I' m following the Campbell et al.(2008) to calculate the failure probability of firms.
Now I have a panel data of the firms' daily stock returns, and try to calculate each firm's annualized three-month rolling standard deviation.
the equation is like this:
Array
k is the index of trading days in month t-1, t-2, t-3, N is the total number of trading days in the three month period.
SIGMA is treated as missing if there are less than five nonzero observations over the three months in the rolling window
I've tried the following code I googled
Code:
*month identifier gen mofd = mofd(date) format mofd %tm sort permno date *3 month rolling window sd bys permno: asrol ret, window(mdate 3) stat(sd) *annualized gen annual_sd = sd3_ret * sqrt(12) / sqrt(3)
so I wonder if anyone could help me:
1. to find the code which can generate three-month rolling sample standard deviation based on the actually trading days ?
2. to count the nonzero observations over the three month rolling window.
here's part of my data.
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input double permno long date double ret float mdate 10000 10024 . 329 10001 9505 . 312 10001 9506 .021739130839705467 312 10001 9509 0 312 10001 9510 0 312 10001 9511 .03191489353775978 312 10001 9512 .030927835032343864 312 10001 9513 0 312 10001 9516 .009999999776482582 312 10001 9517 -.019801979884505272 312 10001 9518 0 312 10001 9519 0 312 10001 9520 0 312 10001 9523 0 312 10001 9524 -.010101010091602802 312 10001 9525 -.010204081423580647 312 10001 9526 0 312 10001 9527 .010309278033673763 312 10001 9530 0 313 10001 9531 0 313 10001 9532 0 313 10001 9533 0 313 10001 9534 0 313 10001 9537 .010204081423580647 313 10001 9538 .020202020183205605 313 10001 9539 -.009900989942252636 313 10001 9540 0 313 10001 9541 -.009999999776482582 313 10001 9545 0 313 10001 9546 .010101010091602802 313 10001 9547 0 313 10001 9548 0 313 10001 9551 .009999999776482582 313 10001 9552 0 313 10001 9553 0 313 10001 9554 .009900989942252636 313 10001 9555 -.019607843831181526 313 10001 9558 0 314 10001 9559 0 314 10001 9560 0 314 10001 9561 .009999999776482582 314 10001 9562 -.009900989942252636 314 10001 9565 .015200004912912846 314 10001 9566 0 314 10001 9567 0 314 10001 9568 0 314 10001 9569 0 314 10001 9572 0 314 10001 9573 0 314 10001 9574 0 314 10001 9575 0 314 10001 9576 .019999999552965164 314 10001 9579 0 314 10001 9580 -.009803921915590763 314 10001 9581 0 314 10001 9582 0 314 10001 9586 0 314 10001 9587 0 315 10001 9588 0 315 10001 9589 0 315 10001 9590 0 315 10001 9593 0 315 10001 9594 0 315 10001 9595 0 315 10001 9596 0 315 10001 9597 0 315 10001 9600 0 315 10001 9601 0 315 10001 9602 0 315 10001 9603 0 315 10001 9604 0 315 10001 9607 .009900989942252636 315 10001 9608 0 315 10001 9609 0 315 10001 9610 0 315 10001 9611 0 315 10001 9614 0 315 10001 9615 0 315 10001 9616 0 315 10001 9617 0 316 10001 9618 0 316 10001 9621 0 316 10001 9622 0 316 10001 9623 0 316 10001 9624 0 316 10001 9625 0 316 10001 9628 0 316 10001 9629 0 316 10001 9630 0 316 10001 9631 0 316 10001 9632 0 316 10001 9635 0 316 10001 9636 0 316 10001 9637 0 316 10001 9638 -.009803921915590763 316 10001 9639 0 316 10001 9643 0 316 10001 9644 0 316 10001 9645 0 316 10001 9646 0 316 end format %d date format %tm mdate
Best regards,
Noah Liu
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