Apply it to your data 12
Which of the assets the largest contributor to the portfolio volatility?
Nahomy Palma
2024-06-27
# 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("COST", "TSLA", "NFLX", "GOOG")
prices <- tq_get(x = symbols,
get = "stock.prices",
from = "2012-12-31",
to = "2024-06-27")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
Step-by-Step
# 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## COST GOOG NFLX TSLA
## 2013-01-31 0.0359115681 0.0660632170 0.5792178072 0.102078114
## 2013-02-28 -0.0076472531 0.0584794011 0.1294683790 -0.074128640
## 2013-03-28 0.0464885488 -0.0087878463 0.0063600708 0.084208138
## 2013-04-30 0.0216284517 0.0375393257 0.1323750202 0.354111531
## 2013-05-31 0.0137932762 0.0550324761 0.0460381788 0.593716684
## 2013-06-28 0.0085374770 0.0104477267 -0.0693560587 0.093672163
## 2013-07-31 0.0601084428 0.0083478658 0.1468488828 0.223739522
## 2013-08-30 -0.0458220385 -0.0471074271 0.1495238153 0.229971642
## 2013-09-30 0.0290715036 0.0336805999 0.0853633452 0.134706620
## 2013-10-31 0.0242754411 0.1626137315 0.0420204523 -0.189806595
## 2013-11-29 0.0635980302 0.0277603361 0.1260456325 -0.228409405
## 2013-12-31 -0.0524563676 0.0560803485 0.0064580908 0.167108541
## 2014-01-31 -0.0575831769 0.0523737221 0.1059769064 0.187261714
## 2014-02-28 0.0414542673 0.0289427457 0.0849673396 0.299722785
## 2014-03-31 -0.0448253593 -0.0863761472 -0.2357726219 -0.160783242
## 2014-04-30 0.0351903031 -0.0559561794 -0.0890406532 -0.002690122
## 2014-05-30 0.0059918074 0.0611852260 0.2603988640 -0.000577422
## 2014-06-30 -0.0074401327 0.0271164958 0.0530627854 0.144457224
## 2014-07-31 0.0234832627 -0.0064174656 -0.0414273713 -0.072372676
## 2014-08-29 0.0296729444 0.0000000000 0.1221472352 0.188794007
## 2014-09-30 0.0344188093 0.0100265853 -0.0569909207 -0.105566477
## 2014-10-31 0.0622562635 -0.0321734129 -0.1386421470 -0.004046477
## 2014-11-28 0.0661384138 -0.0313402973 -0.1250817171 0.011599801
## 2014-12-31 -0.0026066361 -0.0288909045 -0.0144727330 -0.094774519
## 2015-01-30 0.0087100868 0.0153077799 0.2571875124 -0.088365289
## 2015-02-27 0.0623765262 0.0437063825 0.0722680160 -0.001277808
## 2015-03-31 0.0304255797 -0.0188002460 -0.1307827695 -0.074350051
## 2015-04-30 -0.0546593581 -0.0169024997 0.2893246523 0.180226808
## 2015-05-29 -0.0032214231 -0.0097808439 0.1145793430 0.103899574
## 2015-06-30 -0.0542539825 -0.0220411793 0.0513461801 0.067300935
## 2015-07-31 0.0730816914 0.1839181571 0.1972314899 -0.007896616
## 2015-08-31 -0.0340551290 -0.0118342869 0.0062789186 -0.066366250
## 2015-09-30 0.0317637544 -0.0160274954 -0.1079428532 -0.002653519
## 2015-10-30 0.0895905104 0.1555397601 0.0483934447 -0.182659777
## 2015-11-30 0.0232366478 0.0437523489 0.1292201596 0.106828586
## 2015-12-31 0.0004957211 0.0216861540 -0.0753374900 0.041471519
## 2016-01-29 -0.0664309659 -0.0212149794 -0.2194783218 -0.227360626
## 2016-02-29 -0.0045315927 -0.0627391949 0.0169505524 0.003810669
## 2016-03-31 0.0490979494 0.0654275900 0.0902267645 0.179948109
## 2016-04-29 -0.0588894860 -0.0722726575 -0.1270822750 0.046721797
## 2016-05-31 0.0043110957 0.0598051611 0.1304025548 -0.075597968
## 2016-06-30 0.0540987059 -0.0611191370 -0.1144250835 -0.050296440
## 2016-07-29 0.0628101291 0.1050873352 -0.0025174134 0.100785334
## 2016-08-31 -0.0284744524 -0.0022658019 0.0657364022 -0.102058091
## 2016-09-30 -0.0609218486 0.0132615062 0.0112246699 -0.038366372
## 2016-10-31 -0.0308961678 0.0092841269 0.2367091536 -0.031364583
## 2016-11-30 0.0181076140 -0.0343615032 -0.0650992833 -0.043041267
## 2016-12-30 0.0644925915 0.0180152164 0.0564934501 0.120665178
## 2017-01-31 0.0237005699 0.0318398164 0.1280336976 0.164624916
## 2017-02-28 0.0802945114 0.0326201197 0.0100410836 -0.007730364
## 2017-03-31 -0.0550488893 0.0076841895 0.0391854826 0.107278727
## 2017-04-28 0.0569661510 0.0880997263 0.0292677767 0.120916212
## 2017-05-31 0.0587794166 0.0629878112 0.0689841760 0.082295892
## 2017-06-30 -0.1206064481 -0.0599350027 -0.0874853720 0.058654468
## 2017-07-31 -0.0089182333 0.0236741050 0.1954425989 -0.111459860
## 2017-08-31 -0.0080374329 0.0094446956 -0.0390093333 0.095543446
## 2017-09-29 0.0470448974 0.0208390185 0.0373014045 -0.042474144
## 2017-10-31 -0.0197322105 0.0582525689 0.0798771971 -0.028457409
## 2017-11-30 0.1383318001 0.0046809326 -0.0461006656 -0.070862541
## 2017-12-29 0.0091217268 0.0241716306 0.0230816211 0.008061928
## 2018-01-31 0.0459409279 0.1115968360 0.3422453562 0.129254571
## 2018-02-28 -0.0179103531 -0.0573515717 0.0750958659 -0.032266877
## 2018-03-29 -0.0130234025 -0.0683057695 0.0135328404 -0.253920408
## 2018-04-30 0.0452892386 -0.0141136336 0.0563153192 0.099254576
## 2018-05-31 0.0083743648 0.0643892141 0.1180177495 -0.031698139
## 2018-06-29 0.0527603800 0.0278664957 0.1073124981 0.186043257
## 2018-07-31 0.0455079067 0.0871651844 -0.1483892939 -0.140021491
## 2018-08-31 0.0663290930 0.0007637293 0.0857955874 0.011737389
## 2018-09-28 0.0074785902 -0.0205011396 0.0173903689 -0.130439038
## 2018-10-31 -0.0269697142 -0.1028991701 -0.2149050726 0.242170576
## 2018-11-30 0.0138981500 0.0162678569 -0.0532520010 0.038271580
## 2018-12-31 -0.1269317555 -0.0552431137 -0.0667287380 -0.051761952
## 2019-01-31 0.0522183155 0.0750917770 0.2377564149 -0.080628789
## 2019-02-28 0.0216655872 0.0031748582 0.0533383470 0.041032981
## 2019-03-29 0.1016321334 0.0465716277 -0.0043097723 -0.133656413
## 2019-04-30 0.0139030813 0.0128463510 0.0384589024 -0.159123803
## 2019-05-31 -0.0218347898 -0.0740704455 -0.0764149871 -0.253945372
## 2019-06-28 0.0980462181 -0.0208014610 0.0676869856 0.188012109
## 2019-07-31 0.0421259555 0.1183225072 -0.1286120899 0.078092373
## 2019-08-30 0.0693117288 -0.0237704474 -0.0948922673 -0.068516948
## 2019-09-30 -0.0228191606 0.0256755141 -0.0931610198 0.065449565
## 2019-10-31 0.0329299048 0.0331681361 0.0713417349 0.268061253
## 2019-11-29 0.0090469587 0.0349733866 0.0905829220 0.046592176
## 2019-12-31 -0.0198413575 0.0242707998 0.0279227940 0.237359743
## 2020-01-31 0.0387072188 0.0701849397 0.0643897455 0.441578342
## 2020-02-28 -0.0810557568 -0.0684586628 0.0670726963 0.026424253
## 2020-03-31 0.0140919855 -0.1413299968 0.0173805330 -0.242781458
## 2020-04-30 0.0630699038 0.1482720617 0.1116390480 0.400209535
## 2020-05-29 0.0178916769 0.0578073988 -0.0002858456 0.065730500
## 2020-06-30 -0.0171988926 -0.0107722444 0.0807736724 0.257108657
## 2020-07-31 0.0731776305 0.0478934210 0.0717317246 0.281420674
## 2020-08-31 0.0657702230 0.0971010642 0.0799293979 0.554719320
## 2020-09-30 0.0208928021 -0.1061508397 -0.0573783743 -0.149762306
## 2020-10-30 0.0092732145 0.0980590840 -0.0497966282 -0.100371771
## 2020-11-30 0.0912039169 0.0826847930 0.0309615020 0.380308519
## 2020-12-31 -0.0131571276 -0.0050446871 0.0970870744 0.217730753
## 2021-01-29 -0.0668091227 0.0467581545 -0.0155437114 0.117343701
## 2021-02-26 -0.0607612627 0.1039617467 0.0120608627 -0.161038203
## 2021-03-31 0.0628755843 0.0154771302 -0.0324212105 -0.011269836
## 2021-04-30 0.0562815666 0.1527899199 -0.0158244352 0.060292565
## 2021-05-28 0.0164725011 0.0005974000 -0.0209791877 -0.126372343
## 2021-06-30 0.0449724818 0.0385416843 0.0492816132 0.083547955
## 2021-07-30 0.0844260687 0.0760718450 -0.0203491777 0.010973846
## 2021-08-31 0.0582397822 0.0730044848 0.0950695060 0.068224268
## 2021-09-30 -0.0135717006 -0.0875714716 0.0698019211 0.052632612
## 2021-10-29 0.0913578682 0.1066948290 0.1231245456 0.362230202
## 2021-11-30 0.0928768203 -0.0400332327 -0.0727082022 0.027237848
## 2021-12-31 0.0511729417 0.0155160090 -0.0634444870 -0.079968440
## 2022-01-31 -0.1167773301 -0.0640854551 -0.3438762168 -0.120597475
## 2022-02-28 0.0290840861 -0.0059684487 -0.0794420544 -0.073397011
## 2022-03-31 0.1034617976 0.0346686002 -0.0518377235 0.213504290
## 2022-04-29 -0.0781046009 -0.1944949402 -0.6769150653 -0.213125289
## 2022-05-31 -0.1314593431 -0.0081002799 0.0365177336 -0.138340062
## 2022-06-30 0.0276272433 -0.0417810595 -0.1213919403 -0.118657126
## 2022-07-29 0.1234134665 0.0643327822 0.2516130190 0.280480149
## 2022-08-31 -0.0361147826 -0.0663691606 -0.0059760083 -0.075250271
## 2022-09-30 -0.1003082273 -0.1268135963 0.0517762908 -0.038313992
## 2022-10-31 0.0618564802 -0.0156179238 0.2148866613 -0.153346760
## 2022-11-30 0.0725756255 0.0692745192 0.0457051938 -0.155866121
## 2022-12-30 -0.1665905634 -0.1339679494 -0.0354794590 -0.457813194
## 2023-01-31 0.1130547825 0.1182712514 0.1823328028 0.340915767
## 2023-02-28 -0.0524475093 -0.1007318544 -0.0939461074 0.171904973
## 2023-03-31 0.0258717045 0.1412534080 0.0699795803 0.008471140
## 2023-04-28 0.0126990826 0.0397752831 -0.0460542977 -0.233183710
## 2023-05-31 0.0185209142 0.1310217951 0.1805874132 0.216021889
## 2023-06-30 0.0510996471 -0.0196454366 0.1084198198 0.249689452
## 2023-07-31 0.0405679293 0.0956332507 -0.0034566451 0.021391609
## 2023-08-31 -0.0186363046 0.0313565826 -0.0121241583 -0.035588260
## 2023-09-29 0.0281465321 -0.0408674663 -0.1384714676 -0.030929027
## 2023-10-31 -0.0224102040 -0.0509540648 0.0864351944 -0.219831983
## 2023-11-30 0.0722447221 0.0665317218 0.1408733862 0.178463656
## 2023-12-29 0.1300907626 0.0510206888 0.0268736650 0.034390133
## 2024-01-31 0.0513781346 0.0061543537 0.1472315458 -0.282704160
## 2024-02-29 0.0696229645 -0.0143479382 0.0665352476 0.075015304
## 2024-03-28 -0.0152523574 0.0855198712 0.0072878392 -0.138383423
## 2024-04-30 -0.0117661258 0.0781717370 -0.0979910641 0.041724969
## 2024-05-31 0.1136275203 0.0550640917 0.1529149558 -0.028782134
## 2024-06-26 0.0563527807 0.0646657119 0.0546937393 0.097767712# Covariance of asset returns
covariance_matrix <- cov(asset_returns_wide_tbl)
covariance_matrix## COST GOOG NFLX TSLA
## COST 0.003122385 0.001744223 0.001853619 0.002500825
## GOOG 0.001744223 0.004310828 0.003851547 0.003583974
## NFLX 0.001853619 0.003851547 0.017735168 0.006691905
## TSLA 0.002500825 0.003583974 0.006691905 0.029228753# 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, 0.25, .25)
sd_portfolio <- sqrt(t(w) %*% covariance_matrix %*% w)
sd_portfolio## [,1]
## [1,] 0.07699404# 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## COST GOOG NFLX TSLA
## [1,] 0.007485199 0.01095099 0.02445988 0.03409798rowSums(component_contribution)## [1] 0.07699404# Component contribution in percentage
component_percentages <- (component_contribution / sd_portfolio[1,1]) %>%
round(3) %>%
as_tibble()
component_percentages## # A tibble: 1 × 4
## COST GOOG NFLX TSLA
## <dbl> <dbl> <dbl> <dbl>
## 1 0.097 0.142 0.318 0.443component_percentages %>%
as_tibble() %>%
gather(key = "asset", value = "contribution")## # A tibble: 4 × 2
## asset contribution
## <chr> <dbl>
## 1 COST 0.097
## 2 GOOG 0.142
## 3 NFLX 0.318
## 4 TSLA 0.443Custom Function
# 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 ## COST GOOG NFLX TSLA
## 2013-01-31 0.0359115681 0.0660632170 0.5792178072 0.102078114
## 2013-02-28 -0.0076472531 0.0584794011 0.1294683790 -0.074128640
## 2013-03-28 0.0464885488 -0.0087878463 0.0063600708 0.084208138
## 2013-04-30 0.0216284517 0.0375393257 0.1323750202 0.354111531
## 2013-05-31 0.0137932762 0.0550324761 0.0460381788 0.593716684
## 2013-06-28 0.0085374770 0.0104477267 -0.0693560587 0.093672163
## 2013-07-31 0.0601084428 0.0083478658 0.1468488828 0.223739522
## 2013-08-30 -0.0458220385 -0.0471074271 0.1495238153 0.229971642
## 2013-09-30 0.0290715036 0.0336805999 0.0853633452 0.134706620
## 2013-10-31 0.0242754411 0.1626137315 0.0420204523 -0.189806595
## 2013-11-29 0.0635980302 0.0277603361 0.1260456325 -0.228409405
## 2013-12-31 -0.0524563676 0.0560803485 0.0064580908 0.167108541
## 2014-01-31 -0.0575831769 0.0523737221 0.1059769064 0.187261714
## 2014-02-28 0.0414542673 0.0289427457 0.0849673396 0.299722785
## 2014-03-31 -0.0448253593 -0.0863761472 -0.2357726219 -0.160783242
## 2014-04-30 0.0351903031 -0.0559561794 -0.0890406532 -0.002690122
## 2014-05-30 0.0059918074 0.0611852260 0.2603988640 -0.000577422
## 2014-06-30 -0.0074401327 0.0271164958 0.0530627854 0.144457224
## 2014-07-31 0.0234832627 -0.0064174656 -0.0414273713 -0.072372676
## 2014-08-29 0.0296729444 0.0000000000 0.1221472352 0.188794007
## 2014-09-30 0.0344188093 0.0100265853 -0.0569909207 -0.105566477
## 2014-10-31 0.0622562635 -0.0321734129 -0.1386421470 -0.004046477
## 2014-11-28 0.0661384138 -0.0313402973 -0.1250817171 0.011599801
## 2014-12-31 -0.0026066361 -0.0288909045 -0.0144727330 -0.094774519
## 2015-01-30 0.0087100868 0.0153077799 0.2571875124 -0.088365289
## 2015-02-27 0.0623765262 0.0437063825 0.0722680160 -0.001277808
## 2015-03-31 0.0304255797 -0.0188002460 -0.1307827695 -0.074350051
## 2015-04-30 -0.0546593581 -0.0169024997 0.2893246523 0.180226808
## 2015-05-29 -0.0032214231 -0.0097808439 0.1145793430 0.103899574
## 2015-06-30 -0.0542539825 -0.0220411793 0.0513461801 0.067300935
## 2015-07-31 0.0730816914 0.1839181571 0.1972314899 -0.007896616
## 2015-08-31 -0.0340551290 -0.0118342869 0.0062789186 -0.066366250
## 2015-09-30 0.0317637544 -0.0160274954 -0.1079428532 -0.002653519
## 2015-10-30 0.0895905104 0.1555397601 0.0483934447 -0.182659777
## 2015-11-30 0.0232366478 0.0437523489 0.1292201596 0.106828586
## 2015-12-31 0.0004957211 0.0216861540 -0.0753374900 0.041471519
## 2016-01-29 -0.0664309659 -0.0212149794 -0.2194783218 -0.227360626
## 2016-02-29 -0.0045315927 -0.0627391949 0.0169505524 0.003810669
## 2016-03-31 0.0490979494 0.0654275900 0.0902267645 0.179948109
## 2016-04-29 -0.0588894860 -0.0722726575 -0.1270822750 0.046721797
## 2016-05-31 0.0043110957 0.0598051611 0.1304025548 -0.075597968
## 2016-06-30 0.0540987059 -0.0611191370 -0.1144250835 -0.050296440
## 2016-07-29 0.0628101291 0.1050873352 -0.0025174134 0.100785334
## 2016-08-31 -0.0284744524 -0.0022658019 0.0657364022 -0.102058091
## 2016-09-30 -0.0609218486 0.0132615062 0.0112246699 -0.038366372
## 2016-10-31 -0.0308961678 0.0092841269 0.2367091536 -0.031364583
## 2016-11-30 0.0181076140 -0.0343615032 -0.0650992833 -0.043041267
## 2016-12-30 0.0644925915 0.0180152164 0.0564934501 0.120665178
## 2017-01-31 0.0237005699 0.0318398164 0.1280336976 0.164624916
## 2017-02-28 0.0802945114 0.0326201197 0.0100410836 -0.007730364
## 2017-03-31 -0.0550488893 0.0076841895 0.0391854826 0.107278727
## 2017-04-28 0.0569661510 0.0880997263 0.0292677767 0.120916212
## 2017-05-31 0.0587794166 0.0629878112 0.0689841760 0.082295892
## 2017-06-30 -0.1206064481 -0.0599350027 -0.0874853720 0.058654468
## 2017-07-31 -0.0089182333 0.0236741050 0.1954425989 -0.111459860
## 2017-08-31 -0.0080374329 0.0094446956 -0.0390093333 0.095543446
## 2017-09-29 0.0470448974 0.0208390185 0.0373014045 -0.042474144
## 2017-10-31 -0.0197322105 0.0582525689 0.0798771971 -0.028457409
## 2017-11-30 0.1383318001 0.0046809326 -0.0461006656 -0.070862541
## 2017-12-29 0.0091217268 0.0241716306 0.0230816211 0.008061928
## 2018-01-31 0.0459409279 0.1115968360 0.3422453562 0.129254571
## 2018-02-28 -0.0179103531 -0.0573515717 0.0750958659 -0.032266877
## 2018-03-29 -0.0130234025 -0.0683057695 0.0135328404 -0.253920408
## 2018-04-30 0.0452892386 -0.0141136336 0.0563153192 0.099254576
## 2018-05-31 0.0083743648 0.0643892141 0.1180177495 -0.031698139
## 2018-06-29 0.0527603800 0.0278664957 0.1073124981 0.186043257
## 2018-07-31 0.0455079067 0.0871651844 -0.1483892939 -0.140021491
## 2018-08-31 0.0663290930 0.0007637293 0.0857955874 0.011737389
## 2018-09-28 0.0074785902 -0.0205011396 0.0173903689 -0.130439038
## 2018-10-31 -0.0269697142 -0.1028991701 -0.2149050726 0.242170576
## 2018-11-30 0.0138981500 0.0162678569 -0.0532520010 0.038271580
## 2018-12-31 -0.1269317555 -0.0552431137 -0.0667287380 -0.051761952
## 2019-01-31 0.0522183155 0.0750917770 0.2377564149 -0.080628789
## 2019-02-28 0.0216655872 0.0031748582 0.0533383470 0.041032981
## 2019-03-29 0.1016321334 0.0465716277 -0.0043097723 -0.133656413
## 2019-04-30 0.0139030813 0.0128463510 0.0384589024 -0.159123803
## 2019-05-31 -0.0218347898 -0.0740704455 -0.0764149871 -0.253945372
## 2019-06-28 0.0980462181 -0.0208014610 0.0676869856 0.188012109
## 2019-07-31 0.0421259555 0.1183225072 -0.1286120899 0.078092373
## 2019-08-30 0.0693117288 -0.0237704474 -0.0948922673 -0.068516948
## 2019-09-30 -0.0228191606 0.0256755141 -0.0931610198 0.065449565
## 2019-10-31 0.0329299048 0.0331681361 0.0713417349 0.268061253
## 2019-11-29 0.0090469587 0.0349733866 0.0905829220 0.046592176
## 2019-12-31 -0.0198413575 0.0242707998 0.0279227940 0.237359743
## 2020-01-31 0.0387072188 0.0701849397 0.0643897455 0.441578342
## 2020-02-28 -0.0810557568 -0.0684586628 0.0670726963 0.026424253
## 2020-03-31 0.0140919855 -0.1413299968 0.0173805330 -0.242781458
## 2020-04-30 0.0630699038 0.1482720617 0.1116390480 0.400209535
## 2020-05-29 0.0178916769 0.0578073988 -0.0002858456 0.065730500
## 2020-06-30 -0.0171988926 -0.0107722444 0.0807736724 0.257108657
## 2020-07-31 0.0731776305 0.0478934210 0.0717317246 0.281420674
## 2020-08-31 0.0657702230 0.0971010642 0.0799293979 0.554719320
## 2020-09-30 0.0208928021 -0.1061508397 -0.0573783743 -0.149762306
## 2020-10-30 0.0092732145 0.0980590840 -0.0497966282 -0.100371771
## 2020-11-30 0.0912039169 0.0826847930 0.0309615020 0.380308519
## 2020-12-31 -0.0131571276 -0.0050446871 0.0970870744 0.217730753
## 2021-01-29 -0.0668091227 0.0467581545 -0.0155437114 0.117343701
## 2021-02-26 -0.0607612627 0.1039617467 0.0120608627 -0.161038203
## 2021-03-31 0.0628755843 0.0154771302 -0.0324212105 -0.011269836
## 2021-04-30 0.0562815666 0.1527899199 -0.0158244352 0.060292565
## 2021-05-28 0.0164725011 0.0005974000 -0.0209791877 -0.126372343
## 2021-06-30 0.0449724818 0.0385416843 0.0492816132 0.083547955
## 2021-07-30 0.0844260687 0.0760718450 -0.0203491777 0.010973846
## 2021-08-31 0.0582397822 0.0730044848 0.0950695060 0.068224268
## 2021-09-30 -0.0135717006 -0.0875714716 0.0698019211 0.052632612
## 2021-10-29 0.0913578682 0.1066948290 0.1231245456 0.362230202
## 2021-11-30 0.0928768203 -0.0400332327 -0.0727082022 0.027237848
## 2021-12-31 0.0511729417 0.0155160090 -0.0634444870 -0.079968440
## 2022-01-31 -0.1167773301 -0.0640854551 -0.3438762168 -0.120597475
## 2022-02-28 0.0290840861 -0.0059684487 -0.0794420544 -0.073397011
## 2022-03-31 0.1034617976 0.0346686002 -0.0518377235 0.213504290
## 2022-04-29 -0.0781046009 -0.1944949402 -0.6769150653 -0.213125289
## 2022-05-31 -0.1314593431 -0.0081002799 0.0365177336 -0.138340062
## 2022-06-30 0.0276272433 -0.0417810595 -0.1213919403 -0.118657126
## 2022-07-29 0.1234134665 0.0643327822 0.2516130190 0.280480149
## 2022-08-31 -0.0361147826 -0.0663691606 -0.0059760083 -0.075250271
## 2022-09-30 -0.1003082273 -0.1268135963 0.0517762908 -0.038313992
## 2022-10-31 0.0618564802 -0.0156179238 0.2148866613 -0.153346760
## 2022-11-30 0.0725756255 0.0692745192 0.0457051938 -0.155866121
## 2022-12-30 -0.1665905634 -0.1339679494 -0.0354794590 -0.457813194
## 2023-01-31 0.1130547825 0.1182712514 0.1823328028 0.340915767
## 2023-02-28 -0.0524475093 -0.1007318544 -0.0939461074 0.171904973
## 2023-03-31 0.0258717045 0.1412534080 0.0699795803 0.008471140
## 2023-04-28 0.0126990826 0.0397752831 -0.0460542977 -0.233183710
## 2023-05-31 0.0185209142 0.1310217951 0.1805874132 0.216021889
## 2023-06-30 0.0510996471 -0.0196454366 0.1084198198 0.249689452
## 2023-07-31 0.0405679293 0.0956332507 -0.0034566451 0.021391609
## 2023-08-31 -0.0186363046 0.0313565826 -0.0121241583 -0.035588260
## 2023-09-29 0.0281465321 -0.0408674663 -0.1384714676 -0.030929027
## 2023-10-31 -0.0224102040 -0.0509540648 0.0864351944 -0.219831983
## 2023-11-30 0.0722447221 0.0665317218 0.1408733862 0.178463656
## 2023-12-29 0.1300907626 0.0510206888 0.0268736650 0.034390133
## 2024-01-31 0.0513781346 0.0061543537 0.1472315458 -0.282704160
## 2024-02-29 0.0696229645 -0.0143479382 0.0665352476 0.075015304
## 2024-03-28 -0.0152523574 0.0855198712 0.0072878392 -0.138383423
## 2024-04-30 -0.0117661258 0.0781717370 -0.0979910641 0.041724969
## 2024-05-31 0.1136275203 0.0550640917 0.1529149558 -0.028782134
## 2024-06-26 0.0563527807 0.0646657119 0.0546937393 0.097767712calculate_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
# 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, .25, .25)) ## # A tibble: 1 × 4
## COST GOOG NFLX TSLA
## <dbl> <dbl> <dbl> <dbl>
## 1 0.097 0.142 0.318 0.4436 Plot: Column Chart of Component Contribution and Weight
Column Chart of Compound Contribution
plot_data <- asset_returns_wide_tbl %>%
calculate_component_contribution(w = c(.25, .25, .25, .25)) %>%
# Transform to long from
pivot_longer(cols = everything(), names_to = "Asset", values_to = "Contribution") %>%
#Add weights
add_column(weight = c(.25, .25, .25, .25)) %>%
# Transform to log
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 Votality and Weight")
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 contributors to my portfolio’s volatility are Netflix Inc. and Tesla Inc, they both contribute to about 44% and 32%. I do believe my portfolio’s volatility is concentrated with Tesla Inc., mainly due to the fact that Tesla Inc.’s volatility accounts for such a high portion of the overall volatility fo the portfolio.