How to analyze NSEI

How to analyze NSEI?

New update New NSEI analysis

What is NSEI?

NSEI is the symbol of the National Stock Exchange of India. we can plot stocks.

The National Stock Exchange of India (NSEI)  is India's leading stock exchange.

getSymbols("^NSEI", from="2004-01-01", to=Sys.Date())
[1] "^NSEI" 
> chartSeries(Cl(^NSEI))

> chartSeries(Cl(NSEI))
> ret1 <- dailyReturn(Cl(NSEI), type='log')
> par(mfrow=c(2,2))
> acf(ret1, main="Return ACF");
> pacf(ret1, main="Return PACF");
> acf(ret1^2, main="Squared return ACF");
> pacf(ret1^2, main="Squared return PACF")

> par(mfrow=c(1,1))
> m=mean(ret1);s=sd(ret1);
> par(mfrow=c(1,2))
> hist(ret1, nclass=40, freq=FALSE, main='Return histogram');curve(dnorm(x,mean=m,sd=s), from = -0.3, to = 0.2, add=TRUE, col="red")
> plot(density(ret1), main='Return empirical distribution');curve(dnorm(x,mean=m,sd=s), from = -0.3, to = 0.2, add=TRUE, col="red")
> par(mfrow=c(1,1))
> kurtosis(ret1)
daily.returns

Daily return NSEI 
     16.72019 
> plot(density(ret1), main='Return EDF - upper tail', xlim = c(0.1, 0.2),ylim=c(0,2));
> curve(dnorm(x, mean=m,sd=s), from = -0.3, to = 0.2, add=TRUE, col="red")
> plot(density(ret1), xlim=c(-5*s,5*s),log='y', main='Density on log-scale')

> curve(dnorm(x, mean=m,sd=s), from=-5*s, to=5*s, log="y", add=TRUE,col="red")
> qqnorm(ret);qqline(ret);
> chartSeries(ret1)

> garch11.spec = ugarchspec(variance.model = list(model="sGARCH",garchOrder=c(1,1)), mean.model = list(armaOrder=c(0,0)))
> aapl.garch11.fit = ugarchfit(spec=garch11.spec, data=ret1)
> nsei.garch11.fit = ugarchfit(spec=garch11.spec, data=ret1)
> coef(nsei.garch11.fit)
          mu        omega       alpha1 
7.162159e-04 1.511431e-06 9.719140e-02 
       beta1 
8.980645e-01 

Co varience in NSEI
> vcov(nsei.garch11.fit) 
              [,1]          [,2]          [,3]
[1,]  2.853286e-08  2.282690e-11  3.746905e-07
[2,]  2.282690e-11  1.474203e-12  1.604714e-08
[3,]  3.746905e-07  1.604714e-08  2.671834e-04
[4,] -3.647685e-07 -1.660605e-08 -2.513096e-04
              [,4]
[1,] -3.647685e-07
[2,] -1.660605e-08
[3,] -2.513096e-04
[4,]  2.461915e-04
> uncvariance(nsei.garch11.fit) 
[1] 0.0003185922
> uncmean(nsei.garch11.fit) 
[1] 0.0007162159
> infocriteria(nsei.garch11.fit) 
                      
Akaike       -6.128187
Bayes        -6.120506
Shibata      -6.128190
Hannan-Quinn -6.125431