Calculate the standard deviation of the firm's daily stock return over the past three months

[OBADA ALMAJALI]

Hello everyone,

I have daily panel data and I want to calculate the standard deviation of the firm's daily stock return over the past three months. Could anyone help me to find an appropriate code to calculate annualized 3-month rolling sample standard deviation and assuming the standard deviation is centered on zero, instead of centered around mean value given a time period.

Sample of my dataset is attached :



Kind Regards
Obada

[Clyde Schechter]

I want to calculate the standard deviation of the firm's daily stock return over the past three months.

rangestat (sd) ret, by(permno) interval(date -89 0)

Note: to use this code you must install -rangestat-, written by Robert Picard, Roberto Ferrer, and Nick Cox, available from SSC.

I have interpreted "past three months" to mean a 90 day period that includes the current date, hence -interval(date -89 0)-. If that is not what you meant by "past three months," modify the -interval- option accordingly.

Could anyone help me to find an appropriate code to calculate annualized 3-month rolling sample standard deviation and assuming the standard deviation is centered on zero, instead of centered around mean value given a time period.

I do not understand this.

In the future, when showing data examples, please use the -dataex- command to do so. If you are running version 15.1 or a fully updated version 14.2, -dataex- is already part of your official Stata installation. If not, run -ssc install dataex- to get it. Either way, run -help dataex- to read the simple instructions for using it. -dataex- will save you time; it is easier and quicker than typing out tables. It includes complete information about aspects of the data that are often critical to answering your question but cannot be seen from tabular displays or screenshots. It also makes it possible for those who want to help you to create a faithful representation of your example to try out their code, which in turn makes it more likely that their answer will actually work in your data.



When asking for help with code, always show example data. When showing example data, always use -dataex-.

[Attaullah Shah]

In the following example, I shall use asrol, which can be downloaded from SSC. You can read further resources related to asrol here on my website.

In the following example, I assume that the rolling window is based on a fixed length of three months. Later on, I also show the code where the rolling window is based on the past 90 days.

Use example dataset from my site. The data set has three variables, (1) symbols: a string variable to identify each firm (2) close - a numeric variable that records companies' share prices (3) date - a numeric variable that tracks daily dates

use http://fintechprofessor.com/stocks.dta

* List the first 10 observations
     +-----------------------------+
     | symbol        date    close |
     |-----------------------------|
  1. |   AABS   07jan2013    103.5 |
  2. |   AABS   14jan2013      103 |
  3. |   AABS   16jan2013      103 |
  4. |   AABS   17jan2013   105.75 |
  5. |   AABS   25feb2013     96.7 |
     |-----------------------------|
  6. |   AABS   27feb2013      101 |
  7. |   AABS   05mar2013      106 |
  8. |   AABS   19mar2013      101 |
  9. |   AABS   28mar2013      101 |
 10. |   AABS   10apr2013      101 |
     +-----------------------------+

* Create a month identifier
gen mofd = mofd(date)
format mofd %tm

* generate returns
bys symbol (date): gen returns = ln(close / close[_n-1])

* Install asrol
ssc install asrol

* Find 3 months rolling window sd with asrol
bys symbol: asrol returns, window(mofd 3) stat(sd)

* Convert to annual volatility
gen annual_sd = sd3_returns*sqrt(12)/sqrt(3)

list  in 1/10
     +-------------------------------------------------------------------------+
     | symbol        date    close     mofd     returns   sd3_ret~s   annual~d |
     |-------------------------------------------------------------------------|
  1. |   AABS   07jan2013    103.5   2013m1           .     .016786    .033572 |
  2. |   AABS   14jan2013      103   2013m1   -.0048426     .016786    .033572 |
  3. |   AABS   16jan2013      103   2013m1           0     .016786    .033572 |
  4. |   AABS   17jan2013   105.75   2013m1    .0263488     .016786    .033572 |
  5. |   AABS   25feb2013     96.7   2013m2   -.0894644   .05122231   .1024446 |
     |-------------------------------------------------------------------------|
  6. |   AABS   27feb2013      101   2013m2    .0435071   .05122231   .1024446 |
  7. |   AABS   05mar2013      106   2013m3    .0483186   .04661255   .0932251 |
  8. |   AABS   19mar2013      101   2013m3   -.0483186   .04661255   .0932251 |
  9. |   AABS   28mar2013      101   2013m3           0   .04661255   .0932251 |
 10. |   AABS   10apr2013      101   2013m4           0   .05331813   .1066363 |
     +-------------------------------------------------------------------------+

Method 2: Using trading days

bys symbol: asrol returns, window(date 90) stat(sd) gen(sd_90days)

* Convert to annual volatility
gen annual_sd2 = sd_90days *sqrt(252)/sqrt(90)


list  in 1/10

     +------------------------------------------------------------------------------------------------+
     | symbol        date    close     mofd     returns   sd3_ret~s   annual~d   sd_90days   annual~2 |
     |------------------------------------------------------------------------------------------------|
  1. |   AABS   07jan2013    103.5   2013m1           .     .016786    .033572           .          . |
  2. |   AABS   14jan2013      103   2013m1   -.0048426     .016786    .033572           .          . |
  3. |   AABS   16jan2013      103   2013m1           0     .016786    .033572   .00342425   .0057299 |
  4. |   AABS   17jan2013   105.75   2013m1    .0263488     .016786    .033572     .016786   .0280883 |
  5. |   AABS   25feb2013     96.7   2013m2   -.0894644   .05122231   .1024446    .0502229    .084039 |
     |------------------------------------------------------------------------------------------------|
  6. |   AABS   27feb2013      101   2013m2    .0435071   .05122231   .1024446   .05122231   .0857113 |
  7. |   AABS   05mar2013      106   2013m3    .0483186   .04661255   .0932251   .05070348   .0848431 |
  8. |   AABS   19mar2013      101   2013m3   -.0483186   .04661255   .0932251   .05032963   .0842176 |
  9. |   AABS   28mar2013      101   2013m3           0   .04661255   .0932251   .04661255   .0779977 |
 10. |   AABS   10apr2013      101   2013m4           0   .05331813   .1066363   .04361395   .0729801 |
     +------------------------------------------------------------------------------------------------+

The case of centering on zero

gen return2 = returns * returns
bys symbol: asrol return2 , window(date 90) stat(mean) gen(sd0_90days)
gen annual_sd0 = sqrt( sd0_90days ) *sqrt(252)/sqrt(90)

. list symbol date returns annual_sd0 in 1/10

     +-------------------------------------------+
     | symbol        date     returns   annual~0 |
     |-------------------------------------------|
  1. |   AABS   07jan2013           .          . |
  2. |   AABS   14jan2013   -.0048426   .0081033 |
  3. |   AABS   16jan2013           0   .0057299 |
  4. |   AABS   17jan2013    .0263488   .0258817 |
  5. |   AABS   25feb2013   -.0894644   .0781353 |
     |-------------------------------------------|
  6. |   AABS   27feb2013    .0435071    .077098 |
  7. |   AABS   05mar2013    .0483186   .0777363 |
  8. |   AABS   19mar2013   -.0483186   .0781891 |
  9. |   AABS   28mar2013           0   .0731392 |
 10. |   AABS   10apr2013           0   .0689563 |
     +-------------------------------------------+

[Michael Craig]

I have a question concerning post #2 as I have a similar issue. I used it for 21 days (1 month of trading days). From that I want to annualize it. However, I don't know exactly what the rangestat command is giving you. Is this

a) the daily standard deviation calculated over the interval, or

b) the standard deviation over the interval?

I mean, I could use Excel to check it but someone may know the solution and may want to share it. I have not really found the answer in the help rangestat.

[Nick Cox]

Michael Craig In turn I really don't follow what distinction you are making or alluding to. rangestat (SSC) does not pay attention to whether data are daily or anything else specifically; it just works on what it is instructed to work on.

You don't give a data example or exact code you used (FAQ Advice #12 remains as valid as ever), so answers are restricted to explanations of principle or other examples invented for the purpose.

Whether trading days do or do not complicate your problem I can't understand either.

Here's an example you can run:

. clear

. set obs 100
number of observations (_N) was 0, now 100

. gen y = _n

. gen t = y  

. rangestat (count) y (sd) y, int(t -20 0)

.
. list in 1/30

     +-------------------------------+
     |  y    t   y_count        y_sd |
     |-------------------------------|
  1. |  1    1         1           . |
  2. |  2    2         2   .70710678 |
  3. |  3    3         3           1 |
  4. |  4    4         4   1.2909944 |
  5. |  5    5         5   1.5811388 |
     |-------------------------------|
  6. |  6    6         6   1.8708287 |
  7. |  7    7         7   2.1602469 |
  8. |  8    8         8   2.4494897 |
  9. |  9    9         9   2.7386128 |
 10. | 10   10        10   3.0276504 |
     |-------------------------------|
 11. | 11   11        11   3.3166248 |
 12. | 12   12        12   3.6055513 |
 13. | 13   13        13   3.8944405 |
 14. | 14   14        14   4.1833001 |
 15. | 15   15        15    4.472136 |
     |-------------------------------|
 16. | 16   16        16   4.7609523 |
 17. | 17   17        17   5.0497525 |
 18. | 18   18        18   5.3385391 |
 19. | 19   19        19   5.6273143 |
 20. | 20   20        20   5.9160798 |
     |-------------------------------|
 21. | 21   21        21   6.2048368 |
 22. | 22   22        21   6.2048368 |
 23. | 23   23        21   6.2048368 |
 24. | 24   24        21   6.2048368 |
 25. | 25   25        21   6.2048368 |
     |-------------------------------|
 26. | 26   26        21   6.2048368 |
 27. | 27   27        21   6.2048368 |
 28. | 28   28        21   6.2048368 |
 29. | 29   29        21   6.2048368 |
 30. | 30   30        21   6.2048368 |
     +-------------------------------+

.
. su t in 1/21

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
           t |         21          11    6.204837          1         21

Key points that may illuminate your puzzlement:

1. If given a window, rangestat uses whatever is available in that window. So in this example, windows potentially of length 21 only contain 21 observations from observation 21 on. Unlike some other commands, rangestat lets (indeed makes) users decide what they want to do with incomplete windows. Further, the interval() option could catch more observations than are implied by its length, as nothing in rangestat assumes that distinct values of the variable within that interval occur at most once. For example, rangestat neither assumes xtset or tsset beforehand nor makes use of either if either has been declared.

2. The standard deviation recipe used is precisely that of summarize, which is documented. Sample size MINUS 1 is used, not sample size.

3. You refer to annualized rates but I fear that rangestat may disappoint you there. In no sense is it geared to financial data; it's a general command and does not offer hooks for specialised financial calculations. I don't work in that area at all; Robert Picard who is first author and prime mover of rangestat, I think knows more about it than I do, but I think he would affirm that we don't explicitly offer hooks for particular kinds of data. However, what is important is that rangestat can be extended by writing your own Mata functions, as the help explains at length.

I will refer you also to asrol (SSC), which in stark contrast is written with specific financial calculations in mind. I don't advise on its use, not least because its author Attaullah Shah watches the forum for mentions and is always willing to help.

[Michael Craig]

Thanks for the helpful explanation.

I was just wondering if the code in #2
rangestat (sd) ret, by(permno) interval(date -89 0) is calculating a daily standard deviation or a standard deviation over 90 days. For example, if it calculates daily and I need 90 days, I need to take sqrt(90) times daily std. I think it calculates 90day std but I am not totally sure, therefore my question. There is no actually code from my side required, as the interpretation of #2 gives me everything I need.

[Nick Cox]

Sorry, but you seem to be asking the same question, and I don't have a different answer. You make a verbal distinction

daily standard deviation

or

a standard deviation over 90 days

but I fail to understand what you're getting at.

Also, I have explained, or at least implied, that multiplication by sqrt(90) is not an option that rangestat offers, but it is easy enough to apply outside rangestat, or (according to taste and expertise) write your own function for use with rangestat.

I've already explained about incomplete windows in #5.

My claiming ignorance of specialist calculations for finance is not false modesty, but genuine.

There's a key principle behind #5. Don't wonder what code does: read the code itself (in the case of rangestat nothing is hidden) and/or experiment with simple examples where you can check independently what is happening, as I checked with summarize what the SD was.

[Celine Tran]

Dear Nick Cox ,

I would like to calculate the standard deviation of previous two years' weekly stock returns. Assume I get weekly stock returns already. How can I use rangestat command to calculate the standard divation over previous two years? Normally, one years include 52 weeks in financial area. but some years, there are 53 weekly obs....

Because the number of weeks for each year maybe vary. So I cannot do ... interval(month -103 0) .....

Do you have any suggestion for me?

Thank you very much in advance.

[Nick Cox]

#8

From #7 I echo

My claiming ignorance of specialist calculations for finance is not false modesty, but genuine.

What do other people do with such data?

Otherwise p\lease give a data example. FAQ Advice #12 applies as always.

What does a variable month have to do with a question about weekly data?

[Celine Tran]

Dear Nick Cox ,

Thank you for your reply. I do not ask about specialist calculations. I understand what your responese at #7. Therefore, ,my question is different. I would like to know how to use rangestat to calulate the standard deviation of certain number of obs.

Here is my data.

TRCode Date return
GPRL 1/2/2014 
GPRL 1/9/2014 .0308133
GPRL 1/16/2014 -.0013903
GPRL 1/23/2014 -.03063
GPRL 1/30/2014 .0233393
GPRL 2/6/2014 -.0614035
GPRL 2/13/2014 .0007477
GPRL 2/20/2014 .0085917
GPRL 2/27/2014 -.0003704
GPRL 3/6/2014 .1081882
GPRL 3/13/2014 .0177198
GPRL 3/20/2014 -.0466491
GPRL 3/27/2014 .0434183
GPRL 4/3/2014 .0904888
GPRL 4/10/2014 .0529982
GPRL 4/17/2014 -.0353753
GPRL 4/24/2014 .0399523
GPRL 5/1/2014 .016055
GPRL 5/8/2014 -.0513544
GPRL 5/15/2014 .0960738
GPRL 5/22/2014 .1082768
GPRL 5/29/2014 -.0022037
GPRL 6/5/2014 -.0262577
GPRL 6/12/2014 -.0438508
GPRL 6/19/2014 -.0005271
GPRL 6/26/2014 .0780591
GPRL 7/3/2014 .0273973
GPRL 7/10/2014 .0754762
GPRL 7/17/2014 .0225814
GPRL 7/24/2014 .0441654
GPRL 7/31/2014 -.0470661
GPRL 8/7/2014 -.0461271
GPRL 8/14/2014 -.0866788
GPRL 8/21/2014 -.017982
GPRL 8/28/2014 .0155137
GPRL 9/4/2014 -.0042575
GPRL 9/11/2014 -.02666
GPRL 9/18/2014 .0173127
GPRL 9/25/2014 -.001016
GPRL 10/2/2014 -.0030511
GPRL 10/9/2014 .0015302
GPRL 10/16/2014 -.1033868
GPRL 10/23/2014 -.0227208
GPRL 10/30/2014 .0784656
GPRL 11/6/2014 -.0285637
GPRL 11/13/2014 .0183079
GPRL 11/20/2014 -.0332335
GPRL 11/27/2014 -.0369118
GPRL 12/4/2014 -.0263312
GPRL 12/11/2014 -.0090144
GPRL 12/18/2014 -.0451789
GPRL 12/25/2014 -.0123849
GPRL 1/1/2015 -.0318328
GPRL 1/8/2015 -.0099635
GPRL 1/15/2015 .0073801
GPRL 1/22/2015 .038628
GPRL 1/29/2015 .0057711
GPRL 2/5/2015 .0044629
GPRL 2/12/2015 -.0729927
GPRL 2/19/2015 .0619651
GPRL 2/26/2015 .0367505
GPRL 3/5/2015 -.0951493
GPRL 3/12/2015 .0683849
GPRL 3/19/2015 .0331296
GPRL 3/26/2015 .0554172
GPRL 4/2/2015 .0073746
GPRL 4/9/2015 .0430454
GPRL 4/16/2015 .0235823
GPRL 4/23/2015 .0112452
GPRL 4/30/2015 .0008137
GPRL 5/7/2015 -.0468835
GPRL 5/14/2015 .005118
GPRL 5/21/2015 -.0523338
GPRL 5/28/2015 .0295522
GPRL 6/4/2015 .0034793
GPRL 6/11/2015 .0167582
GPRL 6/18/2015 .0048309
GPRL 6/25/2015 -.0079186
GPRL 7/2/2015 -.0142531
GPRL 7/9/2015 -.0575477
GPRL 7/16/2015 -.0104326
GPRL 7/23/2015 -.020155
GPRL 7/30/2015 -.0170886
GPRL 8/6/2015 .0534449
GPRL 8/13/2015 -.021088
GPRL 8/20/2015 -.049641
GPRL 8/27/2015 -.0588042
GPRL 9/3/2015 -.0104712
GPRL 9/10/2015 -.038448
GPRL 9/17/2015 .0172414
GPRL 9/24/2015 .0151461
GPRL 10/1/2015 -.0017762
GPRL 10/8/2015 .03879
GPRL 10/15/2015 .0226105
GPRL 10/22/2015 -.0107203
GPRL 10/29/2015 -.1381646
GPRL 11/5/2015 -.0227898
GPRL 11/12/2015 .0261359
GPRL 11/19/2015 -.0525078
GPRL 11/26/2015 -.0107527
GPRL 12/3/2015 -.0426421
GPRL 12/10/2015 .0004367
GPRL 12/17/2015 -.045395
GPRL 12/24/2015 -.0310928
GPRL 12/31/2015 .0122699
GPRL 1/7/2016 -.0097902
GPRL 1/14/2016 .0390772
GPRL 1/21/2016 .0616221
GPRL 1/28/2016 .0072557
GPRL 2/4/2016 .0224576
GPRL 2/11/2016 .002901
GPRL 2/18/2016 -.0082645
GPRL 2/25/2016 .0004167
GPRL 3/3/2016 .18409
GPRL 3/10/2016 -.2244108
GPRL 3/17/2016 .0435374
GPRL 3/24/2016 -.0147762
GPRL 3/31/2016 .0816056
GPRL 4/7/2016 -.0619902
GPRL 4/14/2016 .0530435
GPRL 4/21/2016 -.0082576
GPRL 4/28/2016 .0399667
GPRL 5/5/2016 .0064051
GPRL 5/12/2016 -.0453461
GPRL 5/19/2016 -.0079167
GPRL 5/26/2016 .0041999
GPRL 6/2/2016 .019657
GPRL 6/9/2016 .4364233
GPRL 6/16/2016 -.1956025
GPRL 6/23/2016 .0099397
GPRL 6/30/2016 -.0042179
GPRL 7/7/2016 .204377
GPRL 7/14/2016 -.0521688
GPRL 7/21/2016 -.3107607
GPRL 7/28/2016 .0897263
GPRL 8/4/2016 -.0526966
GPRL 8/11/2016 -.0425902
GPRL 8/18/2016 .1130277
GPRL 8/25/2016 .0297716
GPRL 9/1/2016 .0154455
GPRL 9/8/2016 -.0292512
GPRL 9/15/2016 .0269184
GPRL 9/22/2016 .0449922
GPRL 9/29/2016 .0475477
GPRL 10/6/2016 .0268049
GPRL 10/13/2016 -.0090498
GPRL 10/20/2016 .0779768
GPRL 10/27/2016 .0130336
GPRL 11/3/2016 -.0681891
GPRL 11/10/2016 -.0176044
GPRL 11/17/2016 -.0049192
GPRL 11/24/2016 -.0105932
GPRL 12/1/2016 .0021413
GPRL 12/8/2016 .0192308
GPRL 12/15/2016 -.0132774
GPRL 12/22/2016 .0290368
GPRL 12/29/2016 .0141087
GPRL 1/5/2017 .0302002
GPRL 1/12/2017 .0032938
GPRL 1/19/2017 -.0338148
GPRL 1/26/2017 .0502888
GPRL 2/2/2017 .0116467
GPRL 2/9/2017 -.0070355
GPRL 2/16/2017 -.0144928
GPRL 2/23/2017 .0395425
GPRL 3/2/2017 .0543854
GPRL 3/9/2017 -.0068575
GPRL 3/16/2017 -.0141099
GPRL 3/23/2017 .0301462
GPRL 3/30/2017 -.0381318
GPRL 4/6/2017 .0236632
GPRL 4/13/2017 .0030021
GPRL 4/20/2017 .0565699
GPRL 4/27/2017 .0127479
GPRL 5/4/2017 .0237762
GPRL 5/11/2017 -.0795082
GPRL 5/18/2017 .0418522
GPRL 5/25/2017 .0635328
GPRL 6/1/2017 -.0008036
GPRL 6/8/2017 .016622
GPRL 6/15/2017 .0263713
GPRL 6/22/2017 .0465057
GPRL 6/29/2017 .0078566
GPRL 7/6/2017 -.0267966
GPRL 7/13/2017 .0297872
GPRL 7/20/2017 .033544
GPRL 7/27/2017 -.020461
GPRL 8/3/2017 -.0489796
GPRL 8/10/2017 -.01641
GPRL 8/17/2017 -.0277207
GPRL 8/24/2017 .029831
GPRL 8/31/2017 .0158934
GPRL 9/7/2017 -.0406258
GPRL 9/14/2017 -.0068385
GPRL 9/21/2017 -.0007945
GPRL 9/28/2017 -.0055659
GPRL 10/5/2017 -.0029318
GPRL 10/12/2017 -.02085
GPRL 10/19/2017 -.015834
GPRL 10/26/2017 -.0061026
GPRL 11/2/2017 .0178621
GPRL 11/9/2017 -.0008226
GPRL 11/16/2017 .0321076
GPRL 11/23/2017 .0071789
GPRL 11/30/2017 -.0285111
GPRL 12/7/2017 .0548913
GPRL 12/14/2017 -.052035
GPRL 12/21/2017 -.0407609
GPRL 12/28/2017 .0048159
TISE 1/2/2014 
TISE 1/9/2014 -.0278686
TISE 1/16/2014 -.0013164
TISE 1/23/2014 .0458407
TISE 1/30/2014 -.0211455
TISE 2/6/2014 -.0187411
TISE 2/13/2014 .0508821
TISE 2/20/2014 -.0226138
TISE 2/27/2014 .0242725
TISE 3/6/2014 -.0467018
TISE 3/13/2014 -.0267481
TISE 3/20/2014 -.0089619
TISE 3/27/2014 -.0001507
TISE 4/3/2014 .0064818
TISE 4/10/2014 .0220159
TISE 4/17/2014 .0033705
TISE 4/24/2014 -.0163575
TISE 5/1/2014 .009948
TISE 5/8/2014 .0263158
TISE 5/15/2014 -.0508523
TISE 5/22/2014 -.0105644
TISE 5/29/2014 .0359976
TISE 6/5/2014 .0136926
TISE 6/12/2014 .042992
TISE 6/19/2014 -.0403843
TISE 6/26/2014 -.0007256
TISE 7/3/2014 .0027592
TISE 7/10/2014 .0194062
TISE 7/17/2014 -.0099446
TISE 7/24/2014 .0007175
TISE 7/31/2014 -.0055922
TISE 8/7/2014 -.0027397
TISE 8/14/2014 -.0005784
TISE 8/21/2014 .0021701
TISE 8/28/2014 -.0153024
TISE 9/4/2014 -.0042516
TISE 9/11/2014 -.0278269
TISE 9/18/2014 -.0137816
TISE 9/25/2014 -.0116708
TISE 10/2/2014 .0023306
TISE 10/9/2014 -.0148814
TISE 10/16/2014 -.0314713
TISE 10/23/2014 -.029082
TISE 10/30/2014 -.001004
TISE 11/6/2014 .0335008
TISE 11/13/2014 -.0427877
TISE 11/20/2014 .0220115
TISE 11/27/2014 .018224
TISE 12/4/2014 -.0261959
TISE 12/11/2014 -.0203843
TISE 12/18/2014 .0006822
TISE 12/25/2014 -.0352821
TISE 1/1/2015 .0641343
TISE 1/8/2015 -.0654159
TISE 1/15/2015 .0191864
TISE 1/22/2015 -.0282378
TISE 1/29/2015 -.0001794
TISE 2/5/2015 .0405454
TISE 2/12/2015 -.0432759
TISE 2/19/2015 -.0720851
TISE 2/26/2015 .0075743
TISE 3/5/2015 -.0354665
TISE 3/12/2015 -.044964
TISE 3/19/2015 -.000837
TISE 3/26/2015 .0433508
TISE 4/2/2015 .007226
TISE 4/9/2015 -.0984456
TISE 4/16/2015 .061229
TISE 4/23/2015 .0131223
TISE 4/30/2015 -.0117188
TISE 5/7/2015 -.0062409
TISE 5/14/2015 -.025539
TISE 5/21/2015 .0227712
TISE 5/28/2015 -.0247847
TISE 6/4/2015 .003446
TISE 6/11/2015 .0002146
TISE 6/18/2015 .0834764
TISE 6/25/2015 -.0883343
TISE 7/2/2015 .011297
TISE 7/9/2015 -.0238453
TISE 7/16/2015 -.0103433
TISE 7/23/2015 .019791
TISE 7/30/2015 -.0196249
TISE 8/6/2015 -.0507117
TISE 8/13/2015 .0513121
TISE 8/20/2015 -.0109204
TISE 8/27/2015 -.3050924
TISE 9/3/2015 .0311284
TISE 9/10/2015 .0003145
TISE 9/17/2015 .0204338
TISE 9/24/2015 .0070856
TISE 10/1/2015 .3386357
TISE 10/8/2015 -.226691
TISE 10/15/2015 .0227541
TISE 10/22/2015 .0366946
TISE 10/29/2015 .0604794
TISE 11/5/2015 -.0010512
TISE 11/12/2015 .2423047
TISE 11/19/2015 -.2166455
TISE 11/26/2015 .0481211
TISE 12/3/2015 .0247614
TISE 12/10/2015 -.0201359
TISE 12/17/2015 -.0133573
TISE 12/24/2015 .0106743
TISE 12/31/2015 .0528078
TISE 1/7/2016 -.0680206
TISE 1/14/2016 -.003413
TISE 1/21/2016 -.1454162
TISE 1/28/2016 .0317509
TISE 2/4/2016 .1443083
TISE 2/11/2016 .024282
TISE 2/18/2016 -.1738465
TISE 2/25/2016 .1255785
TISE 3/3/2016 .0317982
TISE 3/10/2016 .0286929
TISE 3/17/2016 .0433884
TISE 3/24/2016 -.0556931
TISE 3/31/2016 .0180865
TISE 4/7/2016 .0399073
TISE 4/14/2016 -.0505076
TISE 4/21/2016 .011734
TISE 4/28/2016 0
TISE 5/5/2016 -.075
TISE 5/12/2016 .0401226
TISE 5/19/2016 .0375033
TISE 5/26/2016 .091402
TISE 6/2/2016 -.0603265
TISE 6/9/2016 .0392749
TISE 6/16/2016 -.0016957
TISE 6/23/2016 .009949
TISE 6/30/2016 -.0456511
TISE 7/7/2016 .041289
TISE 7/14/2016 .0050774
TISE 7/21/2016 -.0108251
TISE 7/28/2016 .0026751
TISE 8/4/2016 -.0133398
TISE 8/11/2016 .0427729
TISE 8/18/2016 .0014144
TISE 8/25/2016 -.0301318
TISE 9/1/2016 -.0245146
TISE 9/8/2016 .0303558
TISE 9/15/2016 -.0326008
TISE 9/22/2016 .0072391
TISE 9/29/2016 .0146221
TISE 10/6/2016 -.0075721
TISE 10/13/2016 -.0504553
TISE 10/20/2016 -.0049248
TISE 10/27/2016 .0630372
TISE 11/3/2016 -.1359961
TISE 11/10/2016 .1463415
TISE 11/17/2016 -.065809
TISE 11/24/2016 -.0323093
TISE 12/1/2016 .0021894
TISE 12/8/2016 .0027307
TISE 12/15/2016 .0087146
TISE 12/22/2016 -.0029698
TISE 12/29/2016 -.0232873
TISE 1/5/2017 .03715
TISE 1/12/2017 .0248597
TISE 1/19/2017 -.0245175
TISE 1/26/2017 -.018984
TISE 2/2/2017 -.0106296
TISE 2/9/2017 .038292
TISE 2/16/2017 -.0212258
TISE 2/23/2017 .0759013
TISE 3/2/2017 .0788612
TISE 3/9/2017 -.0450724
TISE 3/16/2017 .0141844
TISE 3/23/2017 .0217024
TISE 3/30/2017 -.0160491
TISE 4/6/2017 .0741185
TISE 4/13/2017 .0031264
TISE 4/20/2017 -.0552093
TISE 4/27/2017 .0188501
TISE 5/4/2017 -.0407031
TISE 5/11/2017 .0460463
TISE 5/18/2017 .0313436
TISE 5/25/2017 .024581
TISE 6/1/2017 -.0008724
TISE 6/8/2017 .0406025
TISE 6/15/2017 -.0004196
TISE 6/22/2017 .0304302
TISE 6/29/2017 -.0099796
TISE 7/6/2017 .0514297
TISE 7/13/2017 .1062414
TISE 7/20/2017 .0083127
TISE 7/27/2017 -.0114015
TISE 8/3/2017 .0168559
TISE 8/10/2017 -.0342
TISE 8/17/2017 -.0325203
TISE 8/24/2017 -.0304388
TISE 8/31/2017 .0025039
TISE 9/7/2017 .081268
TISE 9/14/2017 -.0069296
TISE 9/21/2017 .0309537
TISE 9/28/2017 -.0208261
TISE 10/5/2017 .0436016
TISE 10/12/2017 .0823709
TISE 10/19/2017 .0400126
TISE 10/26/2017 -.006035
TISE 11/2/2017 .0034912
TISE 11/9/2017 .0654969
TISE 11/16/2017 -.0340715
TISE 11/23/2017 -.0191064
TISE 11/30/2017 .0394067
TISE 12/7/2017 .0106674
TISE 12/14/2017 -.0359435
TISE 12/21/2017 .0285545
TISE 12/28/2017 -.0208573

I only provide the weekly data for 2 companies from 2014 to 2017. Now, I would like to calculate the variable which is standard deviations of previous two years' stock return. For example, the value of this variable of company GPRL for year 2016 will be the standard devidation of previous 104 weeks (from year 2014 to year 2015, I count that there are 104 weeks, each year has 52 weeks ).

However, there are some problem here. The number of weeks for each year maybe different . I means it is not always 52 weeks each year. So, I do not know what should I do.

Could you please help. Thank you very much in advance.

I do apologise because my data is messy. Honestly, I do not how to post it here as others do.

[Nick Cox]

https://www.statalist.org/forums/help#stata is the link I alluded to earlier, which tells you how to improve your data examples.

[Clyde Schechter]

However, there are some problem here. The number of weeks for each year maybe different . I means it is not always 52 weeks each year. So, I do not know what should I do.

Well, imagine for the moment that you were going to do these calculations by hand. (To be very, very clear, I am not recommending you actually do that--this is a thought experiment.) How would you know which weeks to include in each case? If you can explain that, it will probably not be too hard to write code that corresponds to it. Looking at your example data, it is not obvious to me why the number of weeks should vary from one instance to another, nor where the information defining the included weeks would come from.

[Celine Tran]

Dear Nick Cox and Clyde Schechter ,

For #12, I collect weekly stock price form Datastream. All data, including datem, is automatically download from Datastream. The raw data looks like:

input str10 A str17(IM IP)
"Code"       "DISA.SI(RI)~U$"    "SUNR.SI(RI)~U$"  
"  1/1/1998" "20.29"             "47.43"          
"  1/8/1998" "17.06"             "28.04"          
" 1/15/1998" "15.81"             "42.54"          
" 1/22/1998" "15.82"             "42.56"          
" 1/29/1998" "16.15"             "43.44"          
"  2/5/1998" "24.41"             "45.14"          
" 2/12/1998" "23.77"             "63.54"          
" 2/19/1998" "22.6"              "64.55"          
" 2/26/1998" "26.52"             "64.63"          
"  3/5/1998" "21.5"              "63.61"          
" 3/12/1998" "21.09"             "64.70999999999999"
" 3/19/1998" "22.07"             "65.3"            
" 3/26/1998" "23.01"             "62.59"          
"  4/2/1998" "18.72"             "61.53"          
"  4/9/1998" "18.3"              "39.24"          
" 4/16/1998" "16.59"             "65.44"          
" 4/23/1998" "16.73"             "50.3"            
" 4/30/1998" "16.79"             "50.47"          
"  5/7/1998" "14.8"              "38.41"          
" 5/14/1998" "13.76"             "48.24"          
" 5/21/1998" "15.49"             "48.88"          
" 5/28/1998" "12.1"              "41.76"          
"  6/4/1998" "11.26"             "42.94"          
" 6/11/1998" "10.18"             "35.84"          
" 6/18/1998" "11.38"             "37.41"          
" 6/25/1998" "10.65"             "36.75"          
"  7/2/1998" "9.66"              "47.02"          
"  7/9/1998" "10.32"             "43.63"          
" 7/16/1998" "9.060000000000001" "40.04"          
" 7/23/1998" "8.140000000000001" "42.27"          
" 7/30/1998" "7.37"              "42.1"            
"  8/6/1998" "8"                 "41.55"          
" 8/13/1998" "7.25"              "37"              
" 8/20/1998" "7.98"              "36.99"          
" 8/27/1998" "7.88"              "36.52"          
"  9/3/1998" "7.94"              "35.34"          
" 9/10/1998" "8.050000000000001" "35.83"          
" 9/17/1998" "8.82"              "36"              
" 9/24/1998" "7.37"              "39.1"            
" 10/1/1998" "7.5"               "39.78"          
" 10/8/1998" "6.91"              "35.27"          
"10/15/1998" "8.66"              "40.14"          
"10/22/1998" "9.380000000000001" "40.67"          
"10/29/1998" "11.73"             "37.49"          
" 11/5/1998" "12.5"              "47.83"          
"11/12/1998" "10.69"             "51.39"          
"11/19/1998" "11.71"             "54.14"          
"11/26/1998" "11.5"              "53.21"          
" 12/3/1998" "10.78"             "56.54"          
"12/10/1998" "11.66"             "57.1"            
"12/17/1998" "11.58"             "44.11"          
"12/24/1998" "10.77"             "43.94"          
"12/31/1998" "10.74"             "43.84"          
"  1/7/1999" "11.37"             "43.29"          
" 1/14/1999" "13.56"             "54.56"          
" 1/21/1999" "13.64"             "47.16"          
" 1/28/1999" "11.98"             "45.85"          
"  2/4/1999" "12"                "39.04"          
" 2/11/1999" "12"                "42.86"          
" 2/18/1999" "13.4"              "42.54"          
" 2/25/1999" "12.47"             "37.43"          
"  3/4/1999" "13.13"             "37.21"          
" 3/11/1999" "13.13"             "37.2"            
" 3/18/1999" "12.47"             "37.42"          
" 3/25/1999" "13.93"             "41.89"          
"  4/1/1999" "13.19"             "37.37"          
"  4/8/1999" "14.64"             "41.82"          
" 4/15/1999" "14.96"             "55.7"            
" 4/22/1999" "22.97"             "55.95"          
" 4/29/1999" "20.15"             "51.78"          
"  5/6/1999" "23.08"             "53.94"          
" 5/13/1999" "22.27"             "46.95"          
" 5/20/1999" "22.81"             "52.55"          
" 5/27/1999" "26.33"             "74.63"          
"  6/3/1999" "30.08"             "79.34"          
" 6/10/1999" "35.43"             "78.33"          
" 6/17/1999" "55.08"             "83.54000000000001"
" 6/24/1999" "62.5"              "91.84999999999999"
"  7/1/1999" "72.31999999999999" "108.02"          
"  7/8/1999" "55.18"             "81.41"          
" 7/15/1999" "44.74"             "57.82"          
" 7/22/1999" "41.01"             "56.3"            
" 7/29/1999" "42.76"             "59.69"          
"  8/5/1999" "39.35"             "55.59"          
" 8/12/1999" "34.06"             "52.51"          
" 8/19/1999" "36.99"             "55.45"          
" 8/26/1999" "47.15"             "65.67"          
"  9/2/1999" "42.84"             "56.74"          
"  9/9/1999" "42.77"             "58.18"          
" 9/16/1999" "41.89"             "58"              
" 9/23/1999" "41.37"             "54.27"          
" 9/30/1999" "37.23"             "53.18"          
" 10/7/1999" "40.71"             "55.38"          
"10/14/1999" "39.97"             "53.86"          
"10/21/1999" "37.92"             "51.07"          
"10/28/1999" "36.45"             "50.36"          
" 11/4/1999" "40.27"             "55.82"          
"11/11/1999" "46.1"              "60.92"          
"11/18/1999" "45.49"             "59.56"          
end

Because the raw date is exported from Datastream in a form of row, the first column is Date, the sencond column is stock price of firm 1, the thirst column is for firm 2 and so on.....

Before calculating the standard deviation that I mentioned in #10, I need to format the raw data. In details, I need to transpose the panel data to the form that all firms are in one column, and then the next column is Date. But I do not know how to do. Could you please help? The one I post in #10 is from my mamuanlly transposing.

Thank you very much in advance.

Regards,
Celine

[Clyde Schechter]

This is a -reshape- problem, not a transposition.

rename A str_date
foreach v of varlist IM-IP {
    rename `v' price_`=strtoname(`v'[1])'
}
drop in 1
destring price_*, replace
gen date = daily(str_date, "MDY")
assert missing(date) == missing(str_date)
format date %td
drop str_date

reshape long price_, i(date) j(firm) string
rename price_ price

This question really does not belong in this thread; it is off topic. While it is easy to think of these threads as dialogs between a questioner and a responder, in fact there are many others who read long on selected topics of interest, or who come and search for answers to their own questions in earlier posts. For the sake of those people and not wasting their time, it is important to keep threads on topic. In the future, when you are posing a question that is not clearly related to an existing thread, start a New Topic instead.

[Celine Tran]

Dear Clyde Schechter ,

I appreciate your help. The main question I would like to ask is related to calcuate standard deviation at #10.

I though that I can deal with "reshape" by myself ..., so that I post the question about standard deviation here. I am sorry.

Regarding your code to reshape the panel data, it seems not to work with my data. I post in a new topic and would like to mention you there.

I will back to this topic about standard deviation later.

One a gain, thank you so much.