Assignment 6

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

> 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

Session - 5/2/2012

Assignment 1

Find returns of NSE data of greater than 6 months having selected the 10th data point as start and 95th data point as end.

Commands:


  z<-read.csv(file.choose(),header=T)
 Close<-z$Close
 Close
 Close.ts<-ts(Close)
 Close.ts<-ts(Close,deltat= 1/252)
 Interval<-ts(data=Close.ts[10:95],frequency=1,deltat=1/252)
 z1.ts<-ts(Interval)
 z1.ts
 z1.diff<-diff(z1)
 z2<-lag(Close.ts,K=-1)
 Returns<-z1.diff/z2
 plot(Returns,main=" Returns from 10 th to 95th day of NSE Mid-cap Index ")
 z3<-cbind(z1.ts,z1.diff,Returns)
 plot(z3,main=" Data from 10th-95th day ; Difference ; Returns")

Assignment 2

1-700 data is available, Predict the data from 701-850, use the GLM estimation using LOGIT Analysis for the same.

 z<-read.csv(file.choose(),header=T)
 data1<-z[1:700,1:9]
 head(data1)
 data1$ed<-factor(data1$ed)
 data.est<-glm(default ~ age + ed + employ + address + income, data=data1, family ="binomial")
 summary(data.est)
 return<-z[701:850,1:8]
 return$ed<-factor(return$ed)
 return$probability<-predict(data.est,newdata=return,type="response")
 head(return)