we will access the stock price information of AAPL and analyze the performance

We will obtain the price data of AAPLstock from Yahoo! -R Finance for the given time period

 plot(ni.garch11$zx, ni.garch11$zy, type="l", lwd=2,col="blue",main="GARCH(1,1)-newsimpact", ylab=ni.garch11$yexpr, xlab=ni.garch11$xexpr)

egarch11.spec = ugarchspec(variance.model = list(model="eGARCH",

+ garchOrder=c(1,1)), mean.model = list(armaOrder=c(0,0)))

> aapl.egarch11.fit = ugarchfit(spec=egarch11.spec, data=ret.aapl)

> coef(aapl.egarch11.fit)

          mu        omega       alpha1 

 0.001355199 -0.313404827 -0.107024135 

       beta1       gamma1 

 0.960077271  0.174988083 

 "AAPL"

> ret.aapl <- dailyReturn(Cl(AAPL), type='log')

> chartSeries(ret.aapl)

> chartSeries(Cl(AAPL))

> addRSI()

> tail(AAPL)

           AAPL.Open AAPL.High AAPL.Low

2020-01-16    313.59    315.70   312.09

2020-01-17    316.27    318.74   315.00

2020-01-21    317.19    319.02   316.00

2020-01-22    318.58    319.99   317.31

2020-01-23    317.92    319.56   315.65

2020-01-24    320.25    323.33   317.52

           AAPL.Close AAPL.Volume

2020-01-16     315.24    27207300

2020-01-17     318.73    34454100

2020-01-21     316.57    27710800

2020-01-22     317.70    25458100

2020-01-23     319.23    26118000

2020-01-24     318.31    36600500

           AAPL.Adjusted

2020-01-16        315.24

2020-01-17        318.73

2020-01-21        316.57

2020-01-22        317.70

2020-01-23        319.23

2020-01-24        318.31

aapl_stock<- new.env()
> getSymbols("AAPL", env = aapl_stock, src = "yahoo", from = as.Date("2010-01-01"), to = as.Date("2019-08-12"))
[1] "AAPL"

we need to define the character vector

AAPL<-aapl_stock$AAPL
> head(AAPL)
           AAPL.Open AAPL.High AAPL.Low
2010-01-04  30.49000  30.64286 30.34000
2010-01-05  30.65714  30.79857 30.46429
2010-01-06  30.62571  30.74714 30.10714
2010-01-07  30.25000  30.28571 29.86429
2010-01-08  30.04286  30.28571 29.86572
2010-01-11  30.40000  30.42857 29.77857
           AAPL.Close AAPL.Volume
2010-01-04   30.57286   123432400
2010-01-05   30.62571   150476200
2010-01-06   30.13857   138040000
2010-01-07   30.08286   119282800
2010-01-08   30.28286   111902700
2010-01-11   30.01572   115557400
           AAPL.Adjusted
2010-01-04      26.68133
2010-01-05      26.72746
2010-01-06      26.30233
2010-01-07      26.25370
2010-01-08      26.42825
2010-01-11      26.19511
> chartSeries(AAPL,multi.col=TRUE,theme="white")
> addMACD()
> addBBands()

> AAPL_return <-
+     log(AAPL$AAPL.Close/AAPL$AAPL.Open)
> qqnorm(AAPL_return, main = "Normal Q-Q Plot of AAPL daily log return",
+        xlab = "Theoretical Quantiles",
+        ylab = "Sample Quantiles", plot.it = TRUE, datax = FALSE
+ )
> qqline(AAPL_return, col="red")