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在 R 中估计 GARCH 参数存在的问题(三)
2019-08-15 00:10:00 】 浏览:523
Tags:估计 GARCH 参数 存在 问题

;ged", "sged", "std", "sstd", "snig", "QMLE"), include.mean = TRUE, ## include.delta = NULL, include.skew = NULL, include.shape = NULL, ## leverage = NULL, trace = TRUE, algorithm = c("nlminb", "lbfgsb", ## "nlminb+nm", "lbfgsb+nm"), hessian = c("ropt", "rcd"), ## control = list(), title = NULL, description = NULL, ...) ## NULL

该函数提供了一些选项,要最大化的分布(cond.dist)和用于优化的算法(algorithm)。除非另有说明,否则我将始终选择 cond.dist = QMLE 来指示函数使用 QMLE 估算器。

这是一次调用。

garchFit(
    data = x, cond.dist = "QMLE", include.mean = FALSE)
##
## Series Initialization:
##  ARMA Model:                arma
##  Formula Mean:              ~ arma(0, 0)
##  GARCH Model:               garch
##  Formula Variance:          ~ garch(1, 1)
##  ARMA Order:                0 0
##  Max ARMA Order:            0
##  GARCH Order:               1 1
##  Max GARCH Order:           1
##  Maximum Order:             1
##  Conditional Dist:          QMLE
##  h.start:                   2
##  llh.start:                 1
##  Length of Series:          1000
##  Recursion Init:            mci
##  Series Scale:              0.5320977
##
## Parameter Initialization:
##  Initial Parameters:          $params
##  Limits of Transformations:   $U, $V
##  Which Parameters are Fixed?  $includes
##  Parameter Matrix:
##                      U          V params includes
##     mu     -0.15640604   0.156406    0.0    FALSE
##     omega   0.00000100 100.000000    0.1     TRUE
##     alpha1  0.00000001   1.000000    0.1     TRUE
##     gamma1 -0.99999999   1.000000    0.1    FALSE
##     beta1   0.00000001   1.000000    0.8     TRUE
##     delta   0.00000000   2.000000    2.0    FALSE
##     skew    0.10000000  10.000000    1.0    FALSE
##     shape   1.00000000  10.000000    4.0    FALSE
##  Index List of Parameters to be Optimized:
##  omega alpha1  beta1
##      2      3      5
##  Persistence:                  0.9
##
##
## --- START OF TRACE ---
## Selected Algorithm: nlminb
##
## R coded nlminb Solver:
##
##   0:     1419.0152: 0.100000 0.100000 0.800000
##   1:     1418.6616: 0.108486 0.0998447 0.804683
##   2:     1417.7139: 0.109746 0.0909961 0.800931
##   3:     1416.7807: 0.124977 0.0795152 0.804400
##   4:     1416.7215: 0.141355 0.0446605 0.799891
##   5:     1415.5139: 0.158059 0.0527601 0.794304
##   6:     1415.2330: 0.166344 0.0561552 0.777108
##   7:     1415.0415: 0.195230 0.0637737 0.743465
##   8:     1415.0031: 0.200862 0.0576220 0.740088
##   9:     1414.9585: 0.205990 0.0671331 0.724721
##  10:     1414.9298: 0.219985 0.0713468 0.712919
##  11:     1414.8226: 0.230628 0.0728325 0.697511
##  12:     1414.4689: 0.325750 0.0940514 0.583114
##  13:     1413.4560: 0.581449 0.143094 0.281070
##  14:     1413.2804: 0.659173 0.157127 0.189282
##  15:     1413.2136: 0.697840 0.155964 0.150319
##  16:     1413.1467: 0.720870 0.142550 0.137645
##  17:     1413.1416: 0.726527 0.138146 0.135966
##  18:     1413.1407: 0.728384 0.137960 0.134768
##  19:     1413.1392: 0.731725 0.138321 0.132991
##  20:     1413.1392: 0.731146 0.138558 0.133590
##  21:     1413.1392: 0.730849 0.138621 0.133850
##  22:     1413.1392: 0.730826 0.138622 0.133869
##
## Final Estimate of the Negative LLH:
##  LLH:  782.211    norm LLH:  0.782211
##     omega    alpha1     beta1
## 0.2069173 0.1386221 0.1338686
##
## R-optimhess Difference Approximated Hessian Matrix:
##            omega     alpha1      beta1
## omega  -8858.897 -1839.6144 -2491.9827
## alpha1 -1839.614  -782.8005  -531.7393
## beta1  -2491.983  -531.7393  -729.7246
## attr(,"time")
## Time difference of 0.04132652 secs
##
## --- END OF TRACE ---
##
##
## Time to Estimate Parameters:
##  Time difference of 0.3866439 secs
##
## Title:
##  GARCH Modelling
##
## Call:
##  garchFit(data = x, cond.dist = "QMLE", include.mean = FALSE)
##
## Mean and Variance Equation:
##  data ~ garch(1, 1)
  
		

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在 R 中估计 GARCH 参数存在的问题(三) https://www.cppentry.com/bencandy.php?fid=91&id=228912

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