How to calculate three year rolling return using R-CodePudding

I need to get a 3-year rolling return working (3-year return for each id, for each year).

I have tried to use the PerformanceAnalytics package but I keep getting an error that my data is not a time series.

When I use the function it says TRUE so I am completely stuck as to how to get the 3-year rolling return to work. So I just need someone to provide me with the R code that will produce the 3-year returns.

Here's a sample dataset

ppd_id   FY   TF_1YR
1      2001  -0.0636
1      2002  -0.0929
1      2003  0.1648
1      2004  0.1006
1      2005  0.1098
1      2006  0.0837
1      2007  0.1792
1      2008  -0.1521
1      2009  -0.1003
1      2010  0.0847
1      2011  0.0221
1      2012  0.1801
1      2013  0.146
1      2014  0.1202
1      2015  0.0105
1      2016  0.1022
1      2017  0.1286
1      2018  0.0929

Here's link to dataset

Here's my code

library(smooth)
library(readr)
pensionreturns <- read_csv("pensionreturns.csv")
sma(pensionreturns, h=

CodePudding user response：

Assuming that:

we are starting out with the data frame in the Note at the end which is the data in question duplicated so that there are 2 id's
the third column represents returns so the 3 year returns are the product of one plus each of the last 3 values all minus 1

convert the data to the wide form zoo series z and then use rollapplyr. Omit the fill= argument if the NA's at the beginning are not needed. The result will be a zoo series of returns. (We could use fortify.zoo, see ?fortify.zoo, to convert it to a data frame although it will be easier to perform further time series manipulations if you leave it as a time series.)

library(zoo)

z <- read.zoo(DF2, index = 2, split = 1, FUN = c)
rollapplyr(z   1, 3, prod, fill = NA) - 1

giving this zoo series:

                1            2
2001           NA           NA
2002           NA           NA
2003 -0.010609049 -0.010609049
2004  0.162883042  0.162883042
2005  0.422740161  0.422740161
2006  0.323680900  0.323680900
2007  0.418212355  0.418212355
2008  0.083530596  0.083530596
2009 -0.100440641 -0.100440641
2010 -0.172530498 -0.172530498
2011 -0.002527919 -0.002527919
2012  0.308343674  0.308343674
2013  0.382282521  0.382282521
2014  0.514952431  0.514952431
2015  0.297228567  0.297228567
2016  0.247648627  0.247648627
2017  0.257004321  0.257004321
2018  0.359505217  0.359505217

Note

DF <- structure(list(ppd_id = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), FY = 2001:2018, TF_1YR = c(-0.0636, 
-0.0929, 0.1648, 0.1006, 0.1098, 0.0837, 0.1792, -0.1521, -0.1003, 
0.0847, 0.0221, 0.1801, 0.146, 0.1202, 0.0105, 0.1022, 0.1286, 
0.0929)), class = "data.frame", row.names = c(NA, -18L))

DF2 <- rbind(DF, transform(DF, ppd_id = 2))