Business Application IT Lab

IT Business Application Lab Assignments#6

Session 6
Date:  12th Feb,2013


Assignment 1:
Create a log of return data for a period of more than 1 year(1.1.2012-1.1.2013) and use this data to calculate historical volatility. 

Commands:

> stockprice<-read.csv(file.choose(),header=T)
> head(stockprice)
> closingprice<-stockprice$5
> closingprice.ts<-ts(closingprice,frequency=252)
> returns<-(closingprice.ts-lag(closingprice.ts,k=-1))/lag(closingprice.ts,k=-1)
> z<-scale(returns)+10
> returnslog<-log(z)
> returnslog

Assignment 2:
Calculate ACF plot for logreturns data and do an ADF test & interpret the result.

For acf plotting
> acf(returnslog)

Output:

From the above graph we can see that the results lie between 95% confidence interval.So we can safely conclude that the time series is stationary.



> T=252^0.5
> historicalvolatility<-sd(returnslog)*T
> historicalvolatility
> adf.test(returnslog)

Commands:

From the above output we find that p value is=0.01 which is < 0.05

So, we reject the null hypothesis and accept the alternate hypothesis that the time series is stationary.

Business Application IT Lab

IT Business Application Lab Assignments#5

Session 5
Date:  5th Feb,2013


Assignment 1 

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

Find plot of that return.


Data Set: S&P CNX NIFTY from 01/06/2012 to 31/01/2013

Output:


> z<-read.csv(file.choose(),header=T)
> head(z)
         Date    Open    High     Low   Close Shares.Traded Turnover..Rs..Cr.
1 01-Jun-2012 4910.85 4925.00 4831.75 4841.60     138767416           4989.22
2 04-Jun-2012 4797.30 4858.30 4770.35 4848.15     152339865           5143.54
3 05-Jun-2012 4869.45 4898.95 4847.70 4863.30     141476962           5016.41
4 06-Jun-2012 4886.65 5010.45 4886.15 4997.10     185340406           7188.42
5 07-Jun-2012 5035.35 5059.65 5007.75 5049.65     150558164           6077.37
6 08-Jun-2012 5044.25 5084.45 4994.80 5068.35     138389395           5249.81
> open<-z$Open[10:95]
> open.ts<-ts(open,deltat=1/252)
> open.ts
Time Series:
Start = c(1, 1)
End = c(1, 86)
Frequency = 252
 [1] 5105.10 5069.55 5174.00 5050.80 5114.55 5097.35 5101.75 5158.50 5107.45 5149.45 5148.95 5191.25 5283.85 5298.85 5310.40 5297.05 5324.70 5283.70 5286.60 5315.25
[21] 5240.00 5242.75 5232.35 5228.05 5199.10 5249.85 5233.55 5163.25 5128.80 5118.40 5126.30 5124.30 5129.75 5214.85 5220.70 5233.10 5195.60 5260.85 5295.40 5345.25
[41] 5348.30 5308.20 5316.35 5343.25 5385.95 5368.60 5368.70 5395.75 5426.15 5392.60 5387.85 5348.05 5343.85 5268.60 5298.20 5276.50 5249.15 5243.90 5217.65 5309.45
[61] 5343.65 5361.90 5336.10 5404.45 5435.20 5528.35 5631.75 5602.40 5536.95 5577.00 5691.95 5674.90 5653.40 5673.75 5684.80 5704.75 5727.70 5751.55 5815.00 5751.85
[81] 5708.15 5671.15 5663.50 5681.70 5674.25 5705.60
> summary(open.ts)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
   5051    5218    5309    5356    5433    5815
> z.diff<-diff(open.ts)
> z.diff
Time Series:
Start = c(1, 2)
End = c(1, 86)
Frequency = 252
 [1]  -35.55  104.45 -123.20   63.75  -17.20    4.40   56.75  -51.05   42.00   -0.50   42.30   92.60   15.00   11.55  -13.35   27.65  -41.00    2.90   28.65  -75.25
[21]    2.75  -10.40   -4.30  -28.95   50.75  -16.30  -70.30  -34.45  -10.40    7.90   -2.00    5.45   85.10    5.85   12.40  -37.50   65.25   34.55   49.85    3.05
[41]  -40.10    8.15   26.90   42.70  -17.35    0.10   27.05   30.40  -33.55   -4.75  -39.80   -4.20  -75.25   29.60  -21.70  -27.35   -5.25  -26.25   91.80   34.20
[61]   18.25  -25.80   68.35   30.75   93.15  103.40  -29.35  -65.45   40.05  114.95  -17.05  -21.50   20.35   11.05   19.95   22.95   23.85   63.45  -63.15  -43.70
[81]  -37.00   -7.65   18.20   -7.45   31.35
> returns<-cbind(open.ts,z.diff,lag(open.ts,k=-1))
> returns
Time Series:
Start = c(1, 1)
End = c(1, 87)
Frequency = 252
         open.ts  z.diff lag(open.ts, k = -1)
1.000000 5105.10      NA                   NA
1.003968 5069.55  -35.55              5105.10
1.007937 5174.00  104.45              5069.55
1.011905 5050.80 -123.20              5174.00
1.015873 5114.55   63.75              5050.80
1.019841 5097.35  -17.20              5114.55
1.023810 5101.75    4.40              5097.35
1.027778 5158.50   56.75              5101.75
1.031746 5107.45  -51.05              5158.50
1.035714 5149.45   42.00              5107.45
1.039683 5148.95   -0.50              5149.45
1.043651 5191.25   42.30              5148.95
1.047619 5283.85   92.60              5191.25
1.051587 5298.85   15.00              5283.85
1.055556 5310.40   11.55              5298.85
1.059524 5297.05  -13.35              5310.40
1.063492 5324.70   27.65              5297.05
1.067460 5283.70  -41.00              5324.70
1.071429 5286.60    2.90              5283.70
1.075397 5315.25   28.65              5286.60
1.079365 5240.00  -75.25              5315.25
1.083333 5242.75    2.75              5240.00
1.087302 5232.35  -10.40              5242.75
1.091270 5228.05   -4.30              5232.35
1.095238 5199.10  -28.95              5228.05
1.099206 5249.85   50.75              5199.10
1.103175 5233.55  -16.30              5249.85
1.107143 5163.25  -70.30              5233.55
1.111111 5128.80  -34.45              5163.25
1.115079 5118.40  -10.40              5128.80
1.119048 5126.30    7.90              5118.40
1.123016 5124.30   -2.00              5126.30
1.126984 5129.75    5.45              5124.30
1.130952 5214.85   85.10              5129.75
1.134921 5220.70    5.85              5214.85
1.138889 5233.10   12.40              5220.70
1.142857 5195.60  -37.50              5233.10
1.146825 5260.85   65.25              5195.60
1.150794 5295.40   34.55              5260.85
1.154762 5345.25   49.85              5295.40
1.158730 5348.30    3.05              5345.25
1.162698 5308.20  -40.10              5348.30
1.166667 5316.35    8.15              5308.20
1.170635 5343.25   26.90              5316.35
1.174603 5385.95   42.70              5343.25
1.178571 5368.60  -17.35              5385.95
1.182540 5368.70    0.10              5368.60
1.186508 5395.75   27.05              5368.70
1.190476 5426.15   30.40              5395.75
1.194444 5392.60  -33.55              5426.15
1.198413 5387.85   -4.75              5392.60
1.202381 5348.05  -39.80              5387.85
1.206349 5343.85   -4.20              5348.05
1.210317 5268.60  -75.25              5343.85
1.214286 5298.20   29.60              5268.60
1.218254 5276.50  -21.70              5298.20
1.222222 5249.15  -27.35              5276.50
1.226190 5243.90   -5.25              5249.15
1.230159 5217.65  -26.25              5243.90
1.234127 5309.45   91.80              5217.65
1.238095 5343.65   34.20              5309.45
1.242063 5361.90   18.25              5343.65
1.246032 5336.10  -25.80              5361.90
1.250000 5404.45   68.35              5336.10
1.253968 5435.20   30.75              5404.45
1.257937 5528.35   93.15              5435.20
1.261905 5631.75  103.40              5528.35
1.265873 5602.40  -29.35              5631.75
1.269841 5536.95  -65.45              5602.40
1.273810 5577.00   40.05              5536.95
1.277778 5691.95  114.95              5577.00
1.281746 5674.90  -17.05              5691.95
1.285714 5653.40  -21.50              5674.90
1.289683 5673.75   20.35              5653.40
1.293651 5684.80   11.05              5673.75
1.297619 5704.75   19.95              5684.80
1.301587 5727.70   22.95              5704.75
1.305556 5751.55   23.85              5727.70
1.309524 5815.00   63.45              5751.55
1.313492 5751.85  -63.15              5815.00
1.317460 5708.15  -43.70              5751.85
1.321429 5671.15  -37.00              5708.15
1.325397 5663.50   -7.65              5671.15
1.329365 5681.70   18.20              5663.50
1.333333 5674.25   -7.45              5681.70
1.337302 5705.60   31.35              5674.25
1.341270      NA      NA              5705.60
> returns<-z.diff/lag(open.ts,k=-1)
> returns
Time Series:
Start = c(1, 2)
End = c(1, 86)
Frequency = 252
 [1] -6.963625e-03  2.060341e-02 -2.381136e-02  1.262176e-02 -3.362955e-03  8.631936e-04  1.112363e-02 -9.896288e-03  8.223282e-03 -9.709775e-05  8.215267e-03
[12]  1.783771e-02  2.838839e-03  2.179718e-03 -2.513935e-03  5.219887e-03 -7.699964e-03  5.488578e-04  5.419362e-03 -1.415738e-02  5.248092e-04 -1.983692e-03
[23] -8.218105e-04 -5.537437e-03  9.761305e-03 -3.104851e-03 -1.343256e-02 -6.672154e-03 -2.027765e-03  1.543451e-03 -3.901449e-04  1.063560e-03  1.658950e-02
[34]  1.121796e-03  2.375160e-03 -7.165925e-03  1.255870e-02  6.567380e-03  9.413831e-03  5.706001e-04 -7.497710e-03  1.535360e-03  5.059862e-03  7.991391e-03
[45] -3.221344e-03  1.862683e-05  5.038464e-03  5.634064e-03 -6.183021e-03 -8.808367e-04 -7.386991e-03 -7.853330e-04 -1.408161e-02  5.618191e-03 -4.095731e-03
[56] -5.183360e-03 -1.000162e-03 -5.005816e-03  1.759413e-02  6.441345e-03  3.415269e-03 -4.811727e-03  1.280898e-02  5.689756e-03  1.713828e-02  1.870359e-02
[67] -5.211524e-03 -1.168249e-02  7.233224e-03  2.061144e-02 -2.995458e-03 -3.788613e-03  3.599604e-03  1.947566e-03  3.509358e-03  4.022963e-03  4.163975e-03
[78]  1.103181e-02 -1.085985e-02 -7.597556e-03 -6.481960e-03 -1.348933e-03  3.213561e-03 -1.311227e-03  5.524959e-03
> plot(returns)

Output:

Assignment 2:

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

Output;

z<-read.csv(file.choose(),header=T)

head(z)

z.data<-z[1:700,1:9]

sapply(z.data,mean)

z.data$ed<-factor(z.data$ed)

logit.est<-glm(default~age+employ+address+income+debtinc+creddebt+othdebt,data=z.data,family="binomial")

summary(logit.est)

confint.default(logit.est)

logit.eg2<-with(z[701:850,1:8],data.frame(age=age,employ=employ,address=address,income=income,debtinc=debtinc,creddebt=creddebt,othdebt=othdebt,ed=factor(1:3)))

logit.eg2$prob<-predict(logit.est,newdata=logit.eg2,type="response")

head(logit.eg2)

Output: