I have an XTS object of monthly returns across multiple columns, I'm trying to calculate rolling annual returns (geometric) for each column.
Date Manager 1 Manager 2 Manager 3 Manager 4 Manager 5
20160430 0.0152000 0.0100700 0.0102210 0.0046160 NA
20160531 0.0462000 0.0515240 0.0287490 0.0374920 NA
20160630 0.0007000 0.0126830 0.0156410 0.0130820 NA
20160731 0.0200000 0.0158810 0.0239540 0.0214950 NA
20160831 0.0339000 0.0531980 0.0021170 0.0476160 0.0457650
20160930 -0.0071000 0.0047540 -0.0088080 0.0031540 -0.0034070
20161031 -0.0224000 -0.0181930 0.0181410 -0.0048280 0.0170850
20161130 -0.0439000 -0.0131600 -0.0243030 -0.0064650 -0.0007180
20161231 -0.0051000 0.0200130 0.0204210 0.0160740 0.0172270
20170131 0.0083000 0.0146560 0.0247000 0.0203410 0.0227060
20170228 0.0211000 -0.0067120 0.0257530 0.0029940 0.0124730
20170331 0.0530000 0.0532190 0.0283950 0.0416190 0.0237900
20170430 0.0638300 0.0592280 0.0341340 0.0437430 0.0293500
20170531 0.0339000 0.0264270 0.0287670 0.0207810 0.0179080
20170630 NA -0.0046950 -0.0091310 -0.0074520 -0.0137600
20170731 NA 0.0109280 0.0029630 0.0146560 0.0167990
20170831 NA 0.0290430 0.0372960 0.0284390 0.0229930
20170930 NA 0.0226390 0.0030190 0.0063850 -0.0087170
Exepcted Results:
Date Manager 1 Manager 2 Manager 3 Manager 4 Manager 5
20160430
20160531
20160630
20160731
20160831
20160930
20161031
20161130
20161231
20170131
20170228
20170331 0.121979182 0.212964432 0.176317288 0.213932804
20170430 0.175724107 0.271996881 0.204161963 0.261212111
20170531 0.161901314 0.241637796 0.204183032 0.240897626
20170630 0.220330851 0.174812396 0.215746067
20170731 0.214381041 0.150728807 0.207606539 0.200188843
20170831 0.186529323 0.191124778 0.185500853 0.174054195
20170930 0.207649992 0.205337395 0.189319163 0.167798654
I've been using the PerformanceAnalytics package, but having some trouble applying the function across each column:
apply.rolling(ManagerReturns, width = 12, trim = FALSE ,FUN = Return.annualized)
apply.rolling is a wrapper around rollapply. For some reason apply.rolling doesn't work correctly with your data, but using rollapply will solve the issue.
using rollapply I can get close to your outcome, with a but. The but is that the Return.annualized removes the NA values but continues to calculate. You can see this happening with Manager1 and Manager5. This is not because rollapply, but because of Return.annualized. For example Return.annualized(my_data$Manager5[1:12]) returns an annualized return of 0.2207884.
ra <- rollapply(my_data, width = 12, FUN = Return.annualized, fill = 0)
Manager1 Manager2 Manager3 Manager4 Manager5
2016-04-30 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
2016-05-31 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
2016-06-30 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
2016-07-31 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
2016-08-31 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
2016-09-30 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
2016-10-31 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
2016-11-30 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
2016-12-31 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
2017-01-31 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
2017-02-28 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
2017-03-31 0.1219792 0.2129644 0.1763173 0.2139328 0.2207884
2017-04-30 0.1757241 0.2719969 0.2041620 0.2612121 0.2409790
2017-05-31 0.1619013 0.2416378 0.2041830 0.2408976 0.2406184
2017-06-30 0.1769613 0.2203309 0.1748124 0.2157461 0.1982881
2017-07-31 0.1682027 0.2143810 0.1507288 0.2076065 0.2001888
2017-08-31 0.1368823 0.1865293 0.1911248 0.1855009 0.1740542
2017-09-30 0.1676742 0.2076500 0.2053374 0.1893192 0.1677987
Now you could do something like ra * !is.na(my_data) which will multiply ra with a 0 in case of NA's and will remove the last 4 records of Manager1. But it will not help with Manager5.
data:
my_data <- structure(c(0.0152, 0.0462, 7e-04, 0.02, 0.0339, -0.0071, -0.0224,
-0.0439, -0.0051, 0.0083, 0.0211, 0.053, 0.06383, 0.0339, NA,
NA, NA, NA, 0.01007, 0.051524, 0.012683, 0.015881, 0.053198,
0.004754, -0.018193, -0.01316, 0.020013, 0.014656, -0.006712,
0.053219, 0.059228, 0.026427, -0.004695, 0.010928, 0.029043,
0.022639, 0.010221, 0.028749, 0.015641, 0.023954, 0.002117, -0.008808,
0.018141, -0.024303, 0.020421, 0.0247, 0.025753, 0.028395, 0.034134,
0.028767, -0.009131, 0.002963, 0.037296, 0.003019, 0.004616,
0.037492, 0.013082, 0.021495, 0.047616, 0.003154, -0.004828,
-0.006465, 0.016074, 0.020341, 0.002994, 0.041619, 0.043743,
0.020781, -0.007452, 0.014656, 0.028439, 0.006385, NA, NA, NA,
NA, 0.045765, -0.003407, 0.017085, -0.000718, 0.017227, 0.022706,
0.012473, 0.02379, 0.02935, 0.017908, -0.01376, 0.016799, 0.022993,
-0.008717), .Dim = c(18L, 5L), .Dimnames = list(NULL, c("Manager1",
"Manager2", "Manager3", "Manager4", "Manager5")), index = structure(c(1461974400,
1464652800, 1467244800, 1469923200, 1472601600, 1475193600, 1477872000,
1480464000, 1483142400, 1485820800, 1488240000, 1490918400, 1493510400,
1496188800, 1498780800, 1501459200, 1504137600, 1506729600), tzone = "UTC", tclass = "Date"), class = c("xts",
"zoo"), .indexCLASS = "Date", tclass = "Date", .indexTZ = "UTC", tzone = "UTC")