Apply it to your data 12
Which of the assets the largest contributor to the portfolio volatility?
Alex St. Pierre
2025-11-15
# Load packages
# Core
library(tidyverse)
library(tidyquant)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("AMZN", "META", "NFLX", "NVDA", "GOOGL")
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## AMZN GOOGL META NFLX NVDA
## 2013-01-31 0.0566799395 0.0660633448 0.1516789379 0.5792178352 0.000000000
## 2013-02-28 -0.0046435024 0.0584792943 -0.1282882816 0.1294686037 0.038221817
## 2013-03-28 0.0083654162 -0.0087879305 -0.0632427631 0.0063600533 0.013338804
## 2013-04-30 -0.0487507497 0.0375394049 0.0821454475 0.1323749474 0.070706183
## 2013-05-31 0.0588686246 0.0550324522 -0.1314243044 0.0460379624 0.054651855
## 2013-06-28 0.0310507506 0.0104477803 0.0215322519 -0.0693558290 -0.030167153
## 2013-07-31 0.0813355350 0.0083478368 0.3914336219 0.1468652518 0.028091940
## 2013-08-30 -0.0695574090 -0.0471075668 0.1151224983 0.1495075647 0.026270274
## 2013-09-30 0.1067688897 0.0336808725 0.1959921624 0.0853631921 0.053460310
## 2013-10-31 0.1521839116 0.1626135629 -0.0003983427 0.0420205612 -0.024066214
## 2013-11-29 0.0781496860 0.0277603415 -0.0658538575 0.1260456715 0.032035186
## 2013-12-31 0.0130490386 0.0560802341 0.1505889313 0.0064580000 0.026566929
## 2014-01-31 -0.1059765119 0.0523738047 0.1353367250 0.1059768648 -0.020177412
## 2014-02-28 0.0094619003 0.0289428459 0.0899635907 0.0849672590 0.162107368
## 2014-03-31 -0.0737086161 -0.0868642330 -0.1279129234 -0.2357724841 -0.025903795
## 2014-04-30 -0.1007565303 -0.0419808836 -0.0076654640 -0.0890406585 0.030788693
## 2014-05-30 0.0273091844 0.0664844319 0.0572141571 0.2603989261 0.032886267
## 2014-06-30 0.0383836202 0.0225206339 0.0611264206 0.0530626641 -0.024508545
## 2014-07-31 -0.0369768154 -0.0087954896 0.0766415921 -0.0414272975 -0.057729487
## 2014-08-29 0.0799468404 0.0048367480 0.0294319871 0.1221472546 0.110059905
## 2014-09-30 -0.0502010184 0.0103352524 0.0548688321 -0.0569909815 -0.052782698
## 2014-10-31 -0.0540982347 -0.0355314959 -0.0525993634 -0.1386420583 0.057399152
## 2014-11-28 0.1031187277 -0.0336483998 0.0355004072 -0.1250818519 0.074851930
## 2014-12-31 -0.0872368614 -0.0341226606 0.0041100398 -0.0144727545 -0.044863122
## 2015-01-30 0.1330922557 0.0129003351 -0.0274166300 0.2571876083 -0.043318765
## 2015-02-27 0.0697992426 0.0456004612 0.0395194123 0.0722680343 0.142698847
## 2015-03-31 -0.0214295755 -0.0141947047 0.0403306408 -0.1307827594 -0.052583080
## 2015-04-30 0.1253212736 -0.0107481153 -0.0428664815 0.2893246603 0.058909473
## 2015-05-29 0.0175090293 -0.0063066189 0.0053180135 0.1145792814 0.001459733
## 2015-06-30 0.0112589814 -0.0097295720 0.0798191166 0.0513462657 -0.095717262
## 2015-07-31 0.2111621090 0.1968016190 0.0917318669 0.1972314899 -0.007987946
## 2015-08-31 -0.0443525782 -0.0148319296 -0.0499449037 0.0062789518 0.123595645
## 2015-09-30 -0.0019516837 -0.0146948751 0.0052417109 -0.1079428863 0.092150607
## 2015-10-30 0.2010808743 0.1441989022 0.1259806348 0.0483934975 0.140555762
## 2015-11-30 0.0602956777 0.0339445487 0.0220173207 0.1292201068 0.115404955
## 2015-12-31 0.0165440008 0.0196778523 0.0040211452 -0.0753374900 0.038347587
## 2016-01-29 -0.1410054620 -0.0216460856 0.0696550820 -0.2194782802 -0.118048541
## 2016-02-29 -0.0605352209 -0.0597106846 -0.0482912490 0.0169504292 0.071923713
## 2016-03-31 0.0717834363 0.0617443718 0.0649943039 0.0902267716 0.127654487
## 2016-04-29 0.1053453760 -0.0748524466 0.0300437713 -0.1270821580 -0.002810410
## 2016-05-31 0.0915002899 0.0562641532 0.0104065101 0.1304025124 0.276388394
## 2016-06-30 -0.0099694639 -0.0624280289 -0.0388739210 -0.1144251460 0.006188222
## 2016-07-29 0.0586021229 0.1176170373 0.0811459483 -0.0025173508 0.194443369
## 2016-08-31 0.0135476418 -0.0018845645 0.0174363639 0.0657364217 0.073469152
## 2016-09-30 0.0848953908 0.0178305988 0.0169035701 0.0112245729 0.110693708
## 2016-10-31 -0.0583893058 0.0072370128 0.0209835966 0.2367092462 0.037805172
## 2016-11-30 -0.0509721927 -0.0429128622 -0.1008833876 -0.0650993149 0.260525064
## 2016-12-30 -0.0009330556 0.0211316779 -0.0288708571 0.0564934510 0.146436063
## 2017-01-31 0.0936394059 0.0344063074 0.1246262357 0.1280336724 0.022601840
## 2017-02-28 0.0258446800 0.0297178667 0.0392739568 0.0100411108 -0.071874841
## 2017-03-31 0.0479423007 0.0033910671 0.0469122605 0.0391855218 0.070843649
## 2017-04-28 0.0424566944 0.0866298992 0.0561212929 0.0292677885 -0.043433862
## 2017-05-31 0.0725778018 0.0654865504 0.0080211221 0.0689840448 0.326022070
## 2017-06-30 -0.0271286156 -0.0599207831 -0.0031742321 -0.0874853040 0.001453601
## 2017-07-31 0.0202278808 0.0168734838 0.1142295583 0.1954426244 0.117045114
## 2017-08-31 -0.0072953953 0.0102487575 0.0159430636 -0.0390093333 0.042639071
## 2017-09-29 -0.0198260355 0.0191612572 -0.0064171073 0.0373013834 0.053601279
## 2017-10-31 0.1395154056 0.0591373028 0.0523871227 0.0798772375 0.145700417
## 2017-11-30 0.0626577318 0.0030251988 -0.0161239423 -0.0461007257 -0.029244996
## 2017-12-29 -0.0062057845 0.0164917251 -0.0040719595 0.0230815823 -0.036583442
## 2018-01-31 0.2156265497 0.1153711595 0.0574260336 0.3422455062 0.239240829
## 2018-02-28 0.0415536279 -0.0685265569 -0.0469404886 0.0750957953 -0.014959357
## 2018-03-29 -0.0440034760 -0.0624006180 -0.1097192688 0.0135328146 -0.043969049
## 2018-04-30 0.0788803060 -0.0180572539 0.0736341303 0.0563153817 -0.029312493
## 2018-05-31 0.0397392430 0.0769005897 0.1088543381 0.1180176912 0.115145144
## 2018-06-29 0.0421636787 0.0261904200 0.0131574389 0.1073125782 -0.062544875
## 2018-07-31 0.0446635734 0.0832507900 -0.1186454451 -0.1483894202 0.033048479
## 2018-08-31 0.1243079079 0.0037250797 0.0180879545 0.0857957175 0.137075449
## 2018-09-28 -0.0048359814 -0.0202722585 -0.0662814853 0.0173902455 0.001210556
## 2018-10-31 -0.2258869989 -0.1014946112 -0.0801693259 -0.2149050241 -0.287373613
## 2018-11-30 0.0560700324 0.0173350444 -0.0765079559 -0.0532519750 -0.253667193
## 2018-12-31 -0.1180514843 -0.0600659860 -0.0701060960 -0.0667287371 -0.202283544
## 2019-01-31 0.1348080312 0.0745950421 0.2402517194 0.2377564232 0.073974041
## 2019-02-28 -0.0469930640 0.0005860656 -0.0319402489 0.0533383459 0.071594104
## 2019-03-29 0.0824420184 0.0437155813 0.0319402489 -0.0043098364 0.151870400
## 2019-04-30 0.0786806224 0.0185791219 0.1486248303 0.0384589452 0.007987331
## 2019-05-31 -0.0818753491 -0.0802526734 -0.0859589174 -0.0764150093 -0.288680283
## 2019-06-28 0.0646557767 -0.0216516170 0.0838885857 0.0676869455 0.192591699
## 2019-07-31 -0.0142806686 0.1178241282 0.0063528038 -0.1286120040 0.026972528
## 2019-08-30 -0.0496880810 -0.0229757770 -0.0450721182 -0.0948922909 -0.006207763
## 2019-09-30 -0.0229951159 0.0253862532 -0.0417379665 -0.0931610341 0.038414567
## 2019-10-31 0.0232034080 0.0303740414 0.0734377660 0.0713417093 0.143946589
## 2019-11-29 0.0134958216 0.0353467210 0.0508131951 0.0905829375 0.076031675
## 2019-12-31 0.0257863501 0.0267087557 0.0177448633 0.0279227475 0.082162645
## 2020-01-31 0.0834803026 0.0674022889 -0.0164067621 0.0643897941 0.004790938
## 2020-02-28 -0.0642332026 -0.0675069091 -0.0478817313 0.0670727184 0.133626880
## 2020-03-31 0.0344213022 -0.1420101228 -0.1431448395 0.0173805126 -0.024248341
## 2020-04-30 0.2381504762 0.1475576504 0.2047989739 0.1116390683 0.103279663
## 2020-05-29 -0.0128673719 0.0624755718 0.0949058790 -0.0002858819 0.194461708
## 2020-06-30 0.1218341331 -0.0108501926 0.0087580695 0.0807737255 0.068216599
## 2020-07-31 0.1372488933 0.0481166742 0.1107757624 0.0717317079 0.111189472
## 2020-08-31 0.0866005735 0.0908922991 0.1448210153 0.0799294267 0.231105548
## 2020-09-30 -0.0916533253 -0.1060268510 -0.1128923908 -0.0573784336 0.011895498
## 2020-10-30 -0.0364089187 0.0977571410 0.0046091699 -0.0497965496 -0.076501378
## 2020-11-30 0.0425228214 0.0821050467 0.0513371446 0.0309614228 0.066921810
## 2020-12-31 0.0276719582 -0.0010035791 -0.0138514776 0.0970871760 -0.025899906
## 2021-01-29 -0.0156985929 0.0417490133 -0.0558197489 -0.0155438392 -0.005011020
## 2021-02-26 -0.0359675607 0.1011702012 -0.0027521189 0.0120609342 0.054293336
## 2021-03-31 0.0003717151 0.0198860270 0.1338951686 -0.0324211662 -0.026723167
## 2021-04-30 0.1139202399 0.1319750315 0.0986905392 -0.0158244342 0.117297882
## 2021-05-28 -0.0730764715 0.0014223884 0.0111654943 -0.0209792775 0.079071028
## 2021-06-30 0.0651836137 0.0354053480 0.0561318624 0.0492815858 0.208332102
## 2021-07-30 -0.0332696301 0.0984923550 0.0244043152 -0.0203491346 -0.025494121
## 2021-08-31 0.0421339363 0.0713986318 0.0627652905 0.0950694939 0.138204162
## 2021-09-30 -0.0550034409 -0.0792264698 -0.1113883927 0.0698019104 -0.077484599
## 2021-10-29 0.0262547651 0.1021042415 -0.0477344467 0.1231245610 0.210395907
## 2021-11-30 0.0391473463 -0.0424181785 0.0027467088 -0.0727081801 0.245338307
## 2021-12-31 -0.0505062029 0.0206076928 0.0359899873 -0.0634444996 -0.105149342
## 2022-01-31 -0.1085098142 -0.0681922265 -0.0711186351 -0.3438762220 -0.183267304
## 2022-02-28 0.0263230079 -0.0018233817 -0.3950332245 -0.0794420172 -0.004133520
## 2022-03-31 0.0596239190 0.0292625372 0.0522973497 -0.0518377428 0.112576062
## 2022-04-29 -0.2711856801 -0.1978011431 -0.1036330441 -0.6769151054 -0.386065553
## 2022-05-31 -0.0333130879 -0.0030499316 -0.0346638903 0.0365177351 0.006716913
## 2022-06-30 -0.1238178226 -0.0431002531 -0.1830448709 -0.1213919016 -0.208219196
## 2022-07-29 0.2394860591 0.0653368173 -0.0134230113 0.2516130360 0.180791974
## 2022-08-31 -0.0625299224 -0.0721789007 0.0237875377 -0.0059759741 -0.185089174
## 2022-09-30 -0.1149865758 -0.1234704679 -0.1830210618 0.0517762558 -0.217576883
## 2022-10-31 -0.0981105335 -0.0119900012 -0.3759808898 0.2148866190 0.106044073
## 2022-11-30 -0.0593198454 0.0663157995 0.2372133274 0.0457052075 0.226462019
## 2022-12-30 -0.1391406409 -0.1350744189 0.0187892554 -0.0354794336 -0.146693652
## 2023-01-31 0.2051735029 0.1135551797 0.2134238500 0.1823328761 0.290330264
## 2023-02-28 -0.0902516639 -0.0930260848 0.1606982013 -0.0939461699 0.172531420
## 2023-03-31 0.0918019370 0.1413151736 0.1918602047 0.0699795345 0.179536531
## 2023-04-28 0.0206963031 0.0342100141 0.1256680688 -0.0460542525 -0.001008489
## 2023-05-31 0.1340765703 0.1351255225 0.0967012960 0.1805873321 0.310008200
## 2023-06-30 0.0779864104 -0.0261383286 0.0807399047 0.1084198950 0.111729812
## 2023-07-31 0.0251489708 0.1032530520 0.1045237781 -0.0034566798 0.099530394
## 2023-08-31 0.0318772770 0.0256624090 -0.0739485477 -0.0121241234 0.054674363
## 2023-09-29 -0.0821945627 -0.0397760079 0.0144945221 -0.1384715458 -0.126218838
## 2023-10-31 0.0458940197 -0.0532014762 0.0035245134 0.0864352180 -0.064546307
## 2023-11-30 0.0931972815 0.0658825274 0.0824120058 0.1408734072 0.137050335
## 2023-12-29 0.0392628771 0.0526165850 0.0787649944 0.0268736655 0.057262957
## 2024-01-31 0.0212288659 0.0029308039 0.0973218619 0.1472315750 0.217059180
## 2024-02-29 0.1300782611 -0.0117748867 0.2292254078 0.0665352594 0.251388450
## 2024-03-28 0.0202729146 0.0862345701 -0.0093266641 0.0072878139 0.132939733
## 2024-04-30 -0.0302797899 0.0755835392 -0.1211634354 -0.0979911057 -0.044746487
## 2024-05-31 0.0081949152 0.0579975030 0.0817845141 0.1529150450 0.238127472
## 2024-06-28 0.0910037984 0.0555803249 0.0780399543 0.0505386138 0.119508657
## 2024-07-31 -0.0329830511 -0.0600140899 -0.0600559751 -0.0714375604 -0.054220171
## 2024-08-30 -0.0464130352 -0.0487374989 0.0933880676 0.1099097581 0.019883146
## 2024-09-30 0.0429307038 0.0163309589 0.0945167054 0.0112292200 0.017277928
## 2024-10-31 0.0003755644 0.0312228754 -0.0085263633 0.0638447163 0.089122631
## 2024-11-29 0.1091142212 -0.0127038811 0.0118051295 0.1595497355 0.040520710
## 2024-12-31 0.0538418744 0.1148757734 0.0201028320 0.0050728010 -0.028993106
## 2025-01-31 0.0800742352 0.0748849616 0.1630193142 0.0915374451 -0.111926736
## 2025-02-28 -0.1130190481 -0.1807739532 -0.0309150196 0.0038828370 0.039598579
## 2025-03-31 -0.1095146233 -0.0951932419 -0.1469657547 -0.0502225296 -0.141937962
## 2025-04-30 -0.0311757737 0.0265457341 -0.0486340804 0.1935925475 0.004970099
## 2025-05-30 0.1058429765 0.0783361928 0.1650049154 0.0645899020 0.215623673
## 2025-06-30 0.0677922335 0.0270180736 0.1317316461 0.1036916319 0.156363856
## 2025-07-31 0.0649401278 0.0851844061 0.0467822661 -0.1441174565 0.118521186
## 2025-08-29 -0.0220690894 0.1038951429 -0.0459561912 0.0412703555 -0.020963769
## 2025-09-30 -0.0420508819 0.1334973732 -0.0051905924 -0.0077518692 0.068827324
## 2025-10-31 0.1063983418 0.1455576997 -0.1245959761 -0.0691108133 0.081830418
## 2025-11-14 -0.0398039586 -0.0171453222 -0.0618572811 -0.0059972363 -0.062772130calculate_component_contribution <- function(.data, w) {
covariance_matrix <- cov(.data)
sd_portfolio <- sqrt(t(w) %*% covariance_matrix %*% w)
component_contribution <- (t(w) %*% covariance_matrix * w) / sd_portfolio[1,1]
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
## AMZN GOOGL META NFLX NVDA
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.244 0.175 0.189 0.275 0.1166 Plot: Colum Chart of Component Contribution and Weight
Column Chart of Component Contribution and weight
plot_data <- asset_returns_wide_tbl %>%
calculate_component_contribution(w = c(.25, .25, .2, .2, .1)) %>%
# Transform to long form
pivot_longer(cols = everything(),names_to = "Asset", values_to = "Contribution") %>%
# Add weights
add_column(weight = c(.25, .25, .2, .2, .1)) %>%
# Transform to long
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 = .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?
Answer: NFLX is the largest contributor to my portfolio’s volatility at a value of 27.5%. With that being said, I don’t believe my portfolio’s risk is concentrated in any one specific asset as all other assets have contributions ranging from 11.6% - 24.4%. Given that there are only 5 assets in this portfolio, these ranges are acceptable and fairly balanced in my honest opinion. Also, volatility isn’t necessarily a bad thing either as it presents potential for larger gains within a specific asset if handled correctly.