arma(1,1) kospi

library(quantmod)
library(fGarch)

URL <- "http://ichart.finance.yahoo.com/table.csv?s=^KS11"
dat <- read.csv(URL)
dat$Date <- as.Date(dat$Date, "%Y-%m-%d")

odat<-dat[c(1:100),]

attach(odat)

x<-odat[order(Date),]

fit <- arima(diff(x$Close), order=c(1,0,2))

err <- rnorm(30, 0, sd(diff(x$Close)))

sim <- diff(x$Close)[99]

for (t in 2:30) {
    sim[t] <- fit$coef[1]*sim[t-1] + fit$coef[2]*err[t-1]*0.5
            + fit$coef[3]*err[t-2]*0.5
}

par(mfrow=c(3,1))
par(mar=c(3,3,1,2))

x$newday <- as.Date(x$Date, "%m/%d/%Y")
plot(x$newday, x$Close, type='l', col='red',
    panel.first=grid(col = "gray", lty = "dotted"), pch=20)
points(x$newday, x$Close, pch = 20, col = "red")

ye <- x$Close[100]
td<-x$newday[length(x$newday)]+1
for (j in 2:30) {
td[j] <- x$newday[length(x$newday)] + j

ye[j] <- sim[j-1]+ye[j-1]
}

td<-as.Date(td)
points(td, ye, col='red', pch=20, type='b')

plot(diff(x$Close), type='l', col=heat.colors(32)[15],panel.first=grid(col = "gray", lty = "dotted"))
##########################################################

library(forecast)

fit <- auto.arima(x$Close)
fore <- forecast(fit, h=10)
plot(fore)

ye