Hi, Statalist!


I am writing a thesis on the stock market predictability, using a linear regression model:

Array

I have a monthly data set for the stock market. I am using 17 different macroeconomic and technical variables to predict the stock market. One of them that has been advised by my supervisor was the ''volatility'' measure. I have been trying to construct such a predicting variable, but it seems that there is a lot of different methods. Three of them that I came across were rolling standard deviation:
Array

''ARCH model'' and ''realized volatility estimated from daily data for each month'' ( i only have monthly data).

I have been trying to produce one of these methods in Stata but I don't know which approach is best in my situation since a lot of literature that I found on the topic is using daily data. Do you have any advice on what to do?

. dataex t return, count(30)


Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float t double return
 1  .01242389
 2  .02019115
 3  .02964644
 4  .03405454
 5  .02848361
 6 -.01542547
 7 -.00863808
 8  .01915629
 9  .01136751
10 -.05886838
11  .03742423
12   .0257288
13  .02951672
14  .00101633
15  .04072984
16  .03142682
17 -.00213872
18 -.00795212
19 -.00034236
20 -.00761387
21 -.01335902
22  .01832136
23 -.00212152
24  .03346823
25  .01082923
26  .01575765
27 -.01307993
28 -.00878622
29  .01496919
30 -.01535528
end

Listed 30 out of 1764 observations


Thank you in advance for all the help, and if I am unclear in any way, please let me know.