[R-SIG-Finance] [R-sig-finance] Garch problem
RON70
ron_michael70 at yahoo.com
Tue Mar 17 11:27:38 CET 2009
I have following dataset as monthly percentage return for a stock :
0.173741362
-0.062237174
0.02690583
0.04628821
0.056761269
0.018167457
-0.003103181
0.024902724
0.035687168
0.004398827
-0.054014599
-0.141975309
0.008093525
-0.035682426
0.034227567
-0.072450805
-0.037608486
0.026052104
-0.055664062
-0.024819028
-0.004241782
-0.052183174
-0.004494382
0.081828442
-0.015127804
0.091101695
-0.027184466
-0.032934132
-0.012383901
-0.009404389
0.033755274
-0.004081633
-0.014344262
0.033264033
0.03722334
0.094083414
0.019503546
0.010434783
-0.003442341
-0.121761658
0.041297935
0.050991501
-0.056603774
-0.018095238
0.022308438
-0.059772296
0.042381433
0.012584705
0.003824092
-0.024761905
0.047851562
-0.018173346
0.034646417
-0.033027523
-0.007590133
-0.004780115
0.008645533
-0.016190476
-0.016456922
0.049212598
0.003752345
-0.008411215
0.100848256
0.042808219
0.063218391
-0.016602317
0.056144484
0.054275093
0.028208745
0.020576132
-0.092741935
-0.061481481
-0.08445146
-0.045689655
0.023486902
-0.064430715
0.037735849
0.001818182
-0.025408348
-0.049348231
0.065621939
-0.054227941
-0.040816327
-0.038500507
0.062170706
-0.074404762
0.046087889
0.079918033
-0.046489564
0.080597015
-0.092081031
0.059837728
0.041148325
0.098345588
0.007531381
-0.039036545
0.021607606
0.044839255
-0.07611336
-0.043821209
0.028414299
0.041889483
-0.026518392
0.013181019
0.01300954
0.010273973
-0.022033898
-0.066291161
-0.011600928
-0.020187793
0.00431241
-0.014312977
-0.039690223
-0.023185484
-0.036119711
-0.042826552
-0.035234899
-0.016231884
-0.063052445
-0.031446541
0.045454545
-0.021118012
0.007614213
0.023929471
0.036900369
-0.024911032
-0.0486618
0.051150895
-0.057177616
0.010322581
-0.029374202
0.044736842
0.042821159
-0.073671498
0.070404172
-0.004872107
-0.048959608
0.009009009
0.00127551
0.01910828
0.09
-0.055045872
0.024271845
0.146919431
-0.013429752
0.064921466
0.025565388
0.091083413
-0.024604569
-0.06036036
0.064237776
-0.106306306
0.050403226
-0.065259117
0.137577002
-0.040613718
0.100658514
0.064957265
0.040930979
0.070932922
-0.010079194
-0.055272727
-0.010777521
-0.042801556
0.028455285
0.079841897
0.039531479
0.088028169
0.110032362
0.113119534
0.207962284
0.022983521
0.121237813
0.200378072
0.155905512
-0.122615804
0.060559006
0.010248902
-0.034782609
0.267267267
0.036729858
-0.033142857
-0.177304965
0
-0.064655172
0.153917051
-0.016240682
-0.093369418
0.058208955
-0.122708039
-0.019292605
-0.073770492
-0.081415929
-0.086705202
0.061181435
0.089463221
-0.153284672
-0.038793103
-0.100896861
-0.036408978
-0.01552795
-0.047844374
-0.072335726
-0.330357143
0.075555556
-0.001652893
-0.092301325
Now I fit a GARCH (1,1) model on that :
> garch(Delt(dat)[-1], c(1,1))
***** ESTIMATION WITH ANALYTICAL GRADIENT *****
I INITIAL X(I) D(I)
1 4.331103e-03 1.000e+00
2 5.000000e-02 1.000e+00
3 5.000000e-02 1.000e+00
IT NF F RELDF PRELDF RELDX STPPAR D*STEP
NPRELDF
0 1 -4.507e+02
1 6 -4.508e+02 2.00e-04 3.20e-04 1.5e-03 6.3e+06 1.5e-04
1.01e+03
2 7 -4.508e+02 1.57e-05 1.69e-05 1.4e-03 2.0e+00 1.5e-04
3.19e-01
3 13 -4.521e+02 2.85e-03 4.72e-03 5.6e-01 2.0e+00 1.3e-01
3.16e-01
4 16 -4.602e+02 1.76e-02 4.41e-03 8.1e-01 6.7e-01 5.1e-01
1.99e-02
5 23 -4.607e+02 1.13e-03 2.77e-03 1.6e-04 7.4e+00 1.8e-04
8.48e+00
6 24 -4.607e+02 4.81e-05 4.37e-05 1.6e-04 2.0e+00 1.8e-04
1.77e+01
7 30 -4.638e+02 6.60e-03 8.81e-03 9.8e-02 2.0e+00 1.2e-01
1.84e+01
8 31 -4.645e+02 1.52e-03 7.73e-03 8.2e-02 1.3e+00 1.2e-01
1.39e-02
9 33 -4.688e+02 9.18e-03 6.28e-03 6.8e-02 0.0e+00 1.2e-01
6.94e-03
10 35 -4.693e+02 9.32e-04 9.33e-04 8.9e-03 1.9e+00 1.8e-02
2.86e-02
11 37 -4.699e+02 1.34e-03 1.59e-03 1.6e-02 1.8e+00 3.5e-02
5.99e-02
12 38 -4.704e+02 1.05e-03 1.43e-03 1.6e-02 1.6e+00 3.5e-02
9.10e-03
13 40 -4.705e+02 1.84e-04 2.85e-04 5.3e-03 1.2e+00 1.3e-02
7.52e-04
14 42 -4.705e+02 3.71e-05 5.18e-05 2.4e-03 8.1e-01 5.0e-03
7.09e-05
15 44 -4.705e+02 8.51e-07 3.04e-06 4.9e-04 8.2e-01 9.5e-04
5.29e-06
16 57 -4.705e+02 -7.73e-15 1.09e-15 5.0e-15 4.4e+06 9.1e-15
2.87e-07
***** FALSE CONVERGENCE *****
FUNCTION -4.704848e+02 RELDX 4.961e-15
FUNC. EVALS 57 GRAD. EVALS 16
PRELDF 1.088e-15 NPRELDF 2.867e-07
I FINAL X(I) D(I) G(I)
1 2.824235e-05 1.000e+00 5.619e+01
2 8.649332e-02 1.000e+00 -5.899e-01
3 9.175397e-01 1.000e+00 -6.866e-01
Call:
garch(x = Delt(dat)[-1], order = c(1, 1))
Coefficient(s):
a0 a1 b1
2.824e-05 8.649e-02 9.175e-01
Warning message:
In sqrt(pred$e) : NaNs produced
What we see that sum of alpha and beta coef is more than 1. Therefore
probably I choose a wrong model on my dataset. Can anyone please guide me
how to modify that model?
Regards,
--
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