Date : 12-Feb-2013
Assignment 1 - Download data for NIFTY index from 1st Jan , 2012 to 31st Jan 2013.. Calculcate the log of returns data and find out the historical volatility.
Soln -:
Commands used - :
readData<-read.csv(file.choose() , header=T)
closePrice<-readData[,5] // Reading Closing Price Column
closePrice.ts<-ts(closePrice , frequenxy=252) // making a time series
varLag<- lag(closePrice.ts , k=-1) // calculating stock price for time (t-1)
logNum<- log(closePrice.ts , base=exp(1)) - log(varLag , base=exp(1)) // Calculating log
LogReturns<-logNum/log(varLag , base=exp(1)) // calculating log for returns data
// To calculate Historical volatility
sqrt<-(252)^0.5
histVolaitility<-sd(logreturns)*sqrt
Assignment 2 :
To create an acf plot for the log returns data calculated previously. Also do and adf test and interpret the findings.
Soln -:
// to create acf plot
acf(logReturns)
Grahical Interpreation
- the blue dotted lines represent confidence interval for the hypothesis (95% in default case)
- As all the co-relations plots(vertical lines) lie inside those two blue dotted lines , we can safely suggest that the returns data is "Stationary" in nature. This is visual inspection method for determining stationarity.
Using ADF test
Command used
adf.test(logReturns)
Interpretation from ADF test
Null Hypothesis -: The returns data is not Stationary
Alternative Hypothesis -: Returns Data is stationary
As from the test results p-value = 0.01 which is less than 0.05 value as stated for 95%confidence interval.
Hence Null Hypothesis is rejected.
Results -: given data is stationary in nature
Assignment 1 - Download data for NIFTY index from 1st Jan , 2012 to 31st Jan 2013.. Calculcate the log of returns data and find out the historical volatility.
Soln -:
Commands used - :
readData<-read.csv(file.choose() , header=T)
closePrice<-readData[,5] // Reading Closing Price Column
closePrice.ts<-ts(closePrice , frequenxy=252) // making a time series
varLag<- lag(closePrice.ts , k=-1) // calculating stock price for time (t-1)
logNum<- log(closePrice.ts , base=exp(1)) - log(varLag , base=exp(1)) // Calculating log
LogReturns<-logNum/log(varLag , base=exp(1)) // calculating log for returns data
sqrt<-(252)^0.5
histVolaitility<-sd(logreturns)*sqrt
Assignment 2 :
To create an acf plot for the log returns data calculated previously. Also do and adf test and interpret the findings.
Soln -:
// to create acf plot
acf(logReturns)
Grahical Interpreation
- the blue dotted lines represent confidence interval for the hypothesis (95% in default case)
- As all the co-relations plots(vertical lines) lie inside those two blue dotted lines , we can safely suggest that the returns data is "Stationary" in nature. This is visual inspection method for determining stationarity.
Using ADF test
Command used
adf.test(logReturns)
Interpretation from ADF test
Null Hypothesis -: The returns data is not Stationary
Alternative Hypothesis -: Returns Data is stationary
As from the test results p-value = 0.01 which is less than 0.05 value as stated for 95%confidence interval.
Hence Null Hypothesis is rejected.
Results -: given data is stationary in nature
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