Apply it to your data 12
Which of the assets the largest contributor to the portfolio volatility?
Brady Harvie
2025-11-19
# Load packages
# Core
library(tidyverse)
library(tidyquant)
library(readr)
# Time series
library(lubridate)
library(tibbletime)
# modeling
library(broom)Goal
Examine how each asset contributes to portfolio standard deviation. This is to ensure that our risk is not concentrated in any one asset.
1 Import stock prices
Choose your stocks from 2012-12-31 to present.
symbols <- c("TSLA", "MSFT", "NKE", "JPM", "AAPL")
prices <- tq_get(x = symbols,
get = "stock.prices",
from = "2012-12-31")2 Convert prices to returns (monthly)
asset_returns_tbl <- prices %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
type = "log") %>%
slice(-1) %>%
ungroup() %>%
set_names(c("asset", "date", "returns"))3 Calculate Component Contribution to Portfolio Volatility
# Transform data into wide form
asset_returns_wide_tbl <- asset_returns_tbl %>%
pivot_wider(names_from = asset, values_from = returns) %>%
column_to_rownames(var = "date")
asset_returns_wide_tbl## AAPL JPM MSFT NKE TSLA
## 2013-01-31 -1.555896e-01 0.074549669 0.0273281679 0.0463878076 0.102078114
## 2013-02-28 -2.561110e-02 0.038975453 0.0209152190 0.0114291503 -0.074128640
## 2013-03-28 2.850544e-03 -0.030299432 0.0287202422 0.0802400683 0.084208138
## 2013-04-30 2.711615e-04 0.038370224 0.1457769843 0.0749068325 0.354111531
## 2013-05-31 2.217173e-02 0.107826499 0.0599410845 -0.0276354789 0.593716684
## 2013-06-28 -1.258962e-01 -0.033528885 -0.0103686374 0.0322349798 0.093672163
## 2013-07-31 1.321028e-01 0.061462568 -0.0813947587 -0.0120064580 0.223739522
## 2013-08-30 8.044284e-02 -0.097951423 0.0548546234 0.0017291142 0.229971642
## 2013-09-30 -2.172315e-02 0.022697226 -0.0035993926 0.1452419787 0.134706620
## 2013-10-31 9.201506e-02 0.004433629 0.0620378332 0.0420549954 -0.189806595
## 2013-11-29 6.770807e-02 0.104545664 0.0815624709 0.0436478383 -0.228409405
## 2013-12-31 8.861779e-03 0.021781333 -0.0190635066 -0.0032096479 0.167108541
## 2014-01-31 -1.139484e-01 -0.048308186 0.0114284158 -0.0764781573 0.187261714
## 2014-02-28 5.591792e-02 0.026031053 0.0198150934 0.0752105461 0.299722785
## 2014-03-31 1.975618e-02 0.066219733 0.0676173526 -0.0583763371 -0.160783242
## 2014-04-30 9.476115e-02 -0.074830576 -0.0144983919 -0.0123967259 -0.002690122
## 2014-05-30 7.576481e-02 -0.007351308 0.0203073199 0.0560128851 -0.000577422
## 2014-06-30 2.728657e-02 0.036226399 0.0183939603 0.0082872627 0.144457224
## 2014-07-31 2.832624e-02 0.007833555 0.0344127893 -0.0054309227 -0.072372676
## 2014-08-29 7.465170e-02 0.030398506 0.0574849640 0.0212580229 0.188794007
## 2014-09-30 -1.722053e-02 0.013200845 0.0202639852 0.1271460889 -0.105566477
## 2014-10-31 6.948900e-02 0.010691270 0.0126468768 0.0413957407 -0.004046477
## 2014-11-28 1.007311e-01 -0.005305143 0.0244388862 0.0657677131 0.011599801
## 2014-12-31 -7.460642e-02 0.039437908 -0.0288583659 -0.0292642183 -0.094774519
## 2015-01-30 5.961154e-02 -0.134036725 -0.1395468035 -0.0414072845 -0.088365289
## 2015-02-27 9.601591e-02 0.119456915 0.0890367695 0.0543413966 -0.001277808
## 2015-03-31 -3.187422e-02 -0.011488786 -0.0755302700 0.0325175550 -0.074350051
## 2015-04-30 5.769635e-03 0.049912966 0.1792017172 -0.0149627575 0.180226808
## 2015-05-29 4.434131e-02 0.039062509 -0.0308043535 0.0309563662 0.103899574
## 2015-06-30 -3.793790e-02 0.029656532 -0.0595710908 0.0605841811 0.067300935
## 2015-07-31 -3.348139e-02 0.017814066 0.0561508125 0.0645271023 -0.007896616
## 2015-08-31 -6.848851e-02 -0.066827077 -0.0639507777 -0.0305792061 -0.066366250
## 2015-09-30 -2.205791e-02 -0.050062238 0.0168608851 0.0982087930 -0.002653519
## 2015-10-30 8.011232e-02 0.059589022 0.1733947749 0.0634859979 -0.182659777
## 2015-11-30 -5.821134e-03 0.037123310 0.0386863400 0.0094943548 0.106828586
## 2015-12-31 -1.167900e-01 -0.009795582 0.0205777610 -0.0541858823 0.041471519
## 2016-01-29 -7.822393e-02 -0.097447106 -0.0070544578 -0.0078707765 -0.227360626
## 2016-02-29 -1.288349e-03 -0.055281675 -0.0723442836 -0.0067963298 0.003810669
## 2016-03-31 1.197460e-01 0.050564753 0.0820363196 0.0006247027 0.179948109
## 2016-04-29 -1.507312e-01 0.072421467 -0.1020864559 -0.0420291586 0.046721797
## 2016-05-31 6.931447e-02 0.032228035 0.0678426024 -0.0651946423 -0.075597968
## 2016-06-30 -4.359642e-02 -0.049142437 -0.0351387366 0.0025546376 -0.050296440
## 2016-07-29 8.623536e-02 0.036779007 0.1022680737 0.0054200452 0.100785334
## 2016-08-31 2.337613e-02 0.053713008 0.0198809863 0.0378335738 -0.102058091
## 2016-09-30 6.344845e-02 -0.013573309 0.0024333444 -0.0877707388 -0.038366372
## 2016-10-31 4.324742e-03 0.046556644 0.0394879345 -0.0480495747 -0.031364583
## 2016-11-30 -2.183717e-02 0.146281429 0.0123909402 -0.0021945347 -0.043041267
## 2016-12-30 4.684062e-02 0.073564444 0.0307215781 0.0186661315 0.120665178
## 2017-01-31 4.664158e-02 -0.013906877 0.0395980147 0.0399166536 0.164624916
## 2017-02-28 1.255553e-01 0.068386121 -0.0043733118 0.0774509121 -0.007730364
## 2017-03-31 4.754185e-02 -0.031157955 0.0289606838 -0.0222248484 0.107278727
## 2017-04-28 -6.974618e-05 -0.003879573 0.0387185908 -0.0057585537 0.120916212
## 2017-05-31 6.560702e-02 -0.057361574 0.0256724230 -0.0446566444 0.082295892
## 2017-06-30 -5.891592e-02 0.106699010 -0.0131151678 0.1108367409 0.058654468
## 2017-07-31 3.218079e-02 0.009852320 0.0532497371 0.0008469037 -0.111459860
## 2017-08-31 1.016529e-01 -0.009962552 0.0333893149 -0.1082531177 0.095543446
## 2017-09-29 -6.213489e-02 0.049580951 -0.0037519145 -0.0183456975 -0.042474144
## 2017-10-31 9.240380e-02 0.057849386 0.1103419800 0.0587964441 -0.028457409
## 2017-11-30 2.007521e-02 0.038126918 0.0168411100 0.0941688172 -0.070862541
## 2017-12-29 -1.536336e-02 0.022889250 0.0161455604 0.0379618441 0.008061928
## 2018-01-31 -1.069352e-02 0.083669116 0.1049981780 0.0867716061 0.129254571
## 2018-02-28 6.596094e-02 -0.001470657 -0.0084507813 -0.0175977176 -0.032266877
## 2018-03-29 -5.980357e-02 -0.049062932 -0.0270228068 -0.0058207229 -0.253920408
## 2018-04-30 -1.513394e-02 -0.005821929 0.0243532478 0.0289273502 0.099254576
## 2018-05-31 1.267421e-01 -0.016405205 0.0596519636 0.0486581023 -0.031698139
## 2018-06-29 -9.463225e-03 -0.026610394 -0.0023295767 0.1069231359 0.186043257
## 2018-07-31 2.759896e-02 0.103604835 0.0730207858 -0.0353827683 -0.140021491
## 2018-08-31 1.826736e-01 -0.003223966 0.0610882609 0.0689795020 0.011737389
## 2018-09-28 -8.337726e-03 -0.015302446 0.0179980881 0.0301964126 -0.130439038
## 2018-10-31 -3.095146e-02 -0.027461127 -0.0683874373 -0.1213305162 0.242170576
## 2018-11-30 -1.999128e-01 0.019709080 0.0417977833 0.0040296004 0.038271580
## 2018-12-31 -1.240887e-01 -0.130157755 -0.0877903744 -0.0131317871 -0.051761952
## 2019-01-31 5.368727e-02 0.066576942 0.0277686887 0.0992995753 -0.080628789
## 2019-02-28 4.380270e-02 0.008275074 0.0745113702 0.0459483146 0.041032981
## 2019-03-29 9.260248e-02 -0.030451537 0.0514094265 -0.0153199195 -0.133656413
## 2019-04-30 5.490125e-02 0.144249014 0.1019632337 0.0420893416 -0.159123803
## 2019-05-31 -1.326324e-01 -0.090959554 -0.0507468410 -0.1270014485 -0.253945372
## 2019-06-28 1.226769e-01 0.053649663 0.0798438310 0.0845993242 0.188012109
## 2019-07-31 7.361715e-02 0.043933704 0.0170963852 0.0244746131 0.078092373
## 2019-08-30 -1.659835e-02 -0.054383423 0.0149251823 -0.0153644133 -0.068516948
## 2019-09-30 7.042293e-02 0.068847232 0.0084507581 0.1056920642 0.065449565
## 2019-10-31 1.049764e-01 0.067598284 0.0307390208 -0.0476461863 0.268061253
## 2019-11-29 7.469368e-02 0.053308366 0.0577616592 0.0456631267 0.046592176
## 2019-12-31 9.420379e-02 0.056365535 0.0409013361 0.0803304128 0.237359743
## 2020-01-31 5.260223e-02 -0.045422719 0.0764560801 -0.0507165542 0.441578342
## 2020-02-28 -1.218309e-01 -0.130987429 -0.0467647537 -0.0718011265 0.026424253
## 2020-03-31 -7.231392e-02 -0.254395172 -0.0269000318 -0.0771937531 -0.242781458
## 2020-04-30 1.444239e-01 0.072040273 0.1278000957 0.0522720284 0.400209535
## 2020-05-29 8.166680e-02 0.016056248 0.0250743501 0.1253848850 0.065730500
## 2020-06-30 1.374865e-01 -0.033968653 0.1048638181 -0.0053911405 0.257108657
## 2020-07-31 1.528342e-01 0.036757080 0.0073435822 -0.0044971279 0.281420674
## 2020-08-31 1.960349e-01 0.036075786 0.0978088371 0.1387488104 0.554719320
## 2020-09-30 -1.081718e-01 -0.039911711 -0.0697754676 0.1151080585 -0.149762306
## 2020-10-30 -6.188802e-02 0.027455326 -0.0380861001 -0.0444661433 -0.100371771
## 2020-11-30 9.120469e-02 0.184291572 0.0583261568 0.1148920792 0.380308519
## 2020-12-31 1.084717e-01 0.075071083 0.0382645108 0.0510475423 0.217730753
## 2021-01-29 -5.516474e-03 0.019688641 0.0419973023 -0.0573123949 0.117343701
## 2021-02-26 -8.306863e-02 0.134337417 0.0041095520 0.0108995928 -0.161038203
## 2021-03-31 7.312613e-03 0.033803956 0.0144828515 -0.0141220542 -0.011269836
## 2021-04-30 7.345297e-02 0.016198194 0.0672862537 -0.0020339731 0.060292565
## 2021-05-28 -5.181668e-02 0.065610759 -0.0076565538 0.0305596017 -0.126372343
## 2021-06-30 9.450002e-02 -0.054426023 0.0815697881 0.1240976239 0.083547955
## 2021-07-30 6.295843e-02 -0.018724890 0.0504236656 0.0809136527 0.010973846
## 2021-08-31 4.161136e-02 0.052429272 0.0597685016 -0.0150256315 0.068224268
## 2021-09-30 -7.046167e-02 0.023113122 -0.0684060031 -0.1260499364 0.052632612
## 2021-10-29 5.700134e-02 0.043184555 0.1623661354 0.1414101694 0.362230202
## 2021-11-30 9.991934e-02 -0.067316514 -0.0012828541 0.0115891288 0.027237848
## 2021-12-31 7.160300e-02 -0.003026684 0.0171843399 -0.0135063034 -0.079968440
## 2022-01-31 -1.583707e-02 -0.057573476 -0.0783345858 -0.1183306406 -0.120597475
## 2022-02-28 -5.558245e-02 -0.046840509 -0.0379221423 -0.0809942857 -0.073397011
## 2022-03-31 5.588243e-02 -0.039412246 0.0313648959 -0.0124017431 0.213504290
## 2022-04-29 -1.021775e-01 -0.125476025 -0.1052128951 -0.0760991067 -0.213125289
## 2022-05-31 -5.603715e-02 0.102398409 -0.0182426077 -0.0480487606 -0.138340062
## 2022-06-30 -8.493698e-02 -0.160612226 -0.0569095631 -0.1484559805 -0.118657126
## 2022-07-29 1.728046e-01 0.032933900 0.0890143117 0.1173046645 0.280480149
## 2022-08-31 -3.170527e-02 -0.014230406 -0.0689889486 -0.0765610259 -0.075250271
## 2022-09-30 -1.289443e-01 -0.084640318 -0.1157102876 -0.2445217410 -0.038313992
## 2022-10-31 1.039557e-01 0.195049348 -0.0033114907 0.1088674839 -0.153346760
## 2022-11-30 -3.358515e-02 0.093227951 0.0973290178 0.1685054791 -0.155866121
## 2022-12-30 -1.304192e-01 -0.029971260 -0.0619237600 0.0676657853 -0.457813194
## 2023-01-31 1.048295e-01 0.050130385 0.0327737145 0.0845228135 0.340915767
## 2023-02-28 2.291828e-02 0.023932526 0.0089773456 -0.0694248240 0.171904973
## 2023-03-31 1.121214e-01 -0.095372943 0.1448634836 0.0347432649 0.008471140
## 2023-04-28 2.857494e-02 0.066892452 0.0636927152 0.0327266503 -0.233183710
## 2023-05-31 4.502923e-02 -0.018470840 0.0686912449 -0.1855463998 0.216021889
## 2023-06-30 9.014240e-02 0.069243124 0.0363307006 0.0506910085 0.249689452
## 2023-07-31 1.270443e-02 0.089422534 -0.0136595101 0.0001812493 0.021391609
## 2023-08-31 -4.330830e-02 -0.076477651 -0.0224764898 -0.0818939056 -0.035588260
## 2023-09-29 -9.285928e-02 -0.008992658 -0.0373308940 -0.0583951365 -0.030929027
## 2023-10-31 -2.573389e-03 -0.034614450 0.0684206791 0.0721114630 -0.219831983
## 2023-11-30 1.077599e-01 0.115463124 0.1159548882 0.0704384884 0.178463656
## 2023-12-29 1.349119e-02 0.086017829 -0.0076032241 -0.0121757897 0.034390133
## 2024-01-31 -4.314468e-02 0.030883170 0.0557006051 -0.0670408664 -0.282704160
## 2024-02-29 -1.871736e-02 0.064946998 0.0414474954 0.0233633911 0.075015304
## 2024-03-28 -5.264814e-02 0.073747084 0.0169714305 -0.0970692681 -0.138383423
## 2024-04-30 -6.728948e-03 -0.037859866 -0.0775402278 -0.0184713257 0.041724969
## 2024-05-31 1.224143e-01 0.055241399 0.0659666434 0.0297925148 -0.028782134
## 2024-06-28 9.125898e-02 -0.001827738 0.0738549800 -0.2280936492 0.105427913
## 2024-07-31 5.298219e-02 0.056324675 -0.0661283765 -0.0067893818 0.159378271
## 2024-08-30 3.184116e-02 0.054858322 -0.0010956475 0.1070686172 -0.080549177
## 2024-09-30 1.731639e-02 -0.064016755 0.0310605998 0.0636341593 0.200441406
## 2024-10-31 -3.090177e-02 0.057231914 -0.0572677513 -0.1363797092 -0.046070548
## 2024-11-29 5.041448e-02 0.118023364 0.0432438967 0.0210397191 0.323147326
## 2024-12-31 5.368788e-02 -0.040910361 -0.0046392831 -0.0350592955 0.157010663
## 2025-01-31 -5.930769e-02 0.114093001 -0.0153967911 0.0161241073 0.001880189
## 2025-02-28 2.554346e-02 -0.009963511 -0.0425088050 0.0323703069 -0.322794555
## 2025-03-31 -8.501352e-02 -0.075926073 -0.0559455554 -0.2191026418 -0.122658739
## 2025-04-30 -4.432081e-02 0.003364587 0.0515782678 -0.1182557516 0.085028653
## 2025-05-30 -5.507312e-02 0.076243137 0.1542991057 0.0716607065 0.205293048
## 2025-06-30 2.128049e-02 0.093621408 0.0774059356 0.1657368406 -0.086785707
## 2025-07-31 1.162981e-02 0.026405421 0.0700443986 0.0501031776 -0.030005653
## 2025-08-29 1.130062e-01 0.017334807 -0.0499171172 0.0352529047 0.079775694
## 2025-09-30 9.247092e-02 0.045432092 0.0219793711 -0.0987850620 0.286693192
## 2025-10-31 5.997984e-02 -0.008908117 -0.0002703949 -0.0765711460 0.026275247
## 2025-11-28 3.185157e-02 0.006280032 -0.0492394175 0.0006191134 -0.059539610# Covariance of asset returns
covariance_matrix <- cov(asset_returns_wide_tbl)
covariance_matrix## AAPL JPM MSFT NKE TSLA
## AAPL 0.005901267 0.001301092 0.002171267 0.001831540 0.005255095
## JPM 0.001301092 0.004471347 0.001514195 0.001889714 0.002247494
## MSFT 0.002171267 0.001514195 0.003547420 0.001736476 0.003256758
## NKE 0.001831540 0.001889714 0.001736476 0.005775061 0.001620029
## TSLA 0.005255095 0.002247494 0.003256758 0.001620029 0.028868199# Standard deviation of portfolio
# Summarizes how much each asset's returns vary with those of other assets within the portfolio into a single number
w <- c(0.25, 0.25, 0.2, 0.2, 0.1)
sd_portfolio <- sqrt(t(w) %*% covariance_matrix %*% w)
sd_portfolio## [,1]
## [1,] 0.05405827# Component contribution
# Similar to the formula for sd_portfolio
# Mathematical trick to summarize the same, sd_portfolio, by asset instead of a single number
component_contribution <- (t(w) %*% covariance_matrix * w) / sd_portfolio[1,1]
component_contribution## AAPL JPM MSFT NKE TSLA
## [1,] 0.01445968 0.01086162 0.008523471 0.00959936 0.01061415rowSums(component_contribution)## [1] 0.05405827# Component contribution in percentage
component_percentages <- (component_contribution / sd_portfolio[1,1]) %>%
round(3) %>%
as_tibble()
component_percentages## # A tibble: 1 × 5
## AAPL JPM MSFT NKE TSLA
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.267 0.201 0.158 0.178 0.196component_percentages %>%
as_tibble() %>%
gather(key = "asset", value = "contribution")## # A tibble: 5 × 2
## asset contribution
## <chr> <dbl>
## 1 AAPL 0.267
## 2 JPM 0.201
## 3 MSFT 0.158
## 4 NKE 0.178
## 5 TSLA 0.1964 Component Contribution with a Custom Function
sset_returns_wide_tbl <- asset_returns_tbl %>%
pivot_wider(names_from = asset, values_from = returns) %>%
column_to_rownames(var = "date")
asset_returns_wide_tbl## AAPL JPM MSFT NKE TSLA
## 2013-01-31 -1.555896e-01 0.074549669 0.0273281679 0.0463878076 0.102078114
## 2013-02-28 -2.561110e-02 0.038975453 0.0209152190 0.0114291503 -0.074128640
## 2013-03-28 2.850544e-03 -0.030299432 0.0287202422 0.0802400683 0.084208138
## 2013-04-30 2.711615e-04 0.038370224 0.1457769843 0.0749068325 0.354111531
## 2013-05-31 2.217173e-02 0.107826499 0.0599410845 -0.0276354789 0.593716684
## 2013-06-28 -1.258962e-01 -0.033528885 -0.0103686374 0.0322349798 0.093672163
## 2013-07-31 1.321028e-01 0.061462568 -0.0813947587 -0.0120064580 0.223739522
## 2013-08-30 8.044284e-02 -0.097951423 0.0548546234 0.0017291142 0.229971642
## 2013-09-30 -2.172315e-02 0.022697226 -0.0035993926 0.1452419787 0.134706620
## 2013-10-31 9.201506e-02 0.004433629 0.0620378332 0.0420549954 -0.189806595
## 2013-11-29 6.770807e-02 0.104545664 0.0815624709 0.0436478383 -0.228409405
## 2013-12-31 8.861779e-03 0.021781333 -0.0190635066 -0.0032096479 0.167108541
## 2014-01-31 -1.139484e-01 -0.048308186 0.0114284158 -0.0764781573 0.187261714
## 2014-02-28 5.591792e-02 0.026031053 0.0198150934 0.0752105461 0.299722785
## 2014-03-31 1.975618e-02 0.066219733 0.0676173526 -0.0583763371 -0.160783242
## 2014-04-30 9.476115e-02 -0.074830576 -0.0144983919 -0.0123967259 -0.002690122
## 2014-05-30 7.576481e-02 -0.007351308 0.0203073199 0.0560128851 -0.000577422
## 2014-06-30 2.728657e-02 0.036226399 0.0183939603 0.0082872627 0.144457224
## 2014-07-31 2.832624e-02 0.007833555 0.0344127893 -0.0054309227 -0.072372676
## 2014-08-29 7.465170e-02 0.030398506 0.0574849640 0.0212580229 0.188794007
## 2014-09-30 -1.722053e-02 0.013200845 0.0202639852 0.1271460889 -0.105566477
## 2014-10-31 6.948900e-02 0.010691270 0.0126468768 0.0413957407 -0.004046477
## 2014-11-28 1.007311e-01 -0.005305143 0.0244388862 0.0657677131 0.011599801
## 2014-12-31 -7.460642e-02 0.039437908 -0.0288583659 -0.0292642183 -0.094774519
## 2015-01-30 5.961154e-02 -0.134036725 -0.1395468035 -0.0414072845 -0.088365289
## 2015-02-27 9.601591e-02 0.119456915 0.0890367695 0.0543413966 -0.001277808
## 2015-03-31 -3.187422e-02 -0.011488786 -0.0755302700 0.0325175550 -0.074350051
## 2015-04-30 5.769635e-03 0.049912966 0.1792017172 -0.0149627575 0.180226808
## 2015-05-29 4.434131e-02 0.039062509 -0.0308043535 0.0309563662 0.103899574
## 2015-06-30 -3.793790e-02 0.029656532 -0.0595710908 0.0605841811 0.067300935
## 2015-07-31 -3.348139e-02 0.017814066 0.0561508125 0.0645271023 -0.007896616
## 2015-08-31 -6.848851e-02 -0.066827077 -0.0639507777 -0.0305792061 -0.066366250
## 2015-09-30 -2.205791e-02 -0.050062238 0.0168608851 0.0982087930 -0.002653519
## 2015-10-30 8.011232e-02 0.059589022 0.1733947749 0.0634859979 -0.182659777
## 2015-11-30 -5.821134e-03 0.037123310 0.0386863400 0.0094943548 0.106828586
## 2015-12-31 -1.167900e-01 -0.009795582 0.0205777610 -0.0541858823 0.041471519
## 2016-01-29 -7.822393e-02 -0.097447106 -0.0070544578 -0.0078707765 -0.227360626
## 2016-02-29 -1.288349e-03 -0.055281675 -0.0723442836 -0.0067963298 0.003810669
## 2016-03-31 1.197460e-01 0.050564753 0.0820363196 0.0006247027 0.179948109
## 2016-04-29 -1.507312e-01 0.072421467 -0.1020864559 -0.0420291586 0.046721797
## 2016-05-31 6.931447e-02 0.032228035 0.0678426024 -0.0651946423 -0.075597968
## 2016-06-30 -4.359642e-02 -0.049142437 -0.0351387366 0.0025546376 -0.050296440
## 2016-07-29 8.623536e-02 0.036779007 0.1022680737 0.0054200452 0.100785334
## 2016-08-31 2.337613e-02 0.053713008 0.0198809863 0.0378335738 -0.102058091
## 2016-09-30 6.344845e-02 -0.013573309 0.0024333444 -0.0877707388 -0.038366372
## 2016-10-31 4.324742e-03 0.046556644 0.0394879345 -0.0480495747 -0.031364583
## 2016-11-30 -2.183717e-02 0.146281429 0.0123909402 -0.0021945347 -0.043041267
## 2016-12-30 4.684062e-02 0.073564444 0.0307215781 0.0186661315 0.120665178
## 2017-01-31 4.664158e-02 -0.013906877 0.0395980147 0.0399166536 0.164624916
## 2017-02-28 1.255553e-01 0.068386121 -0.0043733118 0.0774509121 -0.007730364
## 2017-03-31 4.754185e-02 -0.031157955 0.0289606838 -0.0222248484 0.107278727
## 2017-04-28 -6.974618e-05 -0.003879573 0.0387185908 -0.0057585537 0.120916212
## 2017-05-31 6.560702e-02 -0.057361574 0.0256724230 -0.0446566444 0.082295892
## 2017-06-30 -5.891592e-02 0.106699010 -0.0131151678 0.1108367409 0.058654468
## 2017-07-31 3.218079e-02 0.009852320 0.0532497371 0.0008469037 -0.111459860
## 2017-08-31 1.016529e-01 -0.009962552 0.0333893149 -0.1082531177 0.095543446
## 2017-09-29 -6.213489e-02 0.049580951 -0.0037519145 -0.0183456975 -0.042474144
## 2017-10-31 9.240380e-02 0.057849386 0.1103419800 0.0587964441 -0.028457409
## 2017-11-30 2.007521e-02 0.038126918 0.0168411100 0.0941688172 -0.070862541
## 2017-12-29 -1.536336e-02 0.022889250 0.0161455604 0.0379618441 0.008061928
## 2018-01-31 -1.069352e-02 0.083669116 0.1049981780 0.0867716061 0.129254571
## 2018-02-28 6.596094e-02 -0.001470657 -0.0084507813 -0.0175977176 -0.032266877
## 2018-03-29 -5.980357e-02 -0.049062932 -0.0270228068 -0.0058207229 -0.253920408
## 2018-04-30 -1.513394e-02 -0.005821929 0.0243532478 0.0289273502 0.099254576
## 2018-05-31 1.267421e-01 -0.016405205 0.0596519636 0.0486581023 -0.031698139
## 2018-06-29 -9.463225e-03 -0.026610394 -0.0023295767 0.1069231359 0.186043257
## 2018-07-31 2.759896e-02 0.103604835 0.0730207858 -0.0353827683 -0.140021491
## 2018-08-31 1.826736e-01 -0.003223966 0.0610882609 0.0689795020 0.011737389
## 2018-09-28 -8.337726e-03 -0.015302446 0.0179980881 0.0301964126 -0.130439038
## 2018-10-31 -3.095146e-02 -0.027461127 -0.0683874373 -0.1213305162 0.242170576
## 2018-11-30 -1.999128e-01 0.019709080 0.0417977833 0.0040296004 0.038271580
## 2018-12-31 -1.240887e-01 -0.130157755 -0.0877903744 -0.0131317871 -0.051761952
## 2019-01-31 5.368727e-02 0.066576942 0.0277686887 0.0992995753 -0.080628789
## 2019-02-28 4.380270e-02 0.008275074 0.0745113702 0.0459483146 0.041032981
## 2019-03-29 9.260248e-02 -0.030451537 0.0514094265 -0.0153199195 -0.133656413
## 2019-04-30 5.490125e-02 0.144249014 0.1019632337 0.0420893416 -0.159123803
## 2019-05-31 -1.326324e-01 -0.090959554 -0.0507468410 -0.1270014485 -0.253945372
## 2019-06-28 1.226769e-01 0.053649663 0.0798438310 0.0845993242 0.188012109
## 2019-07-31 7.361715e-02 0.043933704 0.0170963852 0.0244746131 0.078092373
## 2019-08-30 -1.659835e-02 -0.054383423 0.0149251823 -0.0153644133 -0.068516948
## 2019-09-30 7.042293e-02 0.068847232 0.0084507581 0.1056920642 0.065449565
## 2019-10-31 1.049764e-01 0.067598284 0.0307390208 -0.0476461863 0.268061253
## 2019-11-29 7.469368e-02 0.053308366 0.0577616592 0.0456631267 0.046592176
## 2019-12-31 9.420379e-02 0.056365535 0.0409013361 0.0803304128 0.237359743
## 2020-01-31 5.260223e-02 -0.045422719 0.0764560801 -0.0507165542 0.441578342
## 2020-02-28 -1.218309e-01 -0.130987429 -0.0467647537 -0.0718011265 0.026424253
## 2020-03-31 -7.231392e-02 -0.254395172 -0.0269000318 -0.0771937531 -0.242781458
## 2020-04-30 1.444239e-01 0.072040273 0.1278000957 0.0522720284 0.400209535
## 2020-05-29 8.166680e-02 0.016056248 0.0250743501 0.1253848850 0.065730500
## 2020-06-30 1.374865e-01 -0.033968653 0.1048638181 -0.0053911405 0.257108657
## 2020-07-31 1.528342e-01 0.036757080 0.0073435822 -0.0044971279 0.281420674
## 2020-08-31 1.960349e-01 0.036075786 0.0978088371 0.1387488104 0.554719320
## 2020-09-30 -1.081718e-01 -0.039911711 -0.0697754676 0.1151080585 -0.149762306
## 2020-10-30 -6.188802e-02 0.027455326 -0.0380861001 -0.0444661433 -0.100371771
## 2020-11-30 9.120469e-02 0.184291572 0.0583261568 0.1148920792 0.380308519
## 2020-12-31 1.084717e-01 0.075071083 0.0382645108 0.0510475423 0.217730753
## 2021-01-29 -5.516474e-03 0.019688641 0.0419973023 -0.0573123949 0.117343701
## 2021-02-26 -8.306863e-02 0.134337417 0.0041095520 0.0108995928 -0.161038203
## 2021-03-31 7.312613e-03 0.033803956 0.0144828515 -0.0141220542 -0.011269836
## 2021-04-30 7.345297e-02 0.016198194 0.0672862537 -0.0020339731 0.060292565
## 2021-05-28 -5.181668e-02 0.065610759 -0.0076565538 0.0305596017 -0.126372343
## 2021-06-30 9.450002e-02 -0.054426023 0.0815697881 0.1240976239 0.083547955
## 2021-07-30 6.295843e-02 -0.018724890 0.0504236656 0.0809136527 0.010973846
## 2021-08-31 4.161136e-02 0.052429272 0.0597685016 -0.0150256315 0.068224268
## 2021-09-30 -7.046167e-02 0.023113122 -0.0684060031 -0.1260499364 0.052632612
## 2021-10-29 5.700134e-02 0.043184555 0.1623661354 0.1414101694 0.362230202
## 2021-11-30 9.991934e-02 -0.067316514 -0.0012828541 0.0115891288 0.027237848
## 2021-12-31 7.160300e-02 -0.003026684 0.0171843399 -0.0135063034 -0.079968440
## 2022-01-31 -1.583707e-02 -0.057573476 -0.0783345858 -0.1183306406 -0.120597475
## 2022-02-28 -5.558245e-02 -0.046840509 -0.0379221423 -0.0809942857 -0.073397011
## 2022-03-31 5.588243e-02 -0.039412246 0.0313648959 -0.0124017431 0.213504290
## 2022-04-29 -1.021775e-01 -0.125476025 -0.1052128951 -0.0760991067 -0.213125289
## 2022-05-31 -5.603715e-02 0.102398409 -0.0182426077 -0.0480487606 -0.138340062
## 2022-06-30 -8.493698e-02 -0.160612226 -0.0569095631 -0.1484559805 -0.118657126
## 2022-07-29 1.728046e-01 0.032933900 0.0890143117 0.1173046645 0.280480149
## 2022-08-31 -3.170527e-02 -0.014230406 -0.0689889486 -0.0765610259 -0.075250271
## 2022-09-30 -1.289443e-01 -0.084640318 -0.1157102876 -0.2445217410 -0.038313992
## 2022-10-31 1.039557e-01 0.195049348 -0.0033114907 0.1088674839 -0.153346760
## 2022-11-30 -3.358515e-02 0.093227951 0.0973290178 0.1685054791 -0.155866121
## 2022-12-30 -1.304192e-01 -0.029971260 -0.0619237600 0.0676657853 -0.457813194
## 2023-01-31 1.048295e-01 0.050130385 0.0327737145 0.0845228135 0.340915767
## 2023-02-28 2.291828e-02 0.023932526 0.0089773456 -0.0694248240 0.171904973
## 2023-03-31 1.121214e-01 -0.095372943 0.1448634836 0.0347432649 0.008471140
## 2023-04-28 2.857494e-02 0.066892452 0.0636927152 0.0327266503 -0.233183710
## 2023-05-31 4.502923e-02 -0.018470840 0.0686912449 -0.1855463998 0.216021889
## 2023-06-30 9.014240e-02 0.069243124 0.0363307006 0.0506910085 0.249689452
## 2023-07-31 1.270443e-02 0.089422534 -0.0136595101 0.0001812493 0.021391609
## 2023-08-31 -4.330830e-02 -0.076477651 -0.0224764898 -0.0818939056 -0.035588260
## 2023-09-29 -9.285928e-02 -0.008992658 -0.0373308940 -0.0583951365 -0.030929027
## 2023-10-31 -2.573389e-03 -0.034614450 0.0684206791 0.0721114630 -0.219831983
## 2023-11-30 1.077599e-01 0.115463124 0.1159548882 0.0704384884 0.178463656
## 2023-12-29 1.349119e-02 0.086017829 -0.0076032241 -0.0121757897 0.034390133
## 2024-01-31 -4.314468e-02 0.030883170 0.0557006051 -0.0670408664 -0.282704160
## 2024-02-29 -1.871736e-02 0.064946998 0.0414474954 0.0233633911 0.075015304
## 2024-03-28 -5.264814e-02 0.073747084 0.0169714305 -0.0970692681 -0.138383423
## 2024-04-30 -6.728948e-03 -0.037859866 -0.0775402278 -0.0184713257 0.041724969
## 2024-05-31 1.224143e-01 0.055241399 0.0659666434 0.0297925148 -0.028782134
## 2024-06-28 9.125898e-02 -0.001827738 0.0738549800 -0.2280936492 0.105427913
## 2024-07-31 5.298219e-02 0.056324675 -0.0661283765 -0.0067893818 0.159378271
## 2024-08-30 3.184116e-02 0.054858322 -0.0010956475 0.1070686172 -0.080549177
## 2024-09-30 1.731639e-02 -0.064016755 0.0310605998 0.0636341593 0.200441406
## 2024-10-31 -3.090177e-02 0.057231914 -0.0572677513 -0.1363797092 -0.046070548
## 2024-11-29 5.041448e-02 0.118023364 0.0432438967 0.0210397191 0.323147326
## 2024-12-31 5.368788e-02 -0.040910361 -0.0046392831 -0.0350592955 0.157010663
## 2025-01-31 -5.930769e-02 0.114093001 -0.0153967911 0.0161241073 0.001880189
## 2025-02-28 2.554346e-02 -0.009963511 -0.0425088050 0.0323703069 -0.322794555
## 2025-03-31 -8.501352e-02 -0.075926073 -0.0559455554 -0.2191026418 -0.122658739
## 2025-04-30 -4.432081e-02 0.003364587 0.0515782678 -0.1182557516 0.085028653
## 2025-05-30 -5.507312e-02 0.076243137 0.1542991057 0.0716607065 0.205293048
## 2025-06-30 2.128049e-02 0.093621408 0.0774059356 0.1657368406 -0.086785707
## 2025-07-31 1.162981e-02 0.026405421 0.0700443986 0.0501031776 -0.030005653
## 2025-08-29 1.130062e-01 0.017334807 -0.0499171172 0.0352529047 0.079775694
## 2025-09-30 9.247092e-02 0.045432092 0.0219793711 -0.0987850620 0.286693192
## 2025-10-31 5.997984e-02 -0.008908117 -0.0002703949 -0.0765711460 0.026275247
## 2025-11-28 3.185157e-02 0.006280032 -0.0492394175 0.0006191134 -0.059539610calculate_component_contribution <- function(.data, w) {
# Covariance of asset returns
covariance_matrix <- cov(.data)
# Standard deviation of portfolio
# Summarizes how much each asset's returns vary with those of other assets within the portfolio into a single number
sd_portfolio <- sqrt(t(w) %*% covariance_matrix %*% w)
# Component contribution
# Similar to the formula for sd_portfolio
# Mathematical trick to summarize the same, sd_portfolio, by asset instead of a single number
component_contribution <- (t(w) %*% covariance_matrix * w) / sd_portfolio[1,1]
# Component contribution in percentage
component_percentages <- (component_contribution / sd_portfolio[1,1]) %>%
round(3) %>%
as_tibble()
return(component_percentages)
}
asset_returns_wide_tbl %>% calculate_component_contribution(w = c(.25, .25, .2, .2, .1))## # A tibble: 1 × 5
## AAPL JPM MSFT NKE TSLA
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.267 0.201 0.158 0.178 0.1965 Visualizing Component Contribution
Column chart of componenet contribution
plot_data <- asset_returns_wide_tbl %>%
calculate_component_contribution(w = c(.25, .25, .2, .2, .1)) %>%
# Transform
pivot_longer(cols = everything(), names_to = "Asset", values_to = "Contribution")
plot_data %>%
ggplot(aes(x = Asset, y = Contribution)) +
geom_col(fill = "cornflowerblue") +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
theme(plot.title = element_text(hjust = 0.5)) +
labs(title = "Percent Contribution to Portfolio Volatility")
6 Plot: Colum Chart of Component Contribution and Weight
plot_data <- asset_returns_wide_tbl %>%
calculate_component_contribution(w = c(.25, .25, .2, .2, .1)) %>%
# Transform
pivot_longer(cols = everything(), names_to = "Asset", values_to = "Contribution") %>%
# Add weights
add_column(weight = c(.25, .25, .2, .2, .1)) %>%
# Transform
pivot_longer(cols = c(Contribution, weight), names_to = "type", values_to = "value")
plot_data %>%
ggplot(aes(x = Asset, y = value, fill = type)) +
geom_col(position = "dodge") +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_fill_tq() +
theme(plot.title = element_text(hjust = 0.5)) +
theme_tq() +
labs(title = "Percent Contribution to Portfolio Volatility and Weight",
y = "Percent",
x = NULL)
Which of the assets in your portfolio the largest contributor to the portfolio volatility? Do you think your portfolio risk is concentrated in any one asset?
AAPL is the largest contributor to the portfolio volatility at around 27%. The second most is JPM at around 20%. These are also the largest weighted assets in the portfolio. The risk is not necessarily concentrated in any one asset. Although AAPL has the highest contribution to the volatility, it is not significant enough compared to the other assets to say the risk is concentrated in AAPL.