#TSLA vs GM
tsla<-getSymbols("TSLA",auto.assign =FALSE,from = "2011-09-01", to = "2021-09-01") #as my train
## '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.
head(tsla)
## TSLA.Open TSLA.High TSLA.Low TSLA.Close TSLA.Volume TSLA.Adjusted
## 2011-09-01 4.932 4.974 4.768 4.800 4240500 4.800
## 2011-09-02 4.732 4.798 4.536 4.614 3849500 4.614
## 2011-09-06 4.500 4.640 4.458 4.588 4049000 4.588
## 2011-09-07 4.678 4.800 4.656 4.768 2296000 4.768
## 2011-09-08 4.716 4.806 4.656 4.722 2528500 4.722
## 2011-09-09 4.674 4.714 4.510 4.594 3346500 4.594
plot(tsla)
tslaClose<-tsla$TSLA.Close
plot(tslaClose)
gm<-getSymbols("GM",auto.assign =FALSE,from = "2011-09-01", to = "2021-09-01") #as my train
head(gm)
## GM.Open GM.High GM.Low GM.Close GM.Volume GM.Adjusted
## 2011-09-01 24.09 24.25 22.91 23.03 16926200 17.79974
## 2011-09-02 22.41 22.55 21.73 22.07 14086700 17.05777
## 2011-09-06 21.36 21.58 20.88 21.44 14282500 16.57084
## 2011-09-07 21.82 23.04 21.82 22.86 13412200 17.66835
## 2011-09-08 22.79 23.13 22.24 22.48 11782500 17.37465
## 2011-09-09 22.36 22.45 21.47 21.76 11920600 16.81817
plot(gm)
gmClose<-gm$GM.Close
plot(gmClose)
#time series
tsla.ts<-ts(tsla$TSLA.Close,frequency=365)
autoplot(tsla.ts)+
labs(title="TSLA daily stock price 2011-09-01 to 2021-09-01")
gm.ts<-ts(gm$GM.Close,frequency=365)
autoplot(gm.ts)+
labs(title="General Motors daily stock price 2011-09-01 to 2021-09-01")
md<-data.frame(tsla$TSLA.Close,gm$GM.Close)
md.ts<-ts(md,frequency=365)
md.ts%>%
autoplot()+
labs(title="TSLA vs GM 2011-09-01 to 2021-09-01",ylab="Price in $")
#Differencing
acf(tsla.ts)
acf(gm.ts)
unitroot_ndiffs(tsla.ts)
## ndiffs
## 2
#ndiffs=2
unitroot_ndiffs(gm.ts)
## ndiffs
## 1
#ndiffs=1
acf(diff(tsla.ts,differences = 2))
acf(diff(gm.ts,differences=1))
VARselect(md.ts,lag.max=10,type="const")$selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 10 10 1 10
#AIC(n) HQ(n) SC(n) FPE(n)
#10 10 1 10
#it indicates choosing 10 as the lag number
varfit<-VAR(md.ts,p=10,type="const")
summary(varfit)
##
## VAR Estimation Results:
## =========================
## Endogenous variables: TSLA.Close, GM.Close
## Deterministic variables: const
## Sample size: 2506
## Log Likelihood: -11350.364
## Roots of the characteristic polynomial:
## 1.001 0.9936 0.8131 0.8131 0.7985 0.7886 0.7886 0.7848 0.7848 0.7801 0.7801 0.7396 0.7396 0.7029 0.7029 0.6621 0.6524 0.6524 0.6131 0.6131
## Call:
## VAR(y = md.ts, p = 10, type = "const")
##
##
## Estimation results for equation TSLA.Close:
## ===========================================
## TSLA.Close = TSLA.Close.l1 + GM.Close.l1 + TSLA.Close.l2 + GM.Close.l2 + TSLA.Close.l3 + GM.Close.l3 + TSLA.Close.l4 + GM.Close.l4 + TSLA.Close.l5 + GM.Close.l5 + TSLA.Close.l6 + GM.Close.l6 + TSLA.Close.l7 + GM.Close.l7 + TSLA.Close.l8 + GM.Close.l8 + TSLA.Close.l9 + GM.Close.l9 + TSLA.Close.l10 + GM.Close.l10 + const
##
## Estimate Std. Error t value Pr(>|t|)
## TSLA.Close.l1 0.92642 0.02006 46.191 < 2e-16 ***
## GM.Close.l1 0.49108 0.23024 2.133 0.033032 *
## TSLA.Close.l2 0.04910 0.02735 1.796 0.072693 .
## GM.Close.l2 -0.14034 0.32459 -0.432 0.665530
## TSLA.Close.l3 0.09816 0.02731 3.594 0.000332 ***
## GM.Close.l3 -0.12427 0.32411 -0.383 0.701444
## TSLA.Close.l4 -0.04044 0.02738 -1.477 0.139792
## GM.Close.l4 -0.76954 0.32363 -2.378 0.017488 *
## TSLA.Close.l5 -0.13992 0.02726 -5.133 3.06e-07 ***
## GM.Close.l5 0.83360 0.32397 2.573 0.010138 *
## TSLA.Close.l6 0.12743 0.02723 4.679 3.03e-06 ***
## GM.Close.l6 -0.58861 0.32443 -1.814 0.069749 .
## TSLA.Close.l7 -0.02486 0.02736 -0.908 0.363743
## GM.Close.l7 0.70442 0.32477 2.169 0.030178 *
## TSLA.Close.l8 0.07090 0.02735 2.593 0.009575 **
## GM.Close.l8 -0.96801 0.32519 -2.977 0.002941 **
## TSLA.Close.l9 0.03411 0.02738 1.246 0.212889
## GM.Close.l9 1.17025 0.32640 3.585 0.000343 ***
## TSLA.Close.l10 -0.09861 0.02019 -4.884 1.11e-06 ***
## GM.Close.l10 -0.65441 0.23115 -2.831 0.004677 **
## const 1.59109 0.85187 1.868 0.061912 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 7.928 on 2485 degrees of freedom
## Multiple R-Squared: 0.9982, Adjusted R-squared: 0.9981
## F-statistic: 6.721e+04 on 20 and 2485 DF, p-value: < 2.2e-16
##
##
## Estimation results for equation GM.Close:
## =========================================
## GM.Close = TSLA.Close.l1 + GM.Close.l1 + TSLA.Close.l2 + GM.Close.l2 + TSLA.Close.l3 + GM.Close.l3 + TSLA.Close.l4 + GM.Close.l4 + TSLA.Close.l5 + GM.Close.l5 + TSLA.Close.l6 + GM.Close.l6 + TSLA.Close.l7 + GM.Close.l7 + TSLA.Close.l8 + GM.Close.l8 + TSLA.Close.l9 + GM.Close.l9 + TSLA.Close.l10 + GM.Close.l10 + const
##
## Estimate Std. Error t value Pr(>|t|)
## TSLA.Close.l1 0.0013976 0.0017581 0.795 0.426717
## GM.Close.l1 0.9993499 0.0201832 49.514 < 2e-16 ***
## TSLA.Close.l2 0.0005292 0.0023973 0.221 0.825300
## GM.Close.l2 -0.0186527 0.0284540 -0.656 0.512181
## TSLA.Close.l3 0.0008139 0.0023940 0.340 0.733897
## GM.Close.l3 0.0181460 0.0284115 0.639 0.523088
## TSLA.Close.l4 -0.0008107 0.0024002 -0.338 0.735571
## GM.Close.l4 -0.0215986 0.0283692 -0.761 0.446527
## TSLA.Close.l5 -0.0041278 0.0023894 -1.728 0.084191 .
## GM.Close.l5 0.0137470 0.0283993 0.484 0.628385
## TSLA.Close.l6 -0.0004049 0.0023873 -0.170 0.865331
## GM.Close.l6 -0.0229991 0.0284392 -0.809 0.418760
## TSLA.Close.l7 0.0049535 0.0023985 2.065 0.039005 *
## GM.Close.l7 0.0837202 0.0284693 2.941 0.003305 **
## TSLA.Close.l8 -0.0006813 0.0023971 -0.284 0.776269
## GM.Close.l8 -0.0968468 0.0285060 -3.397 0.000691 ***
## TSLA.Close.l9 -0.0010076 0.0023999 -0.420 0.674629
## GM.Close.l9 0.0751603 0.0286122 2.627 0.008670 **
## TSLA.Close.l10 -0.0003697 0.0017700 -0.209 0.834561
## GM.Close.l10 -0.0379867 0.0202630 -1.875 0.060953 .
## const 0.2516635 0.0746750 3.370 0.000763 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 0.695 on 2485 degrees of freedom
## Multiple R-Squared: 0.9926, Adjusted R-squared: 0.9925
## F-statistic: 1.662e+04 on 20 and 2485 DF, p-value: < 2.2e-16
##
##
##
## Covariance matrix of residuals:
## TSLA.Close GM.Close
## TSLA.Close 62.8512 0.6342
## GM.Close 0.6342 0.4830
##
## Correlation matrix of residuals:
## TSLA.Close GM.Close
## TSLA.Close 1.0000 0.1151
## GM.Close 0.1151 1.0000