Discussion 6

library(forecast)

## Warning: package 'forecast' was built under R version 3.4.2

library(tseries)

## Warning: package 'tseries' was built under R version 3.4.3

library(xts)

## Warning: package 'xts' was built under R version 3.4.3

## Loading required package: zoo

## Warning: package 'zoo' was built under R version 3.4.3

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

library(readr)
fbdata<-read_csv("~/Dropbox/Boston College/Predictive Analytics/FB.csv")

## Parsed with column specification:
## cols(
##   Date = col_character(),
##   Open = col_double(),
##   High = col_double(),
##   Low = col_double(),
##   Close = col_double(),
##   `Adj Close` = col_double(),
##   Volume = col_integer()
## )

myts<-ts(fbdata$`Adj Close`, frequency=252, start=c(2013,1))

plot(myts, ylab="Adj Close")

#Train and test set
train<-myts[1:1008]
test<-myts[1009:1260]

We will compare the Garch model to the ARIMA model with no regressor since it performed best last week:

myarima<-auto.arima(train)
forecast<-forecast(myarima, h=252)
accuracy<-accuracy(forecast, test)
accuracy

##                         ME      RMSE       MAE        MPE     MAPE
## Training set -0.0001940347  1.491104  1.038689 -0.0275405 1.363181
## Test set     12.6790085282 15.420789 13.869541  7.2571026 8.019694
##                    MASE         ACF1
## Training set  0.9930499 -0.001307305
## Test set     13.2601279           NA

plot(forecast)

Now fit the Garch model:

library(fGarch)

## Warning: package 'fGarch' was built under R version 3.4.2

## Loading required package: timeDate

## Warning: package 'timeDate' was built under R version 3.4.3

## Loading required package: timeSeries

## Warning: package 'timeSeries' was built under R version 3.4.2

## 
## Attaching package: 'timeSeries'

## The following object is masked from 'package:zoo':
## 
##     time<-

## Loading required package: fBasics

## Warning: package 'fBasics' was built under R version 3.4.2

mygarch<-garch(train, trace=FALSE)
1

## [1] 1

plot(mygarch)

#forecast.garch<-forecast(mygarch, n.ahead=252)
#accuracy2<-accuracy(forecast.garch, test)
#accuracy2
#plot(forecast.garch)

I could not figure out how to forecast this garch model and kept recieving an error when using the forecast function. I will keep working on it later this week and hopefully post my results in here, but for now this is all I have.