60257372 -127.6646916 12.39840658
[11,] 4.214986e+04 -0.0719787 0.06153061 -11.5769904 1.70462536
[12,] -7.197870e-02 18.7268970 -0.46324902 -16.1849665 1.23612627
[13,] 6.153061e-02 -0.4632490 0.12685032 1.2327783 -0.20692983
[14,] -1.157699e+01 -16.1849665 1.23277827 3180.7362850 -40.24439774
[15,] 1.704625e+00 1.2361263 -0.20692983 -40.2443977 9.65359055
[16,] -1.608423e-01 -0.4136609 0.07688678 13.4226923 0.70015741
[,16]
[1,] 1.146053e+01
[2,] -2.405406e+02
[3,] 6.706526e+02
[4,] 3.265542e-01
[5,] -4.680544e-03
[6,] -2.586931e+00
[7,] -1.781386e-01
[8,] 1.308570e-01
[9,] 5.498165e-02
[10,] -7.648387e+00
[11,] -1.608423e-01
[12,] -4.136609e-01
[13,] 7.688678e-02
[14,] 1.342269e+01
[15,] 7.001574e-01
[16,] 2.609256e+00
$aic
[1] -443.6969
$asy.se.coef
$asy.se.coef[[1]]
[,1] [,2]
[1,] 0.005951115 0.0006300630
[2,] 0.000000000 0.0003293308
$asy.se.coef[[2]]
[,1] [,2]
[1,] 0.3150396 0.01581263
[2,] 2.3065406 0.24110204
$asy.se.coef[[3]]
[,1] [,2]
[1,] 0.1049158 0.007811719
[2,] 0.4800751 0.010559776
$asy.se.coef[[4]]
[,1] [,2]
[1,] 0.2626887 0.01915952
[2,] 3.1255330 0.36661918
$asy.se.coef[[5]]
[1] 0.6559587
$est.params
$est.params$`1`
[,1] [,2]
[1,] -0.03902944 -2.045331e-05
[2,] 0.00000000 -4.296356e-03
$est.params$`2`
[,1] [,2]
[1,] 0.2268312 0.0001350601
[2,] 2.1110340 0.1252329455
$est.params$`3`
[,1] [,2]
[1,] -0.31434246 -0.0055870676
[2,] -0.01538355 -0.0001628474
$est.params$`4`
[,1] [,2]
[1,] 0.4224089 -0.007414033
[2,] 0.1025256 -0.486932758
$est.params$`5`
[1] -1.102507
$cor
[1] NA 0.031402656 0.058089044 -0.283965989 0.160141195
[6] 0.053237600 0.024081209 0.199587984 0.050169828 0.024045688
[11] 0.022017308 0.015292008 -0.015322752 0.070343728 0.060106129
[16] 0.104828553 0.165459125 0.030923632 0.022277698 0.026315363
[21] 0.020411283 0.102018250 0.102516847 0.035770620 0.024838651
[26] 0.274964544 0.063922572 0.067181338 0.051522997 0.051263760
[31] 0.023492076 0.022088161 0.021845645 0.021179838 0.028180317
[36] 0.028967267 0.023372747 0.022865880 0.020896186 0.180173786
[41] 0.034766653 0.022790880 0.021499773 -0.005938808 -0.137011386
[46] 0.029587448 0.062026969 0.053761176 0.036707465 0.054668898
[51] 0.009740057 0.040966003 0.012100219 0.024982728 0.021599599
[56] 0.021286712 0.020662963 -0.000403477 -0.118423344 0.080086394
[61] 0.017643159 0.287047099 0.043052577 0.095924672 0.129103089
[66] 0.052969944 0.066284046 0.055521350 -0.095508217 0.040009553
[71] 0.022822525 0.020620174 0.080723033 0.044702009 0.051760071
[76] 0.015962034 0.031439947 0.021103665 0.057557712 0.184430145
[81] 0.061929502 0.074235107
$sd1
[1] NA 0.04250885 0.05256355 0.08452372 0.05574627 0.04322082
[7] 0.04134005 0.07735273 0.04624463 0.04210977 0.04121545 0.04524121
[13] 0.04400240 0.04235476 0.04419338 0.04269033 0.04362228 0.04244062
[19] 0.04124873 0.04171567 0.04157849 0.04691775 0.04729332 0.04297800
[25] 0.04133609 0.05057450 0.04475342 0.04245633 0.04322428 0.04275222
[31] 0.04173813 0.04159489 0.04119978 0.04290491 0.04182107 0.04199520
[37] 0.04156416 0.04141240 0.04126752 0.05496396 0.04274670 0.04132509
[43] 0.04113889 0.04241273 0.05098749 0.04227554 0.05554117 0.05507267
[49] 0.04276771 0.04274602 0.04200447 0.04140829 0.04167090 0.04296412
[55] 0.04157519 0.04119998 0.04124952 0.04231602 0.04991076 0.04371888
[61] 0.04217155 0.05092909 0.04333244 0.04758239 0.06275051 0.04389357
[67] 0.05246204 0.04515198 0.06193409 0.04361523 0.04139218 0.04118088
[73] 0.04553376 0.04376651 0.04708736 0.04252185 0.04248968 0.04285559
[79] 0.04458945 0.04858689 0.04500212 0.05183762
$sd2
[1] NA 0.004482407 0.004338972 0.004809936 0.004467527 0.004585295
[7] 0.004308208 0.004536616 0.004338359 0.004304288 0.004302985 0.004303282
[13] 0.004368628 0.004897167 0.004391425 0.005115556 0.005729580 0.004312162
[19] 0.004303200 0.004308760 0.004302868 0.004976847 0.005020608 0.004316566
[25] 0.004309195 0.004939797 0.004398925 0.004894407 0.004397014 0.004840029
[31] 0.004303750 0.004303055 0.004302843 0.004303352 0.004311628 0.004312208
[37] 0.004303962 0.004303769 0.004340892 0.005536457 0.004364419 0.004303177
[43] 0.004302664 0.004381032 0.004754002 0.004305919 0.004331610 0.004329750
[49] 0.004315664 0.004919065 0.004322781 0.004392385 0.004419426 0.004305283
[55] 0.004303278 0.004302883 0.004302743 0.004548148 0.005589150 0.004407496
[61] 0.004305608 0.004895706 0.004332758 0.004476358 0.004463722 0.004425254
[67] 0.004349885 0.004344478 0.004371742 0.004310808 0.004304083 0.004304599
[73] 0.004475105 0.004333152 0.004333715 0.004314340 0.004313605 0.004303264
[79] 0.004365063 0.004618021 0.004372850 0.004343962
$H.estimated
, , 1
[,1] [,2]
[1,] 2.398788e-03 6.043323e-06
[2,] 6.043323e-06 1.742282e-05
, , 2
[,1] [,2]
[1,] 1.807002e-03 5.983524e-06
[2,] 5.983524e-06 2.009197e-05
, , 3
[,1] [,2]
[1,] 2.762927e-03 1.324847e-05
[2,] 1.324847e-05 1.882667e-05
, , 4
[,1] [,2]
[1,] 0.0071442584 -1.154474e-04
[2,] -0.0001154474 2.313548e-05
, , 5
[,1] [,2]
[1,] 3.107646e-03 3.988284e-05
[2,] 3.988284e-05 1.995880e-05
, , 6
[,1] [,2]
[1,] 1.868039e-03 1.055064e-05
[2,] 1.055064e-05 2.102493e-05
, , 7
[,1] [,2]
[1,] 1.709000e-03 4.288901e-06
[2,] 4.288901e-06 1.856066e-05
, , 8
[,1] [,2]
[1,] 5.983444e-03 7.003934e-05
[2,] 7.003934e-05 2.058089e-05
, , 9
[,1] [,2]
[1,] 2.138566e-03 1.006536e-05
[2,] 1.006536e-05 1.882135e-05
, , 10
[,1] [,2]
[1,] 1.773233e-03 4.358343e-06
[2,] 4.358343e-06 1.852689e-05
, , 11
[,1] [,2]
[1,] 1.698713e-03 3.904758e-06
[2,] 3.904758e-06 1.851568e-05
, , 12
[,1] [,2]
[1,] 2.046767e-03 2.977135e-06
[2,] 2.977135e-06 1.851824e-05
, , 13
[,1] [,2]
[1,] 1.936211e-03 -2.945494e-06
[2,] -2.945494e-06 1.908491e-05
, , 14
[,1] [,2]
[1,] 1.793925e-03 1.459058e-05
[2,] 1.459058e-05 2.398224e-05
, , 15
[,1] [,2]
[1,] 1.953055e-03 1.166491e-05
[2,] 1.166491e-05 1.928461e-05
, , 16
[,1] [,2]
[1,] 1.822465e-03 2.289296e-05
[2,] 2.289296e-05 2.616891e-05
, , 17
[,1] [,2]
[1,] 1.902904e-03 4.135442e-05
[2,] 4.135442e-05 3.282809e-05
, , 18
[,1] [,2]
[1,] 1.801206e-03 5.659359e-06
[2,] 5.659359e-06 1.859474e-05
, , 19
[,1] [,2]
[1,] 1.701457e-03 3.954325e-06
[2,] 3.954325e-06 1.851753e-05
, , 20
[,1] [,2]
[1,] 1.740197e-03 4.729997e-06
[2,] 4.729997e-06 1.856541e-05
, , 21
[,1] [,2]
[1,] 1.728771e-03 3.651716e-06
[2,] 3.651716e-06 1.851467e-05
, , 22
[,1] [,2]
[1,] 2.201275e-03 2.382151e-05
[2,] 2.382151e-05 2.476901e-05
, , 23
[,1] [,2]
[1,] 2.236658e-03 2.434172e-05
[2,] 2.434172e-05 2.520650e-05
, , 24
[,1] [,2]
[1,] 1.847108e-03 6.636071e-06
[2,] 6.636071e-06 1.863274e-05
, , 25
[,1] [,2]
[1,] 1.708672e-03 4.424391e-06
[2,] 4.424391e-06 1.856916e-05
, , 26
[,1] [,2]
[1,] 2.557780e-03 6.869377e-05
[2,] 6.869377e-05 2.440159e-05
, , 27
[,1] [,2]
[1,] 2.002868e-03 1.258424e-05
[2,] 1.258424e-05 1.935054e-05
, , 28
[,1] [,2]
[1,] 1.802540e-03 1.396019e-05
[2,] 1.396019e-05 2.395522e-05
, , 29
[,1] [,2]
[1,] 1.868338e-03 9.792344e-06
[2,] 9.792344e-06 1.933373e-05
, , 30
[,1] [,2]
[1,] 0.0018277521 1.060760e-05
[2,] 0.0000106076 2.342588e-05
, , 31
[,1] [,2]
[1,] 1.742072e-03 4.219893e-06
[2,] 4.219893e-06 1.852227e-05
, , 32
[,1] [,2]
[1,] 1.730135e-03 3.953452e-06
[2,] 3.953452e-06 1.851628e-05
, , 33
[,1] [,2]
[1,] 1.697422e-03 3.872712e-06
[2,] 3.872712e-06 1.851446e-05
, , 34
[,1] [,2]
[1,] 1.840831e-03 3.910538e-06
[2,] 3.910538e-06 1.851884e-05
, , 35
[,1] [,2]
[1,] 1.749002e-03 5.081388e-06
[2,] 5.081388e-06 1.859014e-05
, , 36
[,1] [,2]
[1,] 1.763597e-03 5.245741e-06
[2,] 5.245741e-06 1.859513e-05
, , 37
[,1] [,2]
[1,] 1.727580e-03 4.181164e-06
[2,] 4.181164e-06 1.852409e-05
, , 38
[,1] [,2]
[1,] 1.714987e-03 4.075372e-06
[2,] 4.075372e-06 1.852243e-05
, , 39
[,1] [,2]
[1,] 1.703008e-03 3.743298e-06
[2,] 3.743298e-06 1.884335e-05
, , 40
[,1] [,2]
[1,] 3.021037e-03 5.482789e-05
[2,] 5.482789e-05 3.065235e-05
, , 41
[,1] [,2]
[1,] 1.827281e-03 6.486224e-06
[2,] 6.486224e-06 1.904815e-05
, , 42
[,1] [,2]
[1,] 1.707763e-03 4.052884e-06
[2,] 4.052884e-06 1.851733e-05
, , 43
[,1] [,2]
[1,] 1.692408e-03 3.805606e-06
[2,] 3.805606e-06 1.851292e-05
, , 44
[,1] [,2]
[1,] 1.798840e-03 -1.103499e-06
[2,] -1.103499e-06 1.919344e-05
, , 45
[,1] [,2]
[1,] 2.599725e-03 -3.321083e-05
[2,] -3.321083e-05 2.260054e-05
, , 46
[,1] [,2]
[1,] 1.787221e-03 5.385952e-06
[2,] 5.385952e-06 1.854093e-05
, , 47
[,1] [,2]
[1,] 3.084822e-03 1.492262e-05
[2,] 1.492262e-05 1.876285e-05
, , 48
[,1] [,2]
[1,] 0.0030329985 1.281940e-05
[2,] 0.0000128194 1.874673e-05
, , 49
[,1] [,2]
[1,] 1.829077e-03 6.775136e-06
[2,] 6.775136e-06 1.862496e-05
, , 50
[,1] [,2]
[1,] 1.827222e-03 1.149525e-05
[2,] 1.149525e-05 2.419720e-05
, , 51
[,1] [,2]
[1,] 1.764375e-03 1.768562e-06
[2,] 1.768562e-06 1.868643e-05
, , 52
[,1] [,2]
[1,] 1.714646e-03 7.450944e-06
[2,] 7.450944e-06 1.929305e-05
, , 53
[,1] [,2]
[1,] 1.736464e-03 2.228394e-06
[2,] 2.228394e-06 1.953133e-05
, , 54
[,1] [,2]
[1,] 1.845916e-03 4.621122e-06
[2,] 4.621122e-06 1.853546e-05
, , 55
[,1] [,2]
[1,] 1.728496e-03 3.864375e-06
[2,] 3.864375e-06 1.851820e-05
, , 56
[,1] [,2]
[1,] 1.697438e-03 3.773681e-06
[2,] 3.773681e-06 1.851481e-05
, , 57
[,1] [,2]
[1,] 1.701523e-03 3.667388e-06
[2,] 3.667388e-06 1.851360e-05
, , 58
[,1] [,2]
[1,] 1.790646e-03 -7.765298e-08
[2,] -7.765298e-08 2.068565e-05
, , 59
[,1] [,2]
[1,] 2.491084e-03 -3.303522e-05
[2,] -3.303522e-05 3.123859e-05
, , 60
[,1] [,2]
[1,] 1.911341e-03 1.543191e-05
[2,] 1.543191e-05 1.942602e-05
, , 61
[,1] [,2]
[1,] 1.778439e-03 3.203542e-06
[2,] 3.203542e-06 1.853826e-05
, , 62
[,1] [,2]
[1,] 2.593772e-03 7.157055e-05
[2,] 7.157055e-05 2.396793e-05
, , 63
[,1] [,2]
[1,] 1.877700e-03 8.083078e-06
[2,] 8.083078e-06 1.877280e-05
, , 64
[,1] [,2]
[1,] 2.264084e-03 2.043155e-05
[2,] 2.043155e-05 2.003778e-05
, , 65
[,1] [,2]
[1,] 3.937627e-03 3.616188e-05
[2,] 3.616188e-05 1.992481e-05
, , 66
[,1] [,2]
[1,] 1.926645e-03 1.028889e-05
[2,] 1.028889e-05 1.958287e-05
, , 67
[,1] [,2]
[1,] 2.752265e-03 1.512627e-05
[2,] 1.512627e-05 1.892150e-05
, , 68
[,1] [,2]
[1,] 2.038701e-03 1.089117e-05
[2,] 1.089117e-05 1.887449e-05
, , 69
[,1] [,2]
[1,] 3.835832e-03 -2.585979e-05
[2,] -2.585979e-05 1.911213e-05
, , 70
[,1] [,2]
[1,] 1.902289e-03 7.522472e-06
[2,] 7.522472e-06 1.858307e-05
, , 71
[,1] [,2]
[1,] 1.713313e-03 4.065956e-06
[2,] 4.065956e-06 1.852513e-05
, , 72
[,1] [,2]
[1,] 1.695865e-03 3.655281e-06
[2,] 3.655281e-06 1.852958e-05
, , 73
[,1] [,2]
[1,] 0.0020733237 1.644880e-05
[2,] 0.0000164488 2.002657e-05
, , 74
[,1] [,2]
[1,] 0.0019155075 8.477600e-06
[2,] 0.0000084776 1.877621e-05
, , 75
[,1] [,2]
[1,] 2.217220e-03 1.056233e-05
[2,] 1.056233e-05 1.878109e-05
, , 76
[,1] [,2]
[1,] 1.808108e-03 2.928295e-06
[2,] 2.928295e-06 1.861353e-05
, , 77
[,1] [,2]
[1,] 1.805373e-03 5.762429e-06
[2,] 5.762429e-06 1.860719e-05
, , 78
[,1] [,2]
[1,] 1.836602e-03 3.891915e-06
[2,] 3.891915e-06 1.851808e-05
, , 79
[,1] [,2]
[1,] 1.988219e-03 1.120279e-05
[2,] 1.120279e-05 1.905377e-05
, , 80
[,1] [,2]
[1,] 2.360685e-03 4.138156e-05
[2,] 4.138156e-05 2.132612e-05
, , 81
[,1] [,2]
[1,] 2.025191e-03 1.218695e-05
[2,] 1.218695e-05 1.912182e-05
, , 82
[,1] [,2]
[1,] 2.687139e-03 1.671631e-05
[2,] 1.671631e-05 1.887001e-05
$eigenvalues
[1] 4.55569683 0.22879456 0.17683774 0.01426322
$uncond.cov.matrix
[,1] [,2]
[1,] 0.002266730 0.001058754
[2,] 0.001058754 0.014184073
$resid1
[1] 0.000000000 2.606658633 -2.832423405 -0.803429943 -0.076228015
[6] 0.251640690 -3.578761931 0.627605808 0.618572249 0.093834350
[11] 1.992057160 -0.613463505 -0.151066326 0.568366658 -0.281145635
[16] -0.447418119 0.584725959 0.106051164 0.423859148 0.650891694
[21] 0.275002848 0.206027474 0.547710301 0.219653206 -1.720836929
[26] 0.388360723 -0.136578900 0.330299607 -0.015356362 0.530624264
[31] 0.552493411 0.135394145 1.211759017 0.325365217 0.474140365
[36] 0.434967460 0.348083690 -0.051966703 0.590366618 0.941768811
[41] -0.102859959 0.029817748 -0.438515201 -1.283947967 0.205149728
[46] -1.959551678 2.299605523 0.164294634 -0.034506773 1.415352264
[51] -0.217156708 -0.172233261 1.094882761 0.537781247 0.255092253
[56] 0.401673126 -0.274777615 -0.901794851 0.192673354 1.016721711
[61] 0.838610788 0.331314439 0.884670384 2.873061672 -0.079539903
[66] 2.384117962 0.685180049 -2.137240072 -0.009247067 -0.060258875
[71] 0.391339172 0.609775354 0.692992830 1.573446856 1.149792694
[76] 0.640567944 -0.596838960 0.783185773 -1.358186376 0.611629203
[81] -1.552405453 -1.721844943
$resid2
[1] 0.00000000 0.60291683 -0.34446882 0.47408464 0.74401685 -0.36208567
[7] 1.22988753 -0.83357295 -0.08954723 -0.06362390 -0.10131479 -0.25004018
[13] 1.09566787 0.94523475 1.31164457 1.57234641 0.19804905 -0.08528340
[19] 0.30541107 -0.08591785 2.16540690 1.86518502 0.32642704 -0.42228251
[25] 1.40120757 0.90215659 1.09997892 0.90303309 1.19070375 0.05489337
[31] 0.03646646 0.04553108 0.02427045 0.35872374 0.37528677 0.11605970
[37] 0.12034527 -0.28015423 3.32249290 -1.13529670 0.03315067 0.02970541
[43] -0.31984221 -0.76117735 0.05094163 0.55098391 0.36347692 -0.49720367
[49] 1.25203911 -0.71138871 0.43875324 -0.44653544 0.15015921 -0.08924866
[55] -0.08205853 -0.08336948 -0.66487750 -1.48534156 -1.19469384 -0.33165737
[61] -4.17835758 0.48521539 1.54523260 1.28097851 0.47447031 0.68879762
[67] 0.72978029 0.07920992 -0.02991489 -0.02720370 -0.23827852 1.48272617
[73] 0.60612812 0.61341050 -0.57651419 0.40119127 0.11384865 0.96428797
[79] 1.03790954 0.85330306 0.58221097 -0.04007542
attr(,"class")
[1] "mGJR"
我试图复制以下情况：
然后我试图获得如下输出：
1 个答案:
答案 0 :(得分：4)
mGJR命令用于估计GARCH（广义自回归条件异方差）模型。 GARCH模型用于模拟时间序列的波动率（最常见的是资产收益率）。那个（以及许多参数）是您可以从拟合的GJR对象访问的。
如果您想了解更多关于GARCH模型与R中的示例配对的信息，我可以推荐R. Tsay的以下书籍：
- Analysis of Financial Time Series和
- Multivariate Time Series Analysis: With R and Financial Applications
我是否需要输入返回值而不是意外返回到模型中以自动导出意外返回值？
通常 GARCH模型的输入是过去观察到的回报。 （参见上述引用的书籍或最初提出ARCH模型的R. Engle的this article）
有一些测试可以确定时间序列中是否存在任何线性依赖关系。如果有，则需要使用均值模型（例如VARIMA模型）将其删除。金融时间序列的Tsays分析也有例子和不同的案例。第133页很好地解释了波动率模型构建的完整过程。
简短：您的eps1和eps2必须是这些（平均模型更正的）回归系列。
此外，如果我尝试，我的双变量GJR GARCH模型如何 使用从我的输出中导出的系数来描述它？如何从下面的长输出中获得我需要的模型系数？
需要进行一些挖掘，但在查看mgarchBEKK的{{3}}和publication from Schmidbauer & Roesch (2008)时，看起来mGJR规范就是作者Schmidbauer＆amp; Roesch称为双变量不对称二次GARCH（baqGARCH），链接出版物的第5页定义为：
拟合GJR对象的参数按降序表示：C，A，B，Gamma，w。如同出版物的第7页（括号中较小字体的值是t值）：
这是一个可重现的例子，用于拟合mGJR和访问参数：
# packages
library(mgarchBEKK)
# generate heteroscedastic data
dat <- simulateBEKK(series.count = 2, T = 200, c(1,1))
returns1 <- dat$eps[[1]]
returns2 <- dat$eps[[2]]
# fit GJR to data
my_mGJR <- mGJR(eps1 = returns1, eps2 = returns2, order = c(1, 1, 1))
# extract parameters from GJR object
my_param <- my_mGJR$est.params
# assign names
names(my_param) = c('C', 'A', 'B', 'Gamma', 'w')
# access parameters
my_param
拿走，例如系数矩阵B，[1,]和[2,]会告诉您要查看矩阵的哪一行[,1]和[,2]。这里有一个过于简单的解释：由于你有一个双变量模型，对角元素[1,][,1]和[2,][,2]是系数，可以告诉你各个系列在它自己的方差上的一些信息。非对角线元素更多地是关于两个系列的条件协方差或波动溢出。
短：你有（2） - ＆gt;中的等式。您输入如上所示的系数 - ＆gt;您可以求解时间因变量的H_T（时间T的条件协方差矩阵）（returnseries_T-1，H_T-1）。
具体来说，我发现我总共有17个系数 他们是零。
如果系数固定为零，则不会将其计为参数。非对角线下系数C始终固定为零。因此，您总共有16个参数（如果您不限制模型，例如作者在他们的论文中所做的那样）。
然而，当我运行mGJR（）时，它会输出输出结果＆＃34; $ resid1＆＃34; 和＆＃34; $ resid2＆＃34;看起来像残差
这是正确的，它们是系数没有解释的残差，但是对于条件波动率（不确定你在做什么＆＃34;意外回报＆＃34;）。它们可以说是模型无法解释的，随机白噪声（参见例如
或here）。 GARCH模型中的残差主要用于执行一些模型充分性测试来回答这个问题：＆＃34;我的拟合模型是否足以解释条件方差方程？＆＃34;
这里是条件波动率，条件相关性和残差的图。在该系列的条件标准偏差中似乎存在一些波动性聚类（前两个图）。在残差序列中似乎没有那么多结构（最后两个图）。
情节的代码：
library(ggplot2)
library(reshape2)
my_results <- data.frame(index = 1:200,
sd_returns1 = my_mGJR$sd1,
sd_returns2 = my_mGJR$sd2,
cor_returns = my_mGJR$cor,
res_returns1 = my_mGJR$resid1,
res_returns2 = my_mGJR$resid2)
# melt data to long format for plotting
p_results = melt(my_results, id = 'index')
# plot the results
my_p = ggplot(p_results, aes(x = index, y = value)) +
geom_line() +
facet_grid(variable ~ ., scales = "free_y") +
theme_bw()
ggsave('example_cor_sd_res.png', plot = my_p, device = 'png', units = 'cm',
width = 12, height = 15)
我试图复制以下情况：
基本上你拥有所需的一切。可以从参数的标准误差计算参数的重要性（p值或t值）。对于t值，例如您需要将参数除以标准误差。标准错误可以从GJR对象中获取，如：
my_param_se = my_mGJR$asy.se.coef
names(my_param_se) = paste0(rep("tvals_", 5), c('C', 'A', 'B', 'Gamma', 'w'))
my_param_se
由于mGJR命令模型（或baqGARCH）的构造类似于例如BEKK-GARCH你可能无法以与你的例子相同的方式解释它。正如我在上面详细阐述的那样，不同系数的对角线元素将告诉您系列1中创新的系列1的显着条件波动率 。非对角线元素将告诉您一些波动 - 从一个系列到另一个系列的溢出效应。如果您想要考虑到这一点，则需要在表格中包含这些结果。
然后我试图获得如下输出：
我在上面解释过的大部分内容，只是对残差的一个注释。看起来模型充分性是通过LjungBox-Test（= LB？）来衡量的。参见例如
我希望这能回答你的问题。
修改：更新回答以包含其他问题。
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