I would love to analyze monthly stock returns for multiple companies (Panel Data). However, I am struggling with calculating the standard deviation for the last X months by company.
Basically, I want to add another column to my existing data.frame, in which the standard deviation according to a moving window of X months by company is shown. Please find below a simplified example of my data and what I would love to achieve.
#My data:
company = c("1","1","1","1","1","2","2","2","2","2","2","2","3","3","3","3","4","4","4")
return = c(0.01,0.015,-0.01,0.02,0.023,-0.04,-0.02,-0.01,0.05,0.06,0.03,-0.09,0.2,0.3,-0.04,-0.02,-0.01,0.023,-0.04)
stock = data.frame(company,return)
Given this initial situation, I would love to calculate the standard deviation in another column, based on e.g. 3 observations.
#Column to be filled with the respective value
stock["std_3obs"] = NA
#However, I do not manage to fill this column accordingly. The following result for a given row is expected:
#row 1 = Not possible, as there are not enough prior observations available
#row 2 = Not possible, as there are not enough prior observations available
#row 3 = sd(c(0.01,0.015,-0.01) = 0.01322876
#row 7 = Not possible, as there are not enough prior observations available
#row 8 = sd(c(-0.040,-0.020,-0.010)) = 0.01527525
Thanks a lot in advance! Any help is highly appreciated! Please be gentle, as I am fairly new to R.
*Side Note: Research on this issue and adapting other solutions always lead to this error: replacement has X rows, data has Y * where X >>> Y
You can use the rolling functions from zoo package :
library(dplyr)
stock %>%
group_by(company) %>%
mutate(std_3obs = zoo::rollapplyr(return, 3, sd, fill = NA))
# company return std_3obs
# <chr> <dbl> <dbl>
# 1 1 0.01 NA
# 2 1 0.015 NA
# 3 1 -0.01 0.0132
# 4 1 0.02 0.0161
# 5 1 0.023 0.0182
# 6 2 -0.04 NA
# 7 2 -0.02 NA
# 8 2 -0.01 0.0153
# 9 2 0.05 0.0379
#10 2 0.06 0.0379
#11 2 0.03 0.0153
#12 2 -0.09 0.0794
#13 3 0.2 NA
#14 3 0.3 NA
#15 3 -0.04 0.175
#16 3 -0.02 0.191
#17 4 -0.01 NA
#18 4 0.023 NA
#19 4 -0.04 0.0315