how to put external regressors in DCC model in R studio

General
9

There are several problems with your code.

  1. This is not a reprex. You have not included the calls to the libraries. Please go through the following link. A reproducible example helps others to help you.

  1. As I said earlier, you don't need matrix(cbind(rIBM,rBP), ncol = 2). If cbind(rIBM, rBP) is good enough, why will you do the extra typing? Moreover, you confused with the brackets and your code read the distribution.model, start.pars and fixed.pars under mean.model, which is not the case.

  2. names(rx) <- c("rIBM", "rBP", "GOOG", "rt") would have been enough.

  3. Please format your code before uploading. It helps.

I modified your code and got the following. I don't have knowledge on DCC models, so can't interpret the results or can't say whether it's right or wrong. You'll have to judge that yourself.

library(quantmod)
#> Loading required package: xts
#> Loading required package: zoo
#> 
#> Attaching package: 'zoo'
#> The following objects are masked from 'package:base':
#> 
#>     as.Date, as.Date.numeric
#> Loading required package: TTR
#> Version 0.4-0 included new data defaults. See ?getSymbols.
library(rmgarch)
#> Loading required package: rugarch
#> Loading required package: parallel
#> 
#> Attaching package: 'rugarch'
#> The following object is masked from 'package:stats':
#> 
#>     sigma
#> 
#> Attaching package: 'rmgarch'
#> The following objects are masked from 'package:xts':
#> 
#>     first, last
startDate <- as.Date("2007-01-03")
endDate <- as.Date("2019-02-28")
getSymbols("IBM", from = startDate, to = endDate)
#> 'getSymbols' currently uses auto.assign=TRUE by default, but will
#> use auto.assign=FALSE in 0.5-0. You will still be able to use
#> 'loadSymbols' to automatically load data. getOption("getSymbols.env")
#> and getOption("getSymbols.auto.assign") will still be checked for
#> alternate defaults.
#> 
#> This message is shown once per session and may be disabled by setting 
#> options("getSymbols.warning4.0"=FALSE). See ?getSymbols for details.
#> 
#> WARNING: There have been significant changes to Yahoo Finance data.
#> Please see the Warning section of '?getSymbols.yahoo' for details.
#> 
#> This message is shown once per session and may be disabled by setting
#> options("getSymbols.yahoo.warning"=FALSE).
#> [1] "IBM"
getSymbols("GOOG", from = startDate, to = endDate)
#> [1] "GOOG"
getSymbols("BP", from = startDate, to = endDate)
#> [1] "BP"
getSymbols("T", from = startDate, to = endDate)
#> [1] "T"
head(IBM)
#>            IBM.Open IBM.High IBM.Low IBM.Close IBM.Volume IBM.Adjusted
#> 2007-01-03    97.18    98.40   96.26     97.27    9196800     70.11556
#> 2007-01-04    97.25    98.79   96.88     98.31   10524500     70.86525
#> 2007-01-05    97.60    97.95   96.91     97.42    7221300     70.22369
#> 2007-01-08    98.50    99.50   98.35     98.90   10340000     71.29050
#> 2007-01-09    99.08   100.33   99.07    100.07   11108200     72.13392
#> 2007-01-10    98.50    99.05   97.93     98.89    8744800     71.28333
str(IBM)
#> An 'xts' object on 2007-01-03/2019-02-27 containing:
#>   Data: num [1:3059, 1:6] 97.2 97.2 97.6 98.5 99.1 ...
#>  - attr(*, "dimnames")=List of 2
#>   ..$ : NULL
#>   ..$ : chr [1:6] "IBM.Open" "IBM.High" "IBM.Low" "IBM.Close" ...
#>   Indexed by objects of class: [Date] TZ: UTC
#>   xts Attributes:  
#> List of 2
#>  $ src    : chr "yahoo"
#>  $ updated: POSIXct[1:1], format: "2019-03-12 18:45:37"
chartSeries(IBM)


rIBM <- dailyReturn(IBM)
rBP <- dailyReturn(BP)
rGOOG <- dailyReturn(GOOG)
rt <- dailyReturn(T)
chartSeries(rIBM)


rx <- data.frame(rIBM, rBP, rGOOG, rt)
names(rx) <- c("rIBM", "rBP", "GOOG", "rt")
nspec <- ugarchspec(variance.model = list(model = "sGARCH",
                                          garchOrder = c(1, 1),
                                          submodel = NULL,
                                          external.regressors = matrix(cbind(rIBM, rBP), ncol = 2),
                                          variance.targeting = FALSE),
                    mean.model = list(armaOrder = c(0, 0),
                                      external.regressors = cbind(rIBM, rBP)),
                    distribution.model = "norm",
                    start.pars = list(),
                    fixed.pars = list())
nspec2 <- multispec(replicate(4, nspec))
ncl1 <- makePSOCKcluster(4)
nmultft <- multifit(nspec2, rx, cluster = ncl1)
(nspecmv <- dccspec(uspec = nspec2,
                    dccOrder = c(1, 1),
                    external.regressors = cbind(rIBM, rBP),
                    distribution = 'mvnorm'))
#> 
#> *------------------------------*
#> *       DCC GARCH Spec         *
#> *------------------------------*
#> Model          :  DCC(1,1)
#> Estimation     :  2-step
#> Distribution   :  mvnorm
#> No. Parameters :  40
#> No. Series     :  4

Hope this helps.