Apply it to your data 12
Which of the assets the largest contributor to the portfolio volatility?
Owen Doherty
2025-12-03
# 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", "TGT", "WMT", "COST")
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 COST TGT WMT
## 2013-01-31 0.0566799395 0.0359115455 0.0207399364 0.0248960688
## 2013-02-28 -0.0046435024 -0.0076473037 0.0470674482 0.0117956325
## 2013-03-28 0.0083654162 0.0464886570 0.0836038042 0.0620733444
## 2013-04-30 -0.0487507497 0.0216286367 0.0303600624 0.0378940266
## 2013-05-31 0.0588686246 0.0137929552 -0.0099613248 -0.0317803265
## 2013-06-28 0.0310507506 0.0085376620 -0.0092510482 -0.0046876901
## 2013-07-31 0.0813355350 0.0601082787 0.0341191112 0.0452746039
## 2013-08-30 -0.0695574090 -0.0458220116 -0.1118618013 -0.0596996679
## 2013-09-30 0.1067688897 0.0290719119 0.0105272481 0.0133387646
## 2013-10-31 0.1521839116 0.0242752566 0.0125808934 0.0370288724
## 2013-11-29 0.0781496860 0.0635978940 -0.0069133143 0.0540191050
## 2013-12-31 0.0130490386 -0.0524564677 -0.0103774224 -0.0232521533
## 2014-01-31 -0.1059765119 -0.0575831618 -0.1106957294 -0.0523035066
## 2014-02-28 0.0094619003 0.0414543698 0.1066818482 0.0002677993
## 2014-03-31 -0.0737086161 -0.0448254145 -0.0329977351 0.0293258718
## 2014-04-30 -0.1007565303 0.0351900442 0.0202855679 0.0420197047
## 2014-05-30 0.0273091844 0.0059923438 -0.0769025779 -0.0314091608
## 2014-06-30 0.0383836202 -0.0074403423 0.0207487873 -0.0223929194
## 2014-07-31 -0.0369768154 0.0234833295 0.0279072224 -0.0200477199
## 2014-08-29 0.0799468404 0.0296726395 0.0169976887 0.0323258126
## 2014-09-30 -0.0502010184 0.0344192863 0.0425320320 0.0127659151
## 2014-10-31 -0.0540982347 0.0622564977 -0.0138156469 -0.0026188708
## 2014-11-28 0.1031187277 0.0661385347 0.1875000034 0.1378161469
## 2014-12-31 -0.0872368614 -0.0026070460 0.0254836939 -0.0135737635
## 2015-01-30 0.1330922557 0.0087093095 -0.0307679023 -0.0105350301
## 2015-02-27 0.0697992426 0.0623775080 0.0496017752 -0.0124327229
## 2015-03-31 -0.0214295755 0.0304251494 0.0659774041 -0.0142316094
## 2015-04-30 0.1253212736 -0.0546596517 -0.0402784443 -0.0524135881
## 2015-05-29 0.0175090293 -0.0032206005 0.0128400110 -0.0433509251
## 2015-06-30 0.0112589814 -0.0542541314 0.0287062888 -0.0460137267
## 2015-07-31 0.2111621090 0.0730809853 0.0026918910 0.0146945932
## 2015-08-31 -0.0443525782 -0.0340549818 -0.0448221836 -0.0993581514
## 2015-09-30 -0.0019516837 0.0317638341 0.0121506387 0.0016980595
## 2015-10-30 0.2010808743 0.0895905747 -0.0189941673 -0.1246701622
## 2015-11-30 0.0602956777 0.0232363066 -0.0547335497 0.0275691466
## 2015-12-31 0.0165440008 0.0004957496 0.0015160112 0.0492988515
## 2016-01-29 -0.1410054620 -0.0664310133 -0.0026202421 0.0793153459
## 2016-02-29 -0.0605352209 -0.0045311042 0.0882419533 -0.0003022468
## 2016-03-31 0.0717834363 0.0490975754 0.0476672486 0.0392706584
## 2016-04-29 0.1053453760 -0.0588895486 -0.0343712346 -0.0239369987
## 2016-05-31 0.0915002899 0.0043113358 -0.1372355329 0.0641208939
## 2016-06-30 -0.0099694639 0.0540991743 0.0150075932 0.0311569152
## 2016-07-29 0.0586021229 0.0628101159 0.0759577817 -0.0006851060
## 2016-08-31 0.0135476418 -0.0284746953 -0.0627263681 -0.0143680293
## 2016-09-30 0.0848953908 -0.0609213373 -0.0217478055 0.0094736549
## 2016-10-31 -0.0583893058 -0.0308968415 0.0007275474 -0.0295510180
## 2016-11-30 -0.0509721927 0.0181077948 0.1251768485 0.0058387945
## 2016-12-30 -0.0009330556 0.0644927397 -0.0670620652 -0.0116436795
## 2017-01-31 0.0936394059 0.0237004206 -0.1135003841 -0.0350396593
## 2017-02-28 0.0258446800 0.0802945460 -0.0835534644 0.0608890541
## 2017-03-31 0.0479423007 -0.0550494211 -0.0628498255 0.0234092364
## 2017-04-28 0.0424566944 0.0569665704 0.0118878192 0.0421085426
## 2017-05-31 0.0725778018 0.0587793863 -0.0018018239 0.0511563613
## 2017-06-30 -0.0271286156 -0.1206065415 -0.0532517111 -0.0378580525
## 2017-07-31 0.0202278808 -0.0089185400 0.0804398049 0.0553875153
## 2017-08-31 -0.0072953953 -0.0080370469 -0.0272904794 -0.0180253420
## 2017-09-29 -0.0198260355 0.0470449248 0.0789562670 0.0008964969
## 2017-10-31 0.1395154056 -0.0197320993 0.0005081430 0.1109625570
## 2017-11-30 0.0626577318 0.1383318710 0.0247793608 0.1076144588
## 2017-12-29 -0.0062057845 0.0091216854 0.0855493775 0.0207687832
## 2018-01-31 0.2156265497 0.0459410500 0.1421908823 0.0764918460
## 2018-02-28 0.0415536279 -0.0179106349 0.0107468907 -0.1691628171
## 2018-03-29 -0.0440034760 -0.0130231753 -0.0826207532 -0.0056772980
## 2018-04-30 0.0788803060 0.0452887192 0.0446455444 -0.0057486603
## 2018-05-31 0.0397392430 0.0083749348 0.0125279558 -0.0629871978
## 2018-06-29 0.0421636787 0.0527599579 0.0433595380 0.0369860533
## 2018-07-31 0.0446635734 0.0455083478 0.0581797004 0.0409482660
## 2018-08-31 0.1243079079 0.0663288828 0.0889778214 0.0774627491
## 2018-09-28 -0.0048359814 0.0074783991 0.0080816082 -0.0205521426
## 2018-10-31 -0.2258869989 -0.0269696276 -0.0533181146 0.0656295250
## 2018-11-30 0.0560700324 0.0138981299 -0.1560245173 -0.0265766044
## 2018-12-31 -0.1180514843 -0.1269316926 -0.0710990427 -0.0417362002
## 2019-01-31 0.1348080312 0.0522184633 0.0994422772 0.0283645312
## 2019-02-28 -0.0469930640 0.0216653282 0.0038811671 0.0324430238
## 2019-03-29 0.0824420184 0.1016324877 0.0997562579 -0.0094925735
## 2019-04-30 0.0786806224 0.0139029976 -0.0360267023 0.0530141959
## 2019-05-31 -0.0818753491 -0.0218349835 0.0473591422 -0.0084085165
## 2019-06-28 0.0646557767 0.0980462129 0.0737798082 0.0854576124
## 2019-07-31 -0.0142806686 0.0421259737 -0.0024274821 -0.0009959080
## 2019-08-30 -0.0496880810 0.0693116283 0.2218667630 0.0394578764
## 2019-09-30 -0.0229951159 -0.0228190139 -0.0012150581 0.0379542985
## 2019-10-31 0.0232034080 0.0329300011 0.0000000000 -0.0120373614
## 2019-11-29 0.0134958216 0.0090466311 0.1623187098 0.0154860863
## 2019-12-31 0.0257863501 -0.0198409431 0.0252760511 0.0023737847
## 2020-01-31 0.0834803026 0.0387074868 -0.1464845004 -0.0372905590
## 2020-02-28 -0.0642332026 -0.0810564966 -0.0667812142 -0.0613238083
## 2020-03-31 0.0344213022 0.0140924186 -0.1024522135 0.0581107721
## 2020-04-30 0.2381504762 0.0630696532 0.1658369271 0.0674661309
## 2020-05-29 -0.0128673719 0.0178918471 0.1138942603 0.0248289962
## 2020-06-30 0.1218341331 -0.0171987925 -0.0198143006 -0.0351087930
## 2020-07-31 0.1372488933 0.0731774453 0.0484208411 0.0772517545
## 2020-08-31 0.0866005735 0.0657702041 0.1882717886 0.0745885666
## 2020-09-30 -0.0916533253 0.0208929681 0.0402477613 0.0076051670
## 2020-10-30 -0.0364089187 0.0092728923 -0.0335905815 -0.0083258121
## 2020-11-30 0.0425228214 0.0912039764 0.1691406143 0.0963909247
## 2020-12-31 0.0276719582 -0.0131568442 -0.0168515998 -0.0545614232
## 2021-01-29 -0.0156985929 -0.0668094432 0.0259451175 -0.0257180142
## 2021-02-26 -0.0359675607 -0.0607610556 0.0160104157 -0.0782173849
## 2021-03-31 0.0003717151 0.0628753512 0.0767326655 0.0486516889
## 2021-04-30 0.1139202399 0.0562818573 0.0453536035 0.0295951915
## 2021-05-28 -0.0730764715 0.0164722979 0.0938666027 0.0189584382
## 2021-06-30 0.0651836137 0.0449722780 0.0632653481 -0.0071365761
## 2021-07-30 -0.0332696301 0.0844261411 0.0768491283 0.0107909963
## 2021-08-31 0.0421339363 0.0582399761 -0.0519787320 0.0418681281
## 2021-09-30 -0.0550034409 -0.0135715972 -0.0765901409 -0.0606838558
## 2021-10-29 0.0262547651 0.0913576735 0.1265018056 0.0695571266
## 2021-11-30 0.0391473463 0.0928767555 -0.0592959705 -0.0606285988
## 2021-12-31 -0.0505062029 0.0511731912 -0.0521917790 0.0324793738
## 2022-01-31 -0.1085098142 -0.1167773919 -0.0487403594 -0.0343092140
## 2022-02-28 0.0263230079 0.0290841783 -0.0940890154 -0.0338250472
## 2022-03-31 0.0596239190 0.1034613947 0.0604567720 0.1008101796
## 2022-04-29 -0.2711856801 -0.0781042403 0.0745687402 0.0269634584
## 2022-05-31 -0.0333130879 -0.1314593743 -0.3412237240 -0.1698042629
## 2022-06-30 -0.1238178226 0.0276272354 -0.1364653969 -0.0563673802
## 2022-07-29 0.2394860591 0.1234133719 0.1456888725 0.0826081252
## 2022-08-31 -0.0625299224 -0.0361147288 -0.0125340489 0.0081248754
## 2022-09-30 -0.1149865758 -0.1003082240 -0.0774525594 -0.0217355909
## 2022-10-31 -0.0981105335 0.0618564100 0.1015456597 0.0929240417
## 2022-11-30 -0.0593198454 0.0725755241 0.0232764154 0.0684913808
## 2022-12-30 -0.1391406409 -0.1665904895 -0.1141984128 -0.0685298958
## 2023-01-31 0.2051735029 0.1130548988 0.1440934273 0.0145631782
## 2023-02-28 -0.0902516639 -0.0524475224 -0.0151215138 -0.0121681299
## 2023-03-31 0.0918019370 0.0258717094 -0.0171794936 0.0408373072
## 2023-04-28 0.0206963031 0.0126988808 -0.0487449128 0.0235919382
## 2023-05-31 0.1340765703 0.0185211646 -0.1795999116 -0.0237417342
## 2023-06-30 0.0779864104 0.0510997353 0.0073812179 0.0678437215
## 2023-07-31 0.0251489708 0.0405679432 0.0340610333 0.0169068634
## 2023-08-31 0.0318772770 -0.0186364300 -0.0669317261 0.0206048812
## 2023-09-29 -0.0821945627 0.0281461458 -0.1349887152 -0.0166184345
## 2023-10-31 0.0458940197 -0.0224099173 0.0019878064 0.0215261952
## 2023-11-30 0.0931972815 0.0722445947 0.1990888539 -0.0483955600
## 2023-12-29 0.0392628771 0.1300909619 0.0623594661 0.0162177780
## 2024-01-31 0.0212288659 0.0513781204 -0.0237308705 0.0470820524
## 2024-02-29 0.1300782611 0.0696231672 0.1022451490 0.0620581643
## 2024-03-28 0.0202729146 -0.0152525578 0.1474205398 0.0296684711
## 2024-04-30 -0.0302797899 -0.0117661878 -0.0960554544 -0.0137217649
## 2024-05-31 0.0081949152 0.1136275244 -0.0235390138 0.1060150893
## 2024-06-28 0.0910037984 0.0483262482 -0.0533984625 0.0292221416
## 2024-07-31 -0.0329830511 -0.0320615334 0.0158824466 0.0136416023
## 2024-08-30 -0.0464130352 0.0821515904 0.0289072164 0.1207588725
## 2024-09-30 0.0429307038 -0.0065883578 0.0144761041 0.0445699521
## 2024-10-31 0.0003755644 -0.0140173715 -0.0380560697 0.0147512873
## 2024-11-29 0.1091142212 0.1072738018 -0.1185490029 0.1210993655
## 2024-12-31 0.0538418744 -0.0589210901 0.0214594528 -0.0213044751
## 2025-01-31 0.0800742352 0.0671190163 0.0199942359 0.0829078191
## 2025-02-28 -0.1130190481 0.0688948725 -0.0958942393 0.0045737771
## 2025-03-31 -0.1095146233 -0.1032107172 -0.1743686757 -0.1134826443
## 2025-04-30 -0.0311757737 0.0502300978 -0.0762331071 0.1023372927
## 2025-05-30 0.1058429765 0.0462111097 -0.0168449127 0.0174175384
## 2025-06-30 0.0677922335 -0.0495048309 0.0481769960 -0.0095675353
## 2025-07-31 0.0649401278 -0.0521474867 0.0185794438 0.0020433725
## 2025-08-29 -0.0220690894 0.0052931426 -0.0352314150 -0.0079257195
## 2025-09-30 -0.0420508819 -0.0189309483 -0.0676691760 0.0608065129
## 2025-10-31 0.1063983418 -0.0140240978 0.0331135151 -0.0184102073
## 2025-11-28 -0.0460872441 0.0023451712 -0.0103829918 0.0882049221
## 2025-12-02 0.0051321512 0.0091958702 -0.0016566520 0.0170469001# Covariance of asset returns
covariance_matrix <- cov(asset_returns_wide_tbl)
covariance_matrix## AMZN COST TGT WMT
## AMZN 0.0070344314 0.002144688 0.001652569 0.0008347007
## COST 0.0021446885 0.003113376 0.001942291 0.0015325770
## TGT 0.0016525687 0.001942291 0.006769723 0.0017756988
## WMT 0.0008347007 0.001532577 0.001775699 0.0027380000# 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(0.3, 0.25, 0.25, 0.2)
sd_portfolio <- sqrt(t(w) %*% covariance_matrix %*% w)
sd_portfolio## [,1]
## [1,] 0.05102626# 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## AMZN COST TGT WMT
## [1,] 0.01897014 0.01084657 0.01483998 0.00636958rowSums(component_contribution)## [1] 0.05102626# Component contribution in percentage
component_percentages <- (component_contribution / sd_portfolio[1,1]) %>%
round(3) %>%
as_tibble()
component_percentages## # A tibble: 1 × 4
## AMZN COST TGT WMT
## <dbl> <dbl> <dbl> <dbl>
## 1 0.372 0.213 0.291 0.125component_percentages %>%
as_tibble() %>%
gather(key = "asset", value = "contribution")## # A tibble: 4 × 2
## asset contribution
## <chr> <dbl>
## 1 AMZN 0.372
## 2 COST 0.213
## 3 TGT 0.291
## 4 WMT 0.1256 Plot: Colum Chart of Component Contribution and Weight
# 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 COST TGT WMT
## 2013-01-31 0.0566799395 0.0359115455 0.0207399364 0.0248960688
## 2013-02-28 -0.0046435024 -0.0076473037 0.0470674482 0.0117956325
## 2013-03-28 0.0083654162 0.0464886570 0.0836038042 0.0620733444
## 2013-04-30 -0.0487507497 0.0216286367 0.0303600624 0.0378940266
## 2013-05-31 0.0588686246 0.0137929552 -0.0099613248 -0.0317803265
## 2013-06-28 0.0310507506 0.0085376620 -0.0092510482 -0.0046876901
## 2013-07-31 0.0813355350 0.0601082787 0.0341191112 0.0452746039
## 2013-08-30 -0.0695574090 -0.0458220116 -0.1118618013 -0.0596996679
## 2013-09-30 0.1067688897 0.0290719119 0.0105272481 0.0133387646
## 2013-10-31 0.1521839116 0.0242752566 0.0125808934 0.0370288724
## 2013-11-29 0.0781496860 0.0635978940 -0.0069133143 0.0540191050
## 2013-12-31 0.0130490386 -0.0524564677 -0.0103774224 -0.0232521533
## 2014-01-31 -0.1059765119 -0.0575831618 -0.1106957294 -0.0523035066
## 2014-02-28 0.0094619003 0.0414543698 0.1066818482 0.0002677993
## 2014-03-31 -0.0737086161 -0.0448254145 -0.0329977351 0.0293258718
## 2014-04-30 -0.1007565303 0.0351900442 0.0202855679 0.0420197047
## 2014-05-30 0.0273091844 0.0059923438 -0.0769025779 -0.0314091608
## 2014-06-30 0.0383836202 -0.0074403423 0.0207487873 -0.0223929194
## 2014-07-31 -0.0369768154 0.0234833295 0.0279072224 -0.0200477199
## 2014-08-29 0.0799468404 0.0296726395 0.0169976887 0.0323258126
## 2014-09-30 -0.0502010184 0.0344192863 0.0425320320 0.0127659151
## 2014-10-31 -0.0540982347 0.0622564977 -0.0138156469 -0.0026188708
## 2014-11-28 0.1031187277 0.0661385347 0.1875000034 0.1378161469
## 2014-12-31 -0.0872368614 -0.0026070460 0.0254836939 -0.0135737635
## 2015-01-30 0.1330922557 0.0087093095 -0.0307679023 -0.0105350301
## 2015-02-27 0.0697992426 0.0623775080 0.0496017752 -0.0124327229
## 2015-03-31 -0.0214295755 0.0304251494 0.0659774041 -0.0142316094
## 2015-04-30 0.1253212736 -0.0546596517 -0.0402784443 -0.0524135881
## 2015-05-29 0.0175090293 -0.0032206005 0.0128400110 -0.0433509251
## 2015-06-30 0.0112589814 -0.0542541314 0.0287062888 -0.0460137267
## 2015-07-31 0.2111621090 0.0730809853 0.0026918910 0.0146945932
## 2015-08-31 -0.0443525782 -0.0340549818 -0.0448221836 -0.0993581514
## 2015-09-30 -0.0019516837 0.0317638341 0.0121506387 0.0016980595
## 2015-10-30 0.2010808743 0.0895905747 -0.0189941673 -0.1246701622
## 2015-11-30 0.0602956777 0.0232363066 -0.0547335497 0.0275691466
## 2015-12-31 0.0165440008 0.0004957496 0.0015160112 0.0492988515
## 2016-01-29 -0.1410054620 -0.0664310133 -0.0026202421 0.0793153459
## 2016-02-29 -0.0605352209 -0.0045311042 0.0882419533 -0.0003022468
## 2016-03-31 0.0717834363 0.0490975754 0.0476672486 0.0392706584
## 2016-04-29 0.1053453760 -0.0588895486 -0.0343712346 -0.0239369987
## 2016-05-31 0.0915002899 0.0043113358 -0.1372355329 0.0641208939
## 2016-06-30 -0.0099694639 0.0540991743 0.0150075932 0.0311569152
## 2016-07-29 0.0586021229 0.0628101159 0.0759577817 -0.0006851060
## 2016-08-31 0.0135476418 -0.0284746953 -0.0627263681 -0.0143680293
## 2016-09-30 0.0848953908 -0.0609213373 -0.0217478055 0.0094736549
## 2016-10-31 -0.0583893058 -0.0308968415 0.0007275474 -0.0295510180
## 2016-11-30 -0.0509721927 0.0181077948 0.1251768485 0.0058387945
## 2016-12-30 -0.0009330556 0.0644927397 -0.0670620652 -0.0116436795
## 2017-01-31 0.0936394059 0.0237004206 -0.1135003841 -0.0350396593
## 2017-02-28 0.0258446800 0.0802945460 -0.0835534644 0.0608890541
## 2017-03-31 0.0479423007 -0.0550494211 -0.0628498255 0.0234092364
## 2017-04-28 0.0424566944 0.0569665704 0.0118878192 0.0421085426
## 2017-05-31 0.0725778018 0.0587793863 -0.0018018239 0.0511563613
## 2017-06-30 -0.0271286156 -0.1206065415 -0.0532517111 -0.0378580525
## 2017-07-31 0.0202278808 -0.0089185400 0.0804398049 0.0553875153
## 2017-08-31 -0.0072953953 -0.0080370469 -0.0272904794 -0.0180253420
## 2017-09-29 -0.0198260355 0.0470449248 0.0789562670 0.0008964969
## 2017-10-31 0.1395154056 -0.0197320993 0.0005081430 0.1109625570
## 2017-11-30 0.0626577318 0.1383318710 0.0247793608 0.1076144588
## 2017-12-29 -0.0062057845 0.0091216854 0.0855493775 0.0207687832
## 2018-01-31 0.2156265497 0.0459410500 0.1421908823 0.0764918460
## 2018-02-28 0.0415536279 -0.0179106349 0.0107468907 -0.1691628171
## 2018-03-29 -0.0440034760 -0.0130231753 -0.0826207532 -0.0056772980
## 2018-04-30 0.0788803060 0.0452887192 0.0446455444 -0.0057486603
## 2018-05-31 0.0397392430 0.0083749348 0.0125279558 -0.0629871978
## 2018-06-29 0.0421636787 0.0527599579 0.0433595380 0.0369860533
## 2018-07-31 0.0446635734 0.0455083478 0.0581797004 0.0409482660
## 2018-08-31 0.1243079079 0.0663288828 0.0889778214 0.0774627491
## 2018-09-28 -0.0048359814 0.0074783991 0.0080816082 -0.0205521426
## 2018-10-31 -0.2258869989 -0.0269696276 -0.0533181146 0.0656295250
## 2018-11-30 0.0560700324 0.0138981299 -0.1560245173 -0.0265766044
## 2018-12-31 -0.1180514843 -0.1269316926 -0.0710990427 -0.0417362002
## 2019-01-31 0.1348080312 0.0522184633 0.0994422772 0.0283645312
## 2019-02-28 -0.0469930640 0.0216653282 0.0038811671 0.0324430238
## 2019-03-29 0.0824420184 0.1016324877 0.0997562579 -0.0094925735
## 2019-04-30 0.0786806224 0.0139029976 -0.0360267023 0.0530141959
## 2019-05-31 -0.0818753491 -0.0218349835 0.0473591422 -0.0084085165
## 2019-06-28 0.0646557767 0.0980462129 0.0737798082 0.0854576124
## 2019-07-31 -0.0142806686 0.0421259737 -0.0024274821 -0.0009959080
## 2019-08-30 -0.0496880810 0.0693116283 0.2218667630 0.0394578764
## 2019-09-30 -0.0229951159 -0.0228190139 -0.0012150581 0.0379542985
## 2019-10-31 0.0232034080 0.0329300011 0.0000000000 -0.0120373614
## 2019-11-29 0.0134958216 0.0090466311 0.1623187098 0.0154860863
## 2019-12-31 0.0257863501 -0.0198409431 0.0252760511 0.0023737847
## 2020-01-31 0.0834803026 0.0387074868 -0.1464845004 -0.0372905590
## 2020-02-28 -0.0642332026 -0.0810564966 -0.0667812142 -0.0613238083
## 2020-03-31 0.0344213022 0.0140924186 -0.1024522135 0.0581107721
## 2020-04-30 0.2381504762 0.0630696532 0.1658369271 0.0674661309
## 2020-05-29 -0.0128673719 0.0178918471 0.1138942603 0.0248289962
## 2020-06-30 0.1218341331 -0.0171987925 -0.0198143006 -0.0351087930
## 2020-07-31 0.1372488933 0.0731774453 0.0484208411 0.0772517545
## 2020-08-31 0.0866005735 0.0657702041 0.1882717886 0.0745885666
## 2020-09-30 -0.0916533253 0.0208929681 0.0402477613 0.0076051670
## 2020-10-30 -0.0364089187 0.0092728923 -0.0335905815 -0.0083258121
## 2020-11-30 0.0425228214 0.0912039764 0.1691406143 0.0963909247
## 2020-12-31 0.0276719582 -0.0131568442 -0.0168515998 -0.0545614232
## 2021-01-29 -0.0156985929 -0.0668094432 0.0259451175 -0.0257180142
## 2021-02-26 -0.0359675607 -0.0607610556 0.0160104157 -0.0782173849
## 2021-03-31 0.0003717151 0.0628753512 0.0767326655 0.0486516889
## 2021-04-30 0.1139202399 0.0562818573 0.0453536035 0.0295951915
## 2021-05-28 -0.0730764715 0.0164722979 0.0938666027 0.0189584382
## 2021-06-30 0.0651836137 0.0449722780 0.0632653481 -0.0071365761
## 2021-07-30 -0.0332696301 0.0844261411 0.0768491283 0.0107909963
## 2021-08-31 0.0421339363 0.0582399761 -0.0519787320 0.0418681281
## 2021-09-30 -0.0550034409 -0.0135715972 -0.0765901409 -0.0606838558
## 2021-10-29 0.0262547651 0.0913576735 0.1265018056 0.0695571266
## 2021-11-30 0.0391473463 0.0928767555 -0.0592959705 -0.0606285988
## 2021-12-31 -0.0505062029 0.0511731912 -0.0521917790 0.0324793738
## 2022-01-31 -0.1085098142 -0.1167773919 -0.0487403594 -0.0343092140
## 2022-02-28 0.0263230079 0.0290841783 -0.0940890154 -0.0338250472
## 2022-03-31 0.0596239190 0.1034613947 0.0604567720 0.1008101796
## 2022-04-29 -0.2711856801 -0.0781042403 0.0745687402 0.0269634584
## 2022-05-31 -0.0333130879 -0.1314593743 -0.3412237240 -0.1698042629
## 2022-06-30 -0.1238178226 0.0276272354 -0.1364653969 -0.0563673802
## 2022-07-29 0.2394860591 0.1234133719 0.1456888725 0.0826081252
## 2022-08-31 -0.0625299224 -0.0361147288 -0.0125340489 0.0081248754
## 2022-09-30 -0.1149865758 -0.1003082240 -0.0774525594 -0.0217355909
## 2022-10-31 -0.0981105335 0.0618564100 0.1015456597 0.0929240417
## 2022-11-30 -0.0593198454 0.0725755241 0.0232764154 0.0684913808
## 2022-12-30 -0.1391406409 -0.1665904895 -0.1141984128 -0.0685298958
## 2023-01-31 0.2051735029 0.1130548988 0.1440934273 0.0145631782
## 2023-02-28 -0.0902516639 -0.0524475224 -0.0151215138 -0.0121681299
## 2023-03-31 0.0918019370 0.0258717094 -0.0171794936 0.0408373072
## 2023-04-28 0.0206963031 0.0126988808 -0.0487449128 0.0235919382
## 2023-05-31 0.1340765703 0.0185211646 -0.1795999116 -0.0237417342
## 2023-06-30 0.0779864104 0.0510997353 0.0073812179 0.0678437215
## 2023-07-31 0.0251489708 0.0405679432 0.0340610333 0.0169068634
## 2023-08-31 0.0318772770 -0.0186364300 -0.0669317261 0.0206048812
## 2023-09-29 -0.0821945627 0.0281461458 -0.1349887152 -0.0166184345
## 2023-10-31 0.0458940197 -0.0224099173 0.0019878064 0.0215261952
## 2023-11-30 0.0931972815 0.0722445947 0.1990888539 -0.0483955600
## 2023-12-29 0.0392628771 0.1300909619 0.0623594661 0.0162177780
## 2024-01-31 0.0212288659 0.0513781204 -0.0237308705 0.0470820524
## 2024-02-29 0.1300782611 0.0696231672 0.1022451490 0.0620581643
## 2024-03-28 0.0202729146 -0.0152525578 0.1474205398 0.0296684711
## 2024-04-30 -0.0302797899 -0.0117661878 -0.0960554544 -0.0137217649
## 2024-05-31 0.0081949152 0.1136275244 -0.0235390138 0.1060150893
## 2024-06-28 0.0910037984 0.0483262482 -0.0533984625 0.0292221416
## 2024-07-31 -0.0329830511 -0.0320615334 0.0158824466 0.0136416023
## 2024-08-30 -0.0464130352 0.0821515904 0.0289072164 0.1207588725
## 2024-09-30 0.0429307038 -0.0065883578 0.0144761041 0.0445699521
## 2024-10-31 0.0003755644 -0.0140173715 -0.0380560697 0.0147512873
## 2024-11-29 0.1091142212 0.1072738018 -0.1185490029 0.1210993655
## 2024-12-31 0.0538418744 -0.0589210901 0.0214594528 -0.0213044751
## 2025-01-31 0.0800742352 0.0671190163 0.0199942359 0.0829078191
## 2025-02-28 -0.1130190481 0.0688948725 -0.0958942393 0.0045737771
## 2025-03-31 -0.1095146233 -0.1032107172 -0.1743686757 -0.1134826443
## 2025-04-30 -0.0311757737 0.0502300978 -0.0762331071 0.1023372927
## 2025-05-30 0.1058429765 0.0462111097 -0.0168449127 0.0174175384
## 2025-06-30 0.0677922335 -0.0495048309 0.0481769960 -0.0095675353
## 2025-07-31 0.0649401278 -0.0521474867 0.0185794438 0.0020433725
## 2025-08-29 -0.0220690894 0.0052931426 -0.0352314150 -0.0079257195
## 2025-09-30 -0.0420508819 -0.0189309483 -0.0676691760 0.0608065129
## 2025-10-31 0.1063983418 -0.0140240978 0.0331135151 -0.0184102073
## 2025-11-28 -0.0460872441 0.0023451712 -0.0103829918 0.0882049221
## 2025-12-02 0.0051321512 0.0091958702 -0.0016566520 0.0170469001calculate_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(.3, .25, .25, .2)))## # A tibble: 1 × 4
## AMZN COST TGT WMT
## <dbl> <dbl> <dbl> <dbl>
## 1 0.372 0.213 0.291 0.125plot_data <- asset_returns_wide_tbl %>%
calculate_component_contribution(w = c(.3, .25, .25, .2)) %>%
# Transform to long form
pivot_longer(cols = everything(), names_to = "Asset", values_to = "Contribution") %>%
# Add weight
add_column(weight = c(.3, .25, .25, .2)) %>%
# 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 = 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?
The largest contributor to my portfolio’s volatility is AMZN. Indeed, its the asset with the highest black bar (contribution bar) at ~37%. I think my portfolio’s risk is concentrated in that asset because its contribution is notably higher than its weight. TGT also contribute to a part of the portfolio’s risk with ~29% of contribution, which is much less than AMZN. Thus AMZN is the main contributor.