Winners

library(survival)
d<-read.csv("SCOM.csv");d
Max<-d$MAX.PRICE;Max
min=d$MIN.PRICE;min
closing=d$Closing.Price;closing
hist(Max)
hist(min)
hist(closing)
barplot(Max)
barplot(min)
barplot(closing)
# regression
x=d$MAX.PRICE
y=d$MIN.PRICE
z=d$Closing.Price
lm(x~y)
lm(y~z)
lm(x~z)
h=d$volume
lm(x~y+z)
summary(lm(x~y+z))
#correlation
cor(x,y)
cor(y,z)
cor(x,z)
library(linearModel)
aov(lm(y~x))
summary(lm(y~x))
plot(lm(y~x))
hist(x)
install.packages("fGarch")
library(fGarch)
library(timeSeries)
library(statBasics)  
ts.plot(z)
log.z=diff(z);log.z
plot(log.z)
ts.plot(log.z)
acf(log.z)
pacf(log.z)
s=log.z^2;s
m2=garchFit(~garch(1,1),data=log.z,trace=F);m2
summary(m2)
plot(m2)
parmfrow=c(2,2)

D3 = diff(z);D3
library(rugarch)
library(forecast)
m1=ugarchspec(variance.model=list(model="eGARCH",garchOrder=c(1,1)),
              mean.model=list(armaOrder=c(0,0),include.mean=F),
              distribution.model="norm");m1
m1fit=ugarchfit(D3,spec=m1);m1fit
summary(m1fit)
plot(m1fit)
#GJR
m2=ugarchspec(variance.model=list(model="gjrGARCH",garchOrder=c(1,1)),
              mean.model=list(armaOrder=c(0,0),include.mean=F),
              distribution.model="ged");m2
m2fit=ugarchfit(D3,spec=m2);m2fit
summary(m2fit)
plot(m2fit)
#forecasting
f=ugarchforecast(m2fit,n.ahead=120);f
plot(f,which=1)