计算 R 中每月超过 XTS 对象的滚动年回报
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【中文标题】计算 R 中每月超过 XTS 对象的滚动年回报【英文标题】：Calculate rolling annual returns from monthly over XTS object in R 【发布时间】：2019-02-12 17:53:22 【问题描述】：我有一个跨多个列的月回报 XTS 对象，我正在尝试计算每列的滚动年回报（几何）。
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
预期结果：
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
我一直在使用 PerformanceAnalytics 包，但在每列应用该函数时遇到了一些问题：
apply.rolling(ManagerReturns, width = 12, trim = FALSE ,FUN = Return.annualized)
【问题讨论】：
【参考方案1】：apply.rolling 是rollapply 的包装。出于某种原因，apply.rolling 无法正确处理您的数据，但使用 rollapply 可以解决问题。
使用rollapply 我可以接近你的结果，但是。但是 Return.annualized 删除了 NA 值但继续计算。您可以在 Manager1 和 Manager5 上看到这种情况。这不是因为rollapply，而是因为Return.annualized。例如，Return.annualized(my_data$Manager5[1:12]) 返回 0.2207884 的年化回报。
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
现在您可以执行ra * !is.na(my_data) 之类的操作，如果是 NA，则将 ra 与 0 相乘，并删除 Manager1 的最后 4 条记录。但这对Manager5没有帮助。
数据：
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")
【讨论】：
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