在R中使用GARCH模型进行预测

时间：2019-01-20 16:06:58

标签： r dataframe dplyr time-series 

df=structure(list(X = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), 
    json_data.time.updated = structure(1:41, .Label = c("Jan 19, 2019 15:18:00 UTC", 
    "Jan 19, 2019 15:19:00 UTC", "Jan 19, 2019 15:51:00 UTC", 
    "Jan 19, 2019 15:52:00 UTC", "Jan 19, 2019 15:54:00 UTC", 
    "Jan 19, 2019 15:55:00 UTC", "Jan 19, 2019 15:57:00 UTC", 
    "Jan 19, 2019 15:58:00 UTC", "Jan 19, 2019 16:00:00 UTC", 
    "Jan 19, 2019 16:01:00 UTC", "Jan 19, 2019 16:03:00 UTC", 
    "Jan 19, 2019 16:04:00 UTC", "Jan 19, 2019 16:06:00 UTC", 
    "Jan 19, 2019 16:07:00 UTC", "Jan 19, 2019 16:09:00 UTC", 
    "Jan 19, 2019 16:10:00 UTC", "Jan 19, 2019 16:12:00 UTC", 
    "Jan 19, 2019 16:13:00 UTC", "Jan 19, 2019 16:15:00 UTC", 
    "Jan 19, 2019 16:16:00 UTC", "Jan 19, 2019 16:18:00 UTC", 
    "Jan 19, 2019 16:19:00 UTC", "Jan 19, 2019 16:21:00 UTC", 
    "Jan 19, 2019 16:22:00 UTC", "Jan 19, 2019 16:24:00 UTC", 
    "Jan 19, 2019 16:25:00 UTC", "Jan 19, 2019 16:27:00 UTC", 
    "Jan 19, 2019 16:28:00 UTC", "Jan 19, 2019 16:30:00 UTC", 
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    "Jan 19, 2019 16:34:00 UTC", "Jan 19, 2019 16:36:00 UTC", 
    "Jan 19, 2019 16:37:00 UTC", "Jan 19, 2019 16:39:00 UTC", 
    "Jan 19, 2019 16:40:00 UTC", "Jan 19, 2019 16:42:00 UTC", 
    "Jan 19, 2019 16:43:00 UTC", "Jan 19, 2019 16:45:00 UTC", 
    "Jan 19, 2019 16:46:00 UTC", "Jan 19, 2019 16:48:00 UTC"), class = "factor"), 
    json_data.time.updatedISO = structure(1:41, .Label = c("2019-01-19T15:18:00+00:00", 
    "2019-01-19T15:19:00+00:00", "2019-01-19T15:51:00+00:00", 
    "2019-01-19T15:52:00+00:00", "2019-01-19T15:54:00+00:00", 
    "2019-01-19T15:55:00+00:00", "2019-01-19T15:57:00+00:00", 
    "2019-01-19T15:58:00+00:00", "2019-01-19T16:00:00+00:00", 
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    "2019-01-19T16:34:00+00:00", "2019-01-19T16:36:00+00:00", 
    "2019-01-19T16:37:00+00:00", "2019-01-19T16:39:00+00:00", 
    "2019-01-19T16:40:00+00:00", "2019-01-19T16:42:00+00:00", 
    "2019-01-19T16:43:00+00:00", "2019-01-19T16:45:00+00:00", 
    "2019-01-19T16:46:00+00:00", "2019-01-19T16:48:00+00:00"), class = "factor"), 
    json_data.time.updateduk = structure(1:41, .Label = c("Jan 19, 2019 at 15:18 GMT", 
    "Jan 19, 2019 at 15:19 GMT", "Jan 19, 2019 at 15:51 GMT", 
    "Jan 19, 2019 at 15:52 GMT", "Jan 19, 2019 at 15:54 GMT", 
    "Jan 19, 2019 at 15:55 GMT", "Jan 19, 2019 at 15:57 GMT", 
    "Jan 19, 2019 at 15:58 GMT", "Jan 19, 2019 at 16:00 GMT", 
    "Jan 19, 2019 at 16:01 GMT", "Jan 19, 2019 at 16:03 GMT", 
    "Jan 19, 2019 at 16:04 GMT", "Jan 19, 2019 at 16:06 GMT", 
    "Jan 19, 2019 at 16:07 GMT", "Jan 19, 2019 at 16:09 GMT", 
    "Jan 19, 2019 at 16:10 GMT", "Jan 19, 2019 at 16:12 GMT", 
    "Jan 19, 2019 at 16:13 GMT", "Jan 19, 2019 at 16:15 GMT", 
    "Jan 19, 2019 at 16:16 GMT", "Jan 19, 2019 at 16:18 GMT", 
    "Jan 19, 2019 at 16:19 GMT", "Jan 19, 2019 at 16:21 GMT", 
    "Jan 19, 2019 at 16:22 GMT", "Jan 19, 2019 at 16:24 GMT", 
    "Jan 19, 2019 at 16:25 GMT", "Jan 19, 2019 at 16:27 GMT", 
    "Jan 19, 2019 at 16:28 GMT", "Jan 19, 2019 at 16:30 GMT", 
    "Jan 19, 2019 at 16:31 GMT", "Jan 19, 2019 at 16:33 GMT", 
    "Jan 19, 2019 at 16:34 GMT", "Jan 19, 2019 at 16:36 GMT", 
    "Jan 19, 2019 at 16:37 GMT", "Jan 19, 2019 at 16:39 GMT", 
    "Jan 19, 2019 at 16:40 GMT", "Jan 19, 2019 at 16:42 GMT", 
    "Jan 19, 2019 at 16:43 GMT", "Jan 19, 2019 at 16:45 GMT", 
    "Jan 19, 2019 at 16:46 GMT", "Jan 19, 2019 at 16:48 GMT"), class = "factor"), 
    code = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L), .Label = "USD", class = "factor"), rate = structure(1:41, .Label = c("3,735.7750", 
    "3,735.9150", "3,736.9100", "3,735.3200", "3,736.7717", "3,736.0750", 
    "3,734.9600", "3,734.9117", "3,734.2833", "3,734.4950", "3,735.8533", 
    "3,736.1917", "3,735.5450", "3,735.5867", "3,736.0617", "3,736.3417", 
    "3,737.0633", "3,736.9583", "3,737.1667", "3,737.1433", "3,737.0583", 
    "3,736.9283", "3,737.6383", "3,737.5167", "3,737.9133", "3,738.7533", 
    "3,738.6767", "3,738.5767", "3,738.5917", "3,738.8867", "3,739.6333", 
    "3,739.9600", "3,739.3383", "3,739.9267", "3,739.3067", "3,739.5867", 
    "3,739.6567", "3,739.4267", "3,739.1500", "3,739.8817", "3,739.5550"
    ), class = "factor"), description = structure(c(1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "United States Dollar", class = "factor"), 
    rate_float = structure(1:41, .Label = c("3735.775", "3735.915", 
    "3736.91", "3735.32", "3736.7717", "3736.075", "3734.96", 
    "3734.9117", "3734.2833", "3734.495", "3735.8533", "3736.1917", 
    "3735.545", "3735.5867", "3736.0617", "3736.3417", "3737.0633", 
    "3736.9583", "3737.1667", "3737.1433", "3737.0583", "3736.9283", 
    "3737.6383", "3737.5167", "3737.9133", "3738.7533", "3738.6767", 
    "3738.5767", "3738.5917", "3738.8867", "3739.6333", "3739.96", 
    "3739.3383", "3739.9267", "3739.3067", "3739.5867", "3739.6567", 
    "3739.4267", "3739.15", "3739.8817", "3739.555"), class = "factor")), row.names = c(NA, 
41L), class = "data.frame")

我想执行GARCH模型，所以我有这段代码

df$rate_float=as.numeric(df$rate_float)
library("fGarch")
b=garchFit(formula = ~ garch(1, 1), data = df$rate_float, 
         init.rec = c( "mci","uev"), 
         delta = 2, skew = 1, shape = 4, 
         cond.dist = c("norm", "snorm", "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"), control = list(), 
         title = NULL, description = NULL)

garchKappa(cond.dist = c("norm", "ged", "std", "snorm", "sged", "sstd",
                         "snig"), gamma = 0, delta = 2, skew = NA, shape = NA)

因此，时间变量为json_data.time.updateduk。 我如何在10分钟前创建预测？ 其次，但相关的问题，我如何获取初始值的预测以了解预测的质量 所以作为输出我需要 1.像这样预测10分钟 enter image description here

  1. 残差 即像这样的东西 enter image description here

如何做到？

1 个答案:

答案 0 :(得分：2)

您可以预测前进10步

predict(b, n.ahead = 10)
#    meanForecast meanError standardDeviation
# 1      20.19952  20.82351          20.82351
# 2      20.19952  20.84651          20.84651
# 3      20.19952  20.86948          20.86948
# 4      20.19952  20.89243          20.89243
# 5      20.19952  20.91535          20.91535
# 6      20.19952  20.93825          20.93825
# 7      20.19952  20.96112          20.96112
# 8      20.19952  20.98396          20.98396
# 9      20.19952  21.00679          21.00679
# 10     20.19952  21.02959          21.02959

而拟合值由

给出 
fitted(b)
#        1        2        3        4        5        6        7        8        9       10 
# 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 
#       11       12       13       14       15       16       17       18       19       20 
# 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 
#       21       22       23       24       25       26       27       28       29       30 
# 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 
#       31       32       33       34       35       36       37       38       39       40 
# 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 20.19952 
#       41 
# 20.19952

由于要对条件方差建模，因此平均预测值和拟合值是恒定的；因此，预测的其他两列不是恒定的。拟合的条件标准偏差由

给出 
b@sigma.t
#  [1] 11.8995423 19.2244571 18.2258257 17.2273530 16.2290683 15.2310087 14.2332215 13.2357682
#  [9] 12.2387307 11.2422194 10.2463878  9.2514556  8.2577478  7.2657667  6.2763312  5.2908696
# [17]  4.3121073  3.3459283  2.4075307  1.5482802  0.9990692  1.2645580  2.0494083  2.9666550
# [25]  3.9245394  4.8992833  5.8825118  6.8705831  7.8616716  8.8547641  9.8492545 10.8447583
# [33] 11.8410198 12.8378627 13.8351612 14.8328235 15.8307808 16.8289806 17.8273822 18.8259534
# [41] 19.8246687
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