Assignment no 6

R Statistical Tool Assignment-

This class on 12th of Feb was basically conceptual with understanding of concepts like taking a time series data and 

-Finding its returns;
-Conducting a ACF plot to check the stationarity of the Data; 
-Analysing the data through Augmented Dickey Fuller -test. 
-Calculating the historical Volatility and standard deviation of a data set
-standardizing a given data set.

Assignment: 
Create log of returns data  and calculate its historical volatility
Formulae: 
1) logSt-logSt-1/logSt-1
OR
2) log(St-St-1/St-1)
Create ACF Plot for log returns and do the ADF test and analyse on it
Data is as follows:
NSE Index –Jan 2012 –Jan 2013
NIFTY data –Closing prices

Commands:-

> niftychart<-read.csv(file.choose(),header=T)
> closingval<-niftychart$Close

> closingval.ts<-ts(closingval,frequency=252)

> plot(log( closingval.ts))

> minusone.ts<-lag(closingval.ts,K=-1)

> plot(log( minusone.ts))

> z<-log(closingval.ts)-log(minusone.ts)

> z

> returns<-z/log(minusone.ts)

> plot(returns,main="Plot of Log Returns;CNX NSE Nifty Jan-2012 to Jan-2013" )

> acf(returns,main=" The Auto Correlation Plot;   Dotted line shows 95% confidence interval ")

The ACF plot shows that all the correlations lie within our expectations of a 95% confidence interval so there is a fairly good chance of considering the Data to be "STATIONARY"


> adf.test(returns)

 Now with the ADF test and its P-value we can confirm that the Data is "Stationary"

# Now calculating the Historical volatility of the Data

> T<-252^0.5
> histvolatality<-sd(returns)/T

> histvolatality