Apply it to your data 12
Which of the assets the largest contributor to the portfolio volatility?
Ryan Berg
2024-12-02
# 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("TSLA", "AMZN", "WMT", "RGR")
prices <- tq_get(x = symbols,
get = "stock.prices",
from = "2012-12-31",
to = "2024-11-20")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 RGR TSLA WMT
## 2013-01-31 0.0566799395 0.1117935991 0.102078114 0.0248965336
## 2013-02-28 -0.0046435024 0.0729114522 -0.074128640 0.0117958103
## 2013-03-28 0.0083654162 -0.0665751386 0.084208138 0.0620730284
## 2013-04-30 -0.0487507497 0.0105877744 0.354111531 0.0378933889
## 2013-05-31 0.0588686246 -0.0099508099 0.593716684 -0.0317796797
## 2013-06-28 0.0310507506 -0.0455738647 0.093672163 -0.0046875722
## 2013-07-31 0.0813355350 0.0572395845 0.223739522 0.0452740106
## 2013-08-30 -0.0695574090 0.0408137347 0.229971642 -0.0596995599
## 2013-09-30 0.1067688897 0.1789105490 0.134706620 0.0133389542
## 2013-10-31 0.1521839116 0.0434311659 -0.189806595 0.0370289815
## 2013-11-29 0.0781496860 0.1701084272 -0.228409405 0.0540193675
## 2013-12-31 0.0130490386 -0.0512045164 0.167108541 -0.0232522823
## 2014-01-31 -0.1059765119 0.0412758251 0.187261714 -0.0523037258
## 2014-02-28 0.0094619003 -0.1781551686 0.299722785 0.0002677976
## 2014-03-31 -0.0737086161 -0.0551841764 -0.160783242 0.0293257896
## 2014-04-30 -0.1007565303 0.0733307142 -0.002690122 0.0420200534
## 2014-05-30 0.0273091844 -0.0524000919 -0.000577422 -0.0314091748
## 2014-06-30 0.0383836202 -0.0269180972 0.144457224 -0.0223928721
## 2014-07-31 -0.0369768154 -0.1664840843 -0.072372676 -0.0200479846
## 2014-08-29 0.0799468404 0.0178876931 0.188794007 0.0323258326
## 2014-09-30 -0.0502010184 -0.0347160505 -0.105566477 0.0127657660
## 2014-10-31 -0.0540982347 -0.1554521925 -0.004046477 -0.0026185595
## 2014-11-28 0.1031187277 -0.0863213010 0.011599801 0.1378161286
## 2014-12-31 -0.0872368614 -0.0952312640 -0.094774519 -0.0135739178
## 2015-01-30 0.1330922557 0.1541096343 -0.088365289 -0.0105347712
## 2015-02-27 0.0697992426 0.2516438994 -0.001277808 -0.0124328162
## 2015-03-31 -0.0214295755 -0.0426221975 -0.074350051 -0.0142314252
## 2015-04-30 0.1253212736 0.0992775275 0.180226808 -0.0524138973
## 2015-05-29 0.0175090293 -0.0139702406 0.103899574 -0.0433510761
## 2015-06-30 0.0112589814 0.0667572086 0.067300935 -0.0460134624
## 2015-07-31 0.2111621090 0.0437629897 -0.007896616 0.0146946112
## 2015-08-31 -0.0443525782 0.0524474767 -0.066366250 -0.0993583525
## 2015-09-30 -0.0019516837 -0.0691179511 -0.002653519 0.0016978216
## 2015-10-30 0.2010808743 -0.0302712288 -0.182659777 -0.1246697320
## 2015-11-30 0.0602956777 -0.0837943479 0.106828586 0.0275690152
## 2015-12-31 0.0165440008 0.1346585020 0.041471519 0.0492989163
## 2016-01-29 -0.1410054620 -0.0128315236 -0.227360626 0.0793151295
## 2016-02-29 -0.0605352209 0.1779219739 0.003810669 -0.0003016803
## 2016-03-31 0.0717834363 -0.0230996181 0.179948109 0.0392704160
## 2016-04-29 0.1053453760 -0.0657286618 0.046721797 -0.0239371460
## 2016-05-31 0.0915002899 0.0409794035 -0.075597968 0.0641208402
## 2016-06-30 -0.0099694639 -0.0340944146 -0.050296440 0.0311569244
## 2016-07-29 0.0586021229 0.0604686464 0.100785334 -0.0006850857
## 2016-08-31 0.0135476418 -0.0965152682 -0.102058091 -0.0143678175
## 2016-09-30 0.0848953908 -0.0593202081 -0.038366372 0.0094732138
## 2016-10-31 -0.0583893058 0.0627405413 -0.031364583 -0.0295503095
## 2016-11-30 -0.0509721927 -0.1715812509 -0.043041267 0.0058382073
## 2016-12-30 -0.0009330556 0.0249770530 0.120665178 -0.0116434430
## 2017-01-31 0.0936394059 0.0009483082 0.164624916 -0.0350398141
## 2017-02-28 0.0258446800 -0.0565450751 -0.007730364 0.0608894235
## 2017-03-31 0.0479423007 0.0799576726 0.107278727 0.0234087490
## 2017-04-28 0.0424566944 0.1212008660 0.120916212 0.0421087086
## 2017-05-31 0.0725778018 0.0600098119 0.082295892 0.0511562600
## 2017-06-30 -0.0271286156 -0.0246336802 0.058654468 -0.0378575839
## 2017-07-31 0.0202278808 -0.0760281521 -0.111459860 0.0553873556
## 2017-08-31 -0.0072953953 -0.2246871844 0.095543446 -0.0180254134
## 2017-09-29 -0.0198260355 0.1211738002 -0.042474144 0.0008962784
## 2017-10-31 0.1395154056 -0.0424757960 -0.028457409 0.1109631648
## 2017-11-30 0.0626577318 0.1041035415 -0.070862541 0.1076144189
## 2017-12-29 -0.0062057845 0.0198920302 0.008061928 0.0207679702
## 2018-01-31 0.2156265497 -0.0533213456 0.129254571 0.0764922267
## 2018-02-28 0.0415536279 -0.2069860954 -0.032266877 -0.1691627848
## 2018-03-29 -0.0440034760 0.2030203710 -0.253920408 -0.0056771880
## 2018-04-30 0.0788803060 0.0510553117 0.099254576 -0.0057487508
## 2018-05-31 0.0397392430 0.1084520492 -0.031698139 -0.0629872811
## 2018-06-29 0.0421636787 -0.0896120459 0.186043257 0.0369862285
## 2018-07-31 0.0446635734 -0.0326708412 -0.140021491 0.0409480133
## 2018-08-31 0.1243079079 0.1944195575 0.011737389 0.0774629818
## 2018-09-28 -0.0048359814 0.0535442430 -0.130439038 -0.0205520186
## 2018-10-31 -0.2258869989 -0.1507046963 0.242170576 0.0656295882
## 2018-11-30 0.0560700324 -0.0986986669 0.038271580 -0.0265767952
## 2018-12-31 -0.1180514843 -0.0073012420 -0.051761952 -0.0417364136
## 2019-01-31 0.1348080312 0.0233992739 -0.080628789 0.0283648123
## 2019-02-28 -0.0469930640 0.0443399556 0.041032981 0.0324428277
## 2019-03-29 0.0824420184 -0.0659510016 -0.133656413 -0.0094925511
## 2019-04-30 0.0786806224 0.0545037125 -0.159123803 0.0530142824
## 2019-05-31 -0.0818753491 -0.1135098912 -0.253945372 -0.0084086083
## 2019-06-28 0.0646557767 0.0916273450 0.188012109 0.0854574477
## 2019-07-31 -0.0142806686 0.0364068016 0.078092373 -0.0009960790
## 2019-08-30 -0.0496880810 -0.3171597303 -0.068516948 0.0394581272
## 2019-09-30 -0.0229951159 0.0181231418 0.065449565 0.0379542720
## 2019-10-31 0.0232034080 0.0914714435 0.268061253 -0.0120372439
## 2019-11-29 0.0134958216 -0.0020946140 0.046592176 0.0154859448
## 2019-12-31 0.0257863501 0.0319753984 0.237359743 0.0023739856
## 2020-01-31 0.0834803026 0.0515909476 0.441578342 -0.0372904658
## 2020-02-28 -0.0642332026 -0.0303428333 0.026424253 -0.0613239258
## 2020-03-31 0.0344213022 0.0623450118 -0.242781458 0.0581107980
## 2020-04-30 0.2381504762 0.0439992465 0.400209535 0.0674664185
## 2020-05-29 -0.0128673719 0.1645788840 0.065730500 0.0248284930
## 2020-06-30 0.1218341331 0.1981298091 0.257108657 -0.0351086313
## 2020-07-31 0.1372488933 0.0682735308 0.281420674 0.0772518251
## 2020-08-31 0.0866005735 -0.0743259289 0.554719320 0.0745886051
## 2020-09-30 -0.0916533253 -0.1472128424 -0.149762306 0.0076052146
## 2020-10-30 -0.0364089187 0.0891076337 -0.100371771 -0.0083257286
## 2020-11-30 0.0425228214 -0.0794432600 0.380308519 0.0963907125
## 2020-12-31 0.0276719582 0.0606627856 0.217730753 -0.0545614279
## 2021-01-29 -0.0156985929 -0.0266308359 0.117343701 -0.0257180631
## 2021-02-26 -0.0359675607 0.0736117539 -0.161038203 -0.0782174555
## 2021-03-31 0.0003717151 -0.0217062161 -0.011269836 0.0486518235
## 2021-04-30 0.1139202399 -0.0172510816 0.060292565 0.0295951012
## 2021-05-28 -0.0730764715 0.2067930370 -0.126372343 0.0189583652
## 2021-06-30 0.0651836137 0.1308991104 0.083547955 -0.0071365619
## 2021-07-30 -0.0332696301 -0.1906690741 0.010973846 0.0107910550
## 2021-08-31 0.0421339363 0.0623582843 0.068224268 0.0418680322
## 2021-09-30 -0.0550034409 -0.0580542375 0.052632612 -0.0606835312
## 2021-10-29 0.0262547651 0.0668401024 0.362230202 0.0695570156
## 2021-11-30 0.0391473463 -0.0847208837 0.027237848 -0.0606288206
## 2021-12-31 -0.0505062029 -0.0525497281 -0.079968440 0.0324796268
## 2022-01-31 -0.1085098142 -0.0116820613 -0.120597475 -0.0343094247
## 2022-02-28 0.0263230079 0.0742250229 -0.073397011 -0.0338249542
## 2022-03-31 0.0596239190 -0.0268810128 0.213504290 0.1008100953
## 2022-04-29 -0.2711856801 -0.0213404989 -0.213125289 0.0269634706
## 2022-05-31 -0.0333130879 0.0065877259 -0.138340062 -0.1698040947
## 2022-06-30 -0.1238178226 -0.0644893746 -0.118657126 -0.0563675846
## 2022-07-29 0.2394860591 0.0382231386 0.280480149 0.0826081279
## 2022-08-31 -0.0625299224 -0.2275967845 -0.075250271 0.0081250972
## 2022-09-30 -0.1149865758 -0.0283403752 -0.038313992 -0.0217359565
## 2022-10-31 -0.0981105335 0.0999708519 -0.153346760 0.0929242741
## 2022-11-30 -0.0593198454 -0.0141226666 -0.155866121 0.0684915613
## 2022-12-30 -0.1391406409 0.0080976496 -0.457813194 -0.0685300354
## 2023-01-31 0.2051735029 0.1169487376 0.340915767 0.0145630125
## 2023-02-28 -0.0902516639 0.0237919766 0.171904973 -0.0121679518
## 2023-03-31 0.0918019370 -0.0068650989 0.008471140 0.0408372881
## 2023-04-28 0.0206963031 0.0020869804 -0.233183710 0.0235918517
## 2023-05-31 0.1340765703 -0.1041115347 0.216021889 -0.0237417069
## 2023-06-30 0.0779864104 0.0267906576 0.249689452 0.0678437903
## 2023-07-31 0.0251489708 -0.0001887841 0.021391609 0.0169069122
## 2023-08-31 0.0318772770 -0.0196254193 -0.035588260 0.0206048836
## 2023-09-29 -0.0821945627 0.0104146964 -0.030929027 -0.0166184904
## 2023-10-31 0.0458940197 0.0601279925 -0.219831983 0.0215261766
## 2023-11-30 0.0931972815 -0.2265672449 0.178463656 -0.0483956164
## 2023-12-29 0.0392628771 0.0333326007 0.034390133 0.0162177594
## 2024-01-31 0.0212288659 -0.0401804228 -0.282704160 0.0470820679
## 2024-02-29 0.1300782611 -0.0078179792 0.075015304 0.0620581657
## 2024-03-28 0.0202729146 0.0683526277 -0.138383423 0.0296685050
## 2024-04-30 -0.0302797899 0.0017319138 0.041724969 -0.0137217222
## 2024-05-31 0.0081949152 -0.0357689147 -0.028782134 0.1060150534
## 2024-06-28 0.0910037984 -0.0648386184 0.105427913 0.0292221364
## 2024-07-31 -0.0329830511 0.0798025852 0.159378271 0.0136415209
## 2024-08-30 -0.0464130352 -0.0639510689 -0.080549177 0.1207589765
## 2024-09-30 0.0429307038 -0.0105013175 0.200441406 0.0445699361
## 2024-10-31 0.0003755644 -0.0582881199 -0.046070548 0.0147513058
## 2024-11-19 0.0932108617 -0.0240913280 0.325578013 0.0551905300# Covariance of asset returns
covariance_matrix <- cov(asset_returns_wide_tbl)
covariance_matrix## AMZN RGR TSLA WMT
## AMZN 0.0071603669 0.0015549155 0.0052094637 0.0008307755
## RGR 0.0015549155 0.0092376627 0.0002915426 0.0003232826
## TSLA 0.0052094637 0.0002915426 0.0292501230 0.0009392985
## WMT 0.0008307755 0.0003232826 0.0009392985 0.0026338275# 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, 0.2, 0.2, 0.1)
sd_portfolio <- sqrt(t(w) %*% covariance_matrix %*% w)
sd_portfolio## [,1]
## [1,] 0.05296391# 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 RGR TSLA WMT
## [1,] 0.01522756 0.008786731 0.02758342 0.0013662rowSums(component_contribution)## [1] 0.05296391# Component contribution in percentage
component_percentages <- (component_contribution / sd_portfolio[1,1]) %>%
round(3) %>%
as_tibble()
component_percentages## # A tibble: 1 × 4
## AMZN RGR TSLA WMT
## <dbl> <dbl> <dbl> <dbl>
## 1 0.288 0.166 0.521 0.026component_percentages %>%
as_tibble() %>%
gather(key = "asset", value = "contribution")## # A tibble: 4 × 2
## asset contribution
## <chr> <dbl>
## 1 AMZN 0.288
## 2 RGR 0.166
## 3 TSLA 0.521
## 4 WMT 0.026# 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 RGR TSLA WMT
## 2013-01-31 0.0566799395 0.1117935991 0.102078114 0.0248965336
## 2013-02-28 -0.0046435024 0.0729114522 -0.074128640 0.0117958103
## 2013-03-28 0.0083654162 -0.0665751386 0.084208138 0.0620730284
## 2013-04-30 -0.0487507497 0.0105877744 0.354111531 0.0378933889
## 2013-05-31 0.0588686246 -0.0099508099 0.593716684 -0.0317796797
## 2013-06-28 0.0310507506 -0.0455738647 0.093672163 -0.0046875722
## 2013-07-31 0.0813355350 0.0572395845 0.223739522 0.0452740106
## 2013-08-30 -0.0695574090 0.0408137347 0.229971642 -0.0596995599
## 2013-09-30 0.1067688897 0.1789105490 0.134706620 0.0133389542
## 2013-10-31 0.1521839116 0.0434311659 -0.189806595 0.0370289815
## 2013-11-29 0.0781496860 0.1701084272 -0.228409405 0.0540193675
## 2013-12-31 0.0130490386 -0.0512045164 0.167108541 -0.0232522823
## 2014-01-31 -0.1059765119 0.0412758251 0.187261714 -0.0523037258
## 2014-02-28 0.0094619003 -0.1781551686 0.299722785 0.0002677976
## 2014-03-31 -0.0737086161 -0.0551841764 -0.160783242 0.0293257896
## 2014-04-30 -0.1007565303 0.0733307142 -0.002690122 0.0420200534
## 2014-05-30 0.0273091844 -0.0524000919 -0.000577422 -0.0314091748
## 2014-06-30 0.0383836202 -0.0269180972 0.144457224 -0.0223928721
## 2014-07-31 -0.0369768154 -0.1664840843 -0.072372676 -0.0200479846
## 2014-08-29 0.0799468404 0.0178876931 0.188794007 0.0323258326
## 2014-09-30 -0.0502010184 -0.0347160505 -0.105566477 0.0127657660
## 2014-10-31 -0.0540982347 -0.1554521925 -0.004046477 -0.0026185595
## 2014-11-28 0.1031187277 -0.0863213010 0.011599801 0.1378161286
## 2014-12-31 -0.0872368614 -0.0952312640 -0.094774519 -0.0135739178
## 2015-01-30 0.1330922557 0.1541096343 -0.088365289 -0.0105347712
## 2015-02-27 0.0697992426 0.2516438994 -0.001277808 -0.0124328162
## 2015-03-31 -0.0214295755 -0.0426221975 -0.074350051 -0.0142314252
## 2015-04-30 0.1253212736 0.0992775275 0.180226808 -0.0524138973
## 2015-05-29 0.0175090293 -0.0139702406 0.103899574 -0.0433510761
## 2015-06-30 0.0112589814 0.0667572086 0.067300935 -0.0460134624
## 2015-07-31 0.2111621090 0.0437629897 -0.007896616 0.0146946112
## 2015-08-31 -0.0443525782 0.0524474767 -0.066366250 -0.0993583525
## 2015-09-30 -0.0019516837 -0.0691179511 -0.002653519 0.0016978216
## 2015-10-30 0.2010808743 -0.0302712288 -0.182659777 -0.1246697320
## 2015-11-30 0.0602956777 -0.0837943479 0.106828586 0.0275690152
## 2015-12-31 0.0165440008 0.1346585020 0.041471519 0.0492989163
## 2016-01-29 -0.1410054620 -0.0128315236 -0.227360626 0.0793151295
## 2016-02-29 -0.0605352209 0.1779219739 0.003810669 -0.0003016803
## 2016-03-31 0.0717834363 -0.0230996181 0.179948109 0.0392704160
## 2016-04-29 0.1053453760 -0.0657286618 0.046721797 -0.0239371460
## 2016-05-31 0.0915002899 0.0409794035 -0.075597968 0.0641208402
## 2016-06-30 -0.0099694639 -0.0340944146 -0.050296440 0.0311569244
## 2016-07-29 0.0586021229 0.0604686464 0.100785334 -0.0006850857
## 2016-08-31 0.0135476418 -0.0965152682 -0.102058091 -0.0143678175
## 2016-09-30 0.0848953908 -0.0593202081 -0.038366372 0.0094732138
## 2016-10-31 -0.0583893058 0.0627405413 -0.031364583 -0.0295503095
## 2016-11-30 -0.0509721927 -0.1715812509 -0.043041267 0.0058382073
## 2016-12-30 -0.0009330556 0.0249770530 0.120665178 -0.0116434430
## 2017-01-31 0.0936394059 0.0009483082 0.164624916 -0.0350398141
## 2017-02-28 0.0258446800 -0.0565450751 -0.007730364 0.0608894235
## 2017-03-31 0.0479423007 0.0799576726 0.107278727 0.0234087490
## 2017-04-28 0.0424566944 0.1212008660 0.120916212 0.0421087086
## 2017-05-31 0.0725778018 0.0600098119 0.082295892 0.0511562600
## 2017-06-30 -0.0271286156 -0.0246336802 0.058654468 -0.0378575839
## 2017-07-31 0.0202278808 -0.0760281521 -0.111459860 0.0553873556
## 2017-08-31 -0.0072953953 -0.2246871844 0.095543446 -0.0180254134
## 2017-09-29 -0.0198260355 0.1211738002 -0.042474144 0.0008962784
## 2017-10-31 0.1395154056 -0.0424757960 -0.028457409 0.1109631648
## 2017-11-30 0.0626577318 0.1041035415 -0.070862541 0.1076144189
## 2017-12-29 -0.0062057845 0.0198920302 0.008061928 0.0207679702
## 2018-01-31 0.2156265497 -0.0533213456 0.129254571 0.0764922267
## 2018-02-28 0.0415536279 -0.2069860954 -0.032266877 -0.1691627848
## 2018-03-29 -0.0440034760 0.2030203710 -0.253920408 -0.0056771880
## 2018-04-30 0.0788803060 0.0510553117 0.099254576 -0.0057487508
## 2018-05-31 0.0397392430 0.1084520492 -0.031698139 -0.0629872811
## 2018-06-29 0.0421636787 -0.0896120459 0.186043257 0.0369862285
## 2018-07-31 0.0446635734 -0.0326708412 -0.140021491 0.0409480133
## 2018-08-31 0.1243079079 0.1944195575 0.011737389 0.0774629818
## 2018-09-28 -0.0048359814 0.0535442430 -0.130439038 -0.0205520186
## 2018-10-31 -0.2258869989 -0.1507046963 0.242170576 0.0656295882
## 2018-11-30 0.0560700324 -0.0986986669 0.038271580 -0.0265767952
## 2018-12-31 -0.1180514843 -0.0073012420 -0.051761952 -0.0417364136
## 2019-01-31 0.1348080312 0.0233992739 -0.080628789 0.0283648123
## 2019-02-28 -0.0469930640 0.0443399556 0.041032981 0.0324428277
## 2019-03-29 0.0824420184 -0.0659510016 -0.133656413 -0.0094925511
## 2019-04-30 0.0786806224 0.0545037125 -0.159123803 0.0530142824
## 2019-05-31 -0.0818753491 -0.1135098912 -0.253945372 -0.0084086083
## 2019-06-28 0.0646557767 0.0916273450 0.188012109 0.0854574477
## 2019-07-31 -0.0142806686 0.0364068016 0.078092373 -0.0009960790
## 2019-08-30 -0.0496880810 -0.3171597303 -0.068516948 0.0394581272
## 2019-09-30 -0.0229951159 0.0181231418 0.065449565 0.0379542720
## 2019-10-31 0.0232034080 0.0914714435 0.268061253 -0.0120372439
## 2019-11-29 0.0134958216 -0.0020946140 0.046592176 0.0154859448
## 2019-12-31 0.0257863501 0.0319753984 0.237359743 0.0023739856
## 2020-01-31 0.0834803026 0.0515909476 0.441578342 -0.0372904658
## 2020-02-28 -0.0642332026 -0.0303428333 0.026424253 -0.0613239258
## 2020-03-31 0.0344213022 0.0623450118 -0.242781458 0.0581107980
## 2020-04-30 0.2381504762 0.0439992465 0.400209535 0.0674664185
## 2020-05-29 -0.0128673719 0.1645788840 0.065730500 0.0248284930
## 2020-06-30 0.1218341331 0.1981298091 0.257108657 -0.0351086313
## 2020-07-31 0.1372488933 0.0682735308 0.281420674 0.0772518251
## 2020-08-31 0.0866005735 -0.0743259289 0.554719320 0.0745886051
## 2020-09-30 -0.0916533253 -0.1472128424 -0.149762306 0.0076052146
## 2020-10-30 -0.0364089187 0.0891076337 -0.100371771 -0.0083257286
## 2020-11-30 0.0425228214 -0.0794432600 0.380308519 0.0963907125
## 2020-12-31 0.0276719582 0.0606627856 0.217730753 -0.0545614279
## 2021-01-29 -0.0156985929 -0.0266308359 0.117343701 -0.0257180631
## 2021-02-26 -0.0359675607 0.0736117539 -0.161038203 -0.0782174555
## 2021-03-31 0.0003717151 -0.0217062161 -0.011269836 0.0486518235
## 2021-04-30 0.1139202399 -0.0172510816 0.060292565 0.0295951012
## 2021-05-28 -0.0730764715 0.2067930370 -0.126372343 0.0189583652
## 2021-06-30 0.0651836137 0.1308991104 0.083547955 -0.0071365619
## 2021-07-30 -0.0332696301 -0.1906690741 0.010973846 0.0107910550
## 2021-08-31 0.0421339363 0.0623582843 0.068224268 0.0418680322
## 2021-09-30 -0.0550034409 -0.0580542375 0.052632612 -0.0606835312
## 2021-10-29 0.0262547651 0.0668401024 0.362230202 0.0695570156
## 2021-11-30 0.0391473463 -0.0847208837 0.027237848 -0.0606288206
## 2021-12-31 -0.0505062029 -0.0525497281 -0.079968440 0.0324796268
## 2022-01-31 -0.1085098142 -0.0116820613 -0.120597475 -0.0343094247
## 2022-02-28 0.0263230079 0.0742250229 -0.073397011 -0.0338249542
## 2022-03-31 0.0596239190 -0.0268810128 0.213504290 0.1008100953
## 2022-04-29 -0.2711856801 -0.0213404989 -0.213125289 0.0269634706
## 2022-05-31 -0.0333130879 0.0065877259 -0.138340062 -0.1698040947
## 2022-06-30 -0.1238178226 -0.0644893746 -0.118657126 -0.0563675846
## 2022-07-29 0.2394860591 0.0382231386 0.280480149 0.0826081279
## 2022-08-31 -0.0625299224 -0.2275967845 -0.075250271 0.0081250972
## 2022-09-30 -0.1149865758 -0.0283403752 -0.038313992 -0.0217359565
## 2022-10-31 -0.0981105335 0.0999708519 -0.153346760 0.0929242741
## 2022-11-30 -0.0593198454 -0.0141226666 -0.155866121 0.0684915613
## 2022-12-30 -0.1391406409 0.0080976496 -0.457813194 -0.0685300354
## 2023-01-31 0.2051735029 0.1169487376 0.340915767 0.0145630125
## 2023-02-28 -0.0902516639 0.0237919766 0.171904973 -0.0121679518
## 2023-03-31 0.0918019370 -0.0068650989 0.008471140 0.0408372881
## 2023-04-28 0.0206963031 0.0020869804 -0.233183710 0.0235918517
## 2023-05-31 0.1340765703 -0.1041115347 0.216021889 -0.0237417069
## 2023-06-30 0.0779864104 0.0267906576 0.249689452 0.0678437903
## 2023-07-31 0.0251489708 -0.0001887841 0.021391609 0.0169069122
## 2023-08-31 0.0318772770 -0.0196254193 -0.035588260 0.0206048836
## 2023-09-29 -0.0821945627 0.0104146964 -0.030929027 -0.0166184904
## 2023-10-31 0.0458940197 0.0601279925 -0.219831983 0.0215261766
## 2023-11-30 0.0931972815 -0.2265672449 0.178463656 -0.0483956164
## 2023-12-29 0.0392628771 0.0333326007 0.034390133 0.0162177594
## 2024-01-31 0.0212288659 -0.0401804228 -0.282704160 0.0470820679
## 2024-02-29 0.1300782611 -0.0078179792 0.075015304 0.0620581657
## 2024-03-28 0.0202729146 0.0683526277 -0.138383423 0.0296685050
## 2024-04-30 -0.0302797899 0.0017319138 0.041724969 -0.0137217222
## 2024-05-31 0.0081949152 -0.0357689147 -0.028782134 0.1060150534
## 2024-06-28 0.0910037984 -0.0648386184 0.105427913 0.0292221364
## 2024-07-31 -0.0329830511 0.0798025852 0.159378271 0.0136415209
## 2024-08-30 -0.0464130352 -0.0639510689 -0.080549177 0.1207589765
## 2024-09-30 0.0429307038 -0.0105013175 0.200441406 0.0445699361
## 2024-10-31 0.0003755644 -0.0582881199 -0.046070548 0.0147513058
## 2024-11-19 0.0932108617 -0.0240913280 0.325578013 0.0551905300calculate_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, .3))## # A tibble: 1 × 4
## AMZN RGR TSLA WMT
## <dbl> <dbl> <dbl> <dbl>
## 1 0.251 0.206 0.434 0.116 Plot: Colum Chart of Component Contribution and Weight
plot_data <- asset_returns_wide_tbl %>%
calculate_component_contribution(w = c(.25, .25, .2, .3)) %>%
# Transform to long form
pivot_longer(cols = everything() ,names_to = "Asset", values_to = "Contribution") %>%
# Add weights
add_column(weight = c(.25, .25, .2, .3)) %>%
# 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)) +
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?