Apply it to your data 12

# 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("GE", "GILD", "MSCI", "SBUX", "ROL")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices",    
                 from = "2012-12-31",
                 to   = "2022-12-01")

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

##                       GE          GILD         MSCI          ROL         SBUX
## 2013-01-31  0.0596434695  0.0715761823  0.085019553  0.114753594  0.045383720
## 2013-02-28  0.0494738041  0.0796333126 -0.018244984 -0.004930403 -0.019139167
## 2013-03-28 -0.0043160583  0.1359277718  0.023860405  0.001630417  0.037571478
## 2013-04-30 -0.0365595549  0.0341468140  0.004998234 -0.009412558  0.066073766
## 2013-05-31  0.0451729121  0.0730920886  0.032884152  0.041609191  0.040474103
## 2013-06-28  0.0023646253 -0.0607280546 -0.057525749  0.025020926  0.036848171
## 2013-07-31  0.0496317164  0.1803038954  0.052119172 -0.015564336  0.084553393
## 2013-08-30 -0.0517902771 -0.0185751991  0.067832382 -0.026292243 -0.008016991
## 2013-09-30  0.0395693431  0.0422345326  0.070750476  0.068696674  0.087519463
## 2013-10-31  0.0900068538  0.1241428281  0.012588636  0.041741911  0.051650483
## 2013-11-29  0.0196978424  0.0497399210  0.085067484  0.024239357  0.008261097
## 2013-12-31  0.0581697127  0.0038685973 -0.015208484  0.073980686 -0.038415403
## 2014-01-31 -0.1092128883  0.0712986638 -0.023138459 -0.049747752 -0.097327871
## 2014-02-28  0.0221377815  0.0261883010  0.022909503  0.039278372  0.001524581
## 2014-03-31  0.0163557243 -0.1556011666 -0.015911617  0.012645663  0.033535185
## 2014-04-30  0.0378975739  0.1022648775 -0.059375983 -0.005304846 -0.038338449
## 2014-05-30 -0.0037258204  0.0340674060  0.062625053  0.022950306  0.040101583
## 2014-06-30 -0.0110056608  0.0207173593  0.060460927 -0.022087451  0.054986180
## 2014-07-31 -0.0439501645  0.0991293826 -0.013172351 -0.057982179  0.003869392
## 2014-08-29  0.0324689830  0.1611634765  0.019477363  0.053293173  0.005031593
## 2014-09-30 -0.0055436529 -0.0103734959  0.018893136 -0.015923861 -0.030667238
## 2014-10-31  0.0073886020  0.0508237365 -0.003642119  0.084760116  0.001324586
## 2014-11-28  0.0260051237 -0.1101337475  0.035785906  0.026878351  0.076232922
## 2014-12-31 -0.0377784992 -0.0623083897 -0.019207332  0.017370738  0.010290737
## 2015-01-30 -0.0561577872  0.1062832703  0.126179338 -0.001511763  0.064652869
## 2015-02-27  0.0934025708 -0.0124787131  0.044812543  0.018275821  0.069516032
## 2015-03-31 -0.0464653376 -0.0535680370  0.088629020  0.100743352  0.012859747
## 2015-04-30  0.0875488086  0.0239640244 -0.001959263  0.002826572  0.046020554
## 2015-05-29  0.0069916457  0.1106495138  0.016894323  0.004067306  0.050063331
## 2015-06-30 -0.0175345418  0.0455666235 -0.008090452  0.139306485  0.031448030
## 2015-07-31 -0.0178474920  0.0066398760  0.102008243  0.016339726  0.077313135
## 2015-08-31 -0.0502852437 -0.1148706607 -0.115438291 -0.035299092 -0.054435476
## 2015-09-30  0.0248968356 -0.0637924405 -0.017670049 -0.038332894  0.038193727
## 2015-10-30  0.1368959649  0.0964298901  0.119388769 -0.001862652  0.096045611
## 2015-11-30  0.0346622791 -0.0202725342  0.048830281  0.019055072 -0.015806325
## 2015-12-31  0.0470705283 -0.0417536859  0.028261889 -0.047133727 -0.022402672
## 2016-01-29 -0.0680761527 -0.1981592288 -0.046685545  0.061759707  0.012251897
## 2016-02-29  0.0093476044  0.0499368454  0.027324554  0.003097725 -0.039776560
## 2016-03-31  0.0870398771  0.0562959251  0.049249190 -0.015004840  0.025274931
## 2016-04-29 -0.0332618129 -0.0405454312  0.024798014 -0.009260986 -0.059881563
## 2016-05-31 -0.0170551288 -0.0131224241  0.052302459  0.059789140 -0.020626503
## 2016-06-30  0.0480647028 -0.0370970840 -0.034035849  0.029469825  0.039823107
## 2016-07-29 -0.0108592251 -0.0485087076  0.109449813 -0.037950764  0.016150536
## 2016-08-31  0.0032062610 -0.0138106455  0.049587400  0.014912350 -0.028377652
## 2016-09-30 -0.0454754170  0.0154359866 -0.071039652  0.027000303 -0.037877287
## 2016-10-31 -0.0177114958 -0.0719133678 -0.045703694  0.051259338 -0.019961501
## 2016-11-30  0.0554768919  0.0009502942 -0.014026316  0.048160184  0.092935705
## 2016-12-30  0.0344397566 -0.0223501562 -0.000253843  0.050078715 -0.043182489
## 2017-01-31 -0.0620102093  0.0116620362  0.049164876  0.042880288 -0.005418327
## 2017-02-28  0.0116414394 -0.0275675715  0.136712350  0.039408690  0.033938745
## 2017-03-31 -0.0003354117 -0.0293337362  0.027116056  0.015470306  0.026376691
## 2017-04-28 -0.0275576245  0.0092328682  0.031697307  0.044767979  0.028199504
## 2017-05-31 -0.0571379216 -0.0548696914  0.016814297  0.106512696  0.061537506
## 2017-06-30 -0.0052053131  0.0948967215  0.012309555 -0.056352910 -0.086997173
## 2017-07-31 -0.0532244859  0.0723401925  0.056257058  0.064215982 -0.077159935
## 2017-08-31 -0.0422709111  0.0954414008  0.054129641  0.025383746  0.020674906
## 2017-09-29 -0.0052437122 -0.0264288912  0.019783878  0.038215783 -0.021185294
## 2017-10-31 -0.1818257029 -0.0777411510  0.003927248 -0.049538494  0.020820779
## 2017-11-30 -0.0973457494 -0.0024041845  0.095301623  0.058843116  0.058198376
## 2017-12-29 -0.0401340881 -0.0360833366 -0.016925590  0.003876059 -0.006768071
## 2018-01-31 -0.0761821170  0.1567792323  0.095568467  0.058637636 -0.010854341
## 2018-02-28 -0.1280322567 -0.0624086947  0.018926442  0.021616751  0.010501052
## 2018-03-29 -0.0456764851 -0.0362961381  0.054654659  0.015005005  0.013740608
## 2018-04-30  0.0428375189 -0.0428193113  0.002405191 -0.050437819 -0.005543110
## 2018-05-31  0.0007107486 -0.0692104132  0.084010985  0.028245527 -0.010541193
## 2018-06-29 -0.0251139773  0.0578061409  0.017439253  0.054923364 -0.148490466
## 2018-07-31  0.0014682626  0.0941029434  0.004583596  0.043905962  0.069961317
## 2018-08-31 -0.0519498954 -0.0273522610  0.084614949  0.091927107  0.026968865
## 2018-09-28 -0.1269196782  0.0270903069 -0.015936799  0.010101932  0.061493684
## 2018-10-31 -0.1113818343 -0.1243777790 -0.165297849 -0.024857282  0.024847065
## 2018-11-30 -0.2976325264  0.0536812908  0.047575462  0.074106581  0.140772150
## 2018-12-31  0.0106648012 -0.1314658830 -0.063465415 -0.160226305 -0.035391098
## 2019-01-31  0.2942653654  0.1126716864  0.144032075  0.031090574  0.056450532
## 2019-02-28  0.0616060147 -0.0739432239  0.084754349  0.065755355  0.035843545
## 2019-03-29 -0.0382005630  0.0094743146  0.073659388  0.048237547  0.056446624
## 2019-04-30  0.0178578101  0.0004613478  0.125287366 -0.073516516  0.043948474
## 2019-05-31 -0.0744865082 -0.0438439845 -0.021485774 -0.025995469 -0.016446744
## 2019-06-28  0.1073815630  0.0913361678  0.081911487 -0.046304543  0.097268369
## 2019-07-31 -0.0047732618 -0.0306606423 -0.049577577 -0.067460640  0.121817720
## 2019-08-30 -0.2363888565 -0.0306857138  0.035114223 -0.018493176  0.023339237
## 2019-09-30  0.0814027556  0.0067757146 -0.074662171  0.037683723 -0.088066478
## 2019-10-31  0.1100475562  0.0051931566  0.074363608  0.112059663 -0.044636755
## 2019-11-29  0.1215613955  0.0539268220  0.102582303 -0.057090726  0.015222209
## 2019-12-31 -0.0089022333 -0.0248506024 -0.003904282 -0.077999143  0.028729961
## 2020-01-31  0.1093846417 -0.0277753410  0.101635429  0.134925045 -0.035778109
## 2020-02-28 -0.1347943885  0.0930062057  0.035223063 -0.010474105 -0.073792941
## 2020-03-31 -0.3140203861  0.0842419861 -0.022177716 -0.035339337 -0.176498736
## 2020-04-30 -0.1549906539  0.1165339265  0.123672071  0.101479300  0.154585135
## 2020-05-29 -0.0344088107 -0.0762900511  0.007669778  0.045936645  0.021923266
## 2020-06-30  0.0403089746 -0.0026385843  0.015000043  0.014016220 -0.058071607
## 2020-07-31 -0.1179659991 -0.1012675788  0.118933963  0.211994070  0.039173686
## 2020-08-31  0.0435201567 -0.0408040510 -0.005032764  0.052461526  0.104138694
## 2020-09-30 -0.0158510739 -0.0443851012 -0.045182656 -0.017379102  0.017020248
## 2020-10-30  0.1748028436 -0.0831202722 -0.019643446  0.065357204  0.012031481
## 2020-11-30  0.3162458991  0.0424235461  0.159232328 -0.008318783  0.124427828
## 2020-12-31  0.0600407018 -0.0292790330  0.086764843  0.024615752  0.087470278
## 2021-01-29 -0.0111733753  0.1186601011 -0.121861645 -0.081280509 -0.099891681
## 2021-02-26  0.1605506660 -0.0661658716  0.049253390 -0.080277458  0.113907173
## 2021-03-31  0.0467135429  0.0625863030  0.011417738  0.036991882  0.011412812
## 2021-04-30 -0.0007620029 -0.0181112502  0.147196287  0.079819260  0.046665566
## 2021-05-28  0.0691961405  0.0407526252 -0.035292818 -0.087286688 -0.001371345
## 2021-06-30 -0.0428508771  0.0511331912  0.129925434  0.003221519 -0.018344234
## 2021-07-30 -0.0386265986 -0.0083120882  0.111502058  0.114007485  0.082536982
## 2021-08-31  0.0173203349  0.0636778342  0.064455529  0.017373732 -0.029110901
## 2021-09-30 -0.0220600964 -0.0310619609 -0.042229579 -0.096775667 -0.063058697
## 2021-10-29  0.0177012777 -0.0738107841  0.088872695 -0.002834499 -0.039195313
## 2021-11-30 -0.0989498363  0.0605522102 -0.053165921 -0.051893652  0.037405586
## 2021-12-31 -0.0046140982  0.0621172485 -0.026987214  0.027561368  0.064715152
## 2022-01-31  0.0001058190 -0.0556444703 -0.133501159 -0.103381227 -0.173689974
## 2022-02-28  0.0108428512 -0.1284689962 -0.064460490  0.059389116 -0.063666653
## 2022-03-31 -0.0419942411 -0.0031874014  0.002369132  0.071543438 -0.008973476
## 2022-04-29 -0.2048687715 -0.0018520226 -0.177125145 -0.044036712 -0.197853254
## 2022-05-31  0.0489497466  0.0887932933  0.051563270  0.058609314  0.057440723
## 2022-06-30 -0.2055131015 -0.0355933891 -0.070714140 -0.015345551 -0.027246970
## 2022-07-29  0.1491347717 -0.0338960371  0.155186530  0.099415121  0.104207927
## 2022-08-31 -0.0063794377  0.0603982012 -0.066572838 -0.130547808 -0.002728313
## 2022-09-30 -0.1695469356 -0.0171686626 -0.063049665  0.026886618  0.002257528
## 2022-10-31  0.2285882144  0.2404671010  0.105796643  0.193409473  0.027276930
## 2022-11-30  0.0997284779  0.1128142227  0.082517618 -0.036653271  0.171478081

# Covariance of asset returns
covariance_matrix <- cov(asset_returns_wide_tbl)

covariance_matrix

##                GE         GILD        MSCI          ROL         SBUX
## GE   0.0090952999 0.0010940844 0.002013042 0.0004372444 0.0016255421
## GILD 0.0010940844 0.0055761261 0.001247834 0.0002206646 0.0004109835
## MSCI 0.0020130425 0.0012478342 0.004524904 0.0016182804 0.0020206589
## ROL  0.0004372444 0.0002206646 0.001618280 0.0036493854 0.0008120992
## SBUX 0.0016255421 0.0004109835 0.002020659 0.0008120992 0.0040638689

# 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,] 0.04645226

# Component contribution
# Similar to the formula for sd_portfolio
# Mathematical trick to summarize the same, sd_portfolio, by asset instead of a single number
calculate_component_contribution <- (t(w) %*% covariance_matrix * w) / sd_portfolio[1,1]
calculate_component_contribution

##              GE       GILD        MSCI         ROL        SBUX
## [1,] 0.01722175 0.01077639 0.009669807 0.005593787 0.003190523

rowSums(calculate_component_contribution)

## [1] 0.04645226

# Component contribution in percentage
component_percentages <- (calculate_component_contribution / sd_portfolio[1,1]) %>%
    round(3) %>%
    as_tibble()

component_percentages

## # A tibble: 1 × 5
##      GE  GILD  MSCI   ROL  SBUX
##   <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.371 0.232 0.208  0.12 0.069

component_percentages %>%

    as_tibble() %>%
    gather(key = "asset", value = "contribution")

## # A tibble: 5 × 2
##   asset contribution
##   <chr>        <dbl>
## 1 GE           0.371
## 2 GILD         0.232
## 3 MSCI         0.208
## 4 ROL          0.12 
## 5 SBUX         0.069

4 Component Contribution with a Custom 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

##                       GE          GILD         MSCI          ROL         SBUX
## 2013-01-31  0.0596434695  0.0715761823  0.085019553  0.114753594  0.045383720
## 2013-02-28  0.0494738041  0.0796333126 -0.018244984 -0.004930403 -0.019139167
## 2013-03-28 -0.0043160583  0.1359277718  0.023860405  0.001630417  0.037571478
## 2013-04-30 -0.0365595549  0.0341468140  0.004998234 -0.009412558  0.066073766
## 2013-05-31  0.0451729121  0.0730920886  0.032884152  0.041609191  0.040474103
## 2013-06-28  0.0023646253 -0.0607280546 -0.057525749  0.025020926  0.036848171
## 2013-07-31  0.0496317164  0.1803038954  0.052119172 -0.015564336  0.084553393
## 2013-08-30 -0.0517902771 -0.0185751991  0.067832382 -0.026292243 -0.008016991
## 2013-09-30  0.0395693431  0.0422345326  0.070750476  0.068696674  0.087519463
## 2013-10-31  0.0900068538  0.1241428281  0.012588636  0.041741911  0.051650483
## 2013-11-29  0.0196978424  0.0497399210  0.085067484  0.024239357  0.008261097
## 2013-12-31  0.0581697127  0.0038685973 -0.015208484  0.073980686 -0.038415403
## 2014-01-31 -0.1092128883  0.0712986638 -0.023138459 -0.049747752 -0.097327871
## 2014-02-28  0.0221377815  0.0261883010  0.022909503  0.039278372  0.001524581
## 2014-03-31  0.0163557243 -0.1556011666 -0.015911617  0.012645663  0.033535185
## 2014-04-30  0.0378975739  0.1022648775 -0.059375983 -0.005304846 -0.038338449
## 2014-05-30 -0.0037258204  0.0340674060  0.062625053  0.022950306  0.040101583
## 2014-06-30 -0.0110056608  0.0207173593  0.060460927 -0.022087451  0.054986180
## 2014-07-31 -0.0439501645  0.0991293826 -0.013172351 -0.057982179  0.003869392
## 2014-08-29  0.0324689830  0.1611634765  0.019477363  0.053293173  0.005031593
## 2014-09-30 -0.0055436529 -0.0103734959  0.018893136 -0.015923861 -0.030667238
## 2014-10-31  0.0073886020  0.0508237365 -0.003642119  0.084760116  0.001324586
## 2014-11-28  0.0260051237 -0.1101337475  0.035785906  0.026878351  0.076232922
## 2014-12-31 -0.0377784992 -0.0623083897 -0.019207332  0.017370738  0.010290737
## 2015-01-30 -0.0561577872  0.1062832703  0.126179338 -0.001511763  0.064652869
## 2015-02-27  0.0934025708 -0.0124787131  0.044812543  0.018275821  0.069516032
## 2015-03-31 -0.0464653376 -0.0535680370  0.088629020  0.100743352  0.012859747
## 2015-04-30  0.0875488086  0.0239640244 -0.001959263  0.002826572  0.046020554
## 2015-05-29  0.0069916457  0.1106495138  0.016894323  0.004067306  0.050063331
## 2015-06-30 -0.0175345418  0.0455666235 -0.008090452  0.139306485  0.031448030
## 2015-07-31 -0.0178474920  0.0066398760  0.102008243  0.016339726  0.077313135
## 2015-08-31 -0.0502852437 -0.1148706607 -0.115438291 -0.035299092 -0.054435476
## 2015-09-30  0.0248968356 -0.0637924405 -0.017670049 -0.038332894  0.038193727
## 2015-10-30  0.1368959649  0.0964298901  0.119388769 -0.001862652  0.096045611
## 2015-11-30  0.0346622791 -0.0202725342  0.048830281  0.019055072 -0.015806325
## 2015-12-31  0.0470705283 -0.0417536859  0.028261889 -0.047133727 -0.022402672
## 2016-01-29 -0.0680761527 -0.1981592288 -0.046685545  0.061759707  0.012251897
## 2016-02-29  0.0093476044  0.0499368454  0.027324554  0.003097725 -0.039776560
## 2016-03-31  0.0870398771  0.0562959251  0.049249190 -0.015004840  0.025274931
## 2016-04-29 -0.0332618129 -0.0405454312  0.024798014 -0.009260986 -0.059881563
## 2016-05-31 -0.0170551288 -0.0131224241  0.052302459  0.059789140 -0.020626503
## 2016-06-30  0.0480647028 -0.0370970840 -0.034035849  0.029469825  0.039823107
## 2016-07-29 -0.0108592251 -0.0485087076  0.109449813 -0.037950764  0.016150536
## 2016-08-31  0.0032062610 -0.0138106455  0.049587400  0.014912350 -0.028377652
## 2016-09-30 -0.0454754170  0.0154359866 -0.071039652  0.027000303 -0.037877287
## 2016-10-31 -0.0177114958 -0.0719133678 -0.045703694  0.051259338 -0.019961501
## 2016-11-30  0.0554768919  0.0009502942 -0.014026316  0.048160184  0.092935705
## 2016-12-30  0.0344397566 -0.0223501562 -0.000253843  0.050078715 -0.043182489
## 2017-01-31 -0.0620102093  0.0116620362  0.049164876  0.042880288 -0.005418327
## 2017-02-28  0.0116414394 -0.0275675715  0.136712350  0.039408690  0.033938745
## 2017-03-31 -0.0003354117 -0.0293337362  0.027116056  0.015470306  0.026376691
## 2017-04-28 -0.0275576245  0.0092328682  0.031697307  0.044767979  0.028199504
## 2017-05-31 -0.0571379216 -0.0548696914  0.016814297  0.106512696  0.061537506
## 2017-06-30 -0.0052053131  0.0948967215  0.012309555 -0.056352910 -0.086997173
## 2017-07-31 -0.0532244859  0.0723401925  0.056257058  0.064215982 -0.077159935
## 2017-08-31 -0.0422709111  0.0954414008  0.054129641  0.025383746  0.020674906
## 2017-09-29 -0.0052437122 -0.0264288912  0.019783878  0.038215783 -0.021185294
## 2017-10-31 -0.1818257029 -0.0777411510  0.003927248 -0.049538494  0.020820779
## 2017-11-30 -0.0973457494 -0.0024041845  0.095301623  0.058843116  0.058198376
## 2017-12-29 -0.0401340881 -0.0360833366 -0.016925590  0.003876059 -0.006768071
## 2018-01-31 -0.0761821170  0.1567792323  0.095568467  0.058637636 -0.010854341
## 2018-02-28 -0.1280322567 -0.0624086947  0.018926442  0.021616751  0.010501052
## 2018-03-29 -0.0456764851 -0.0362961381  0.054654659  0.015005005  0.013740608
## 2018-04-30  0.0428375189 -0.0428193113  0.002405191 -0.050437819 -0.005543110
## 2018-05-31  0.0007107486 -0.0692104132  0.084010985  0.028245527 -0.010541193
## 2018-06-29 -0.0251139773  0.0578061409  0.017439253  0.054923364 -0.148490466
## 2018-07-31  0.0014682626  0.0941029434  0.004583596  0.043905962  0.069961317
## 2018-08-31 -0.0519498954 -0.0273522610  0.084614949  0.091927107  0.026968865
## 2018-09-28 -0.1269196782  0.0270903069 -0.015936799  0.010101932  0.061493684
## 2018-10-31 -0.1113818343 -0.1243777790 -0.165297849 -0.024857282  0.024847065
## 2018-11-30 -0.2976325264  0.0536812908  0.047575462  0.074106581  0.140772150
## 2018-12-31  0.0106648012 -0.1314658830 -0.063465415 -0.160226305 -0.035391098
## 2019-01-31  0.2942653654  0.1126716864  0.144032075  0.031090574  0.056450532
## 2019-02-28  0.0616060147 -0.0739432239  0.084754349  0.065755355  0.035843545
## 2019-03-29 -0.0382005630  0.0094743146  0.073659388  0.048237547  0.056446624
## 2019-04-30  0.0178578101  0.0004613478  0.125287366 -0.073516516  0.043948474
## 2019-05-31 -0.0744865082 -0.0438439845 -0.021485774 -0.025995469 -0.016446744
## 2019-06-28  0.1073815630  0.0913361678  0.081911487 -0.046304543  0.097268369
## 2019-07-31 -0.0047732618 -0.0306606423 -0.049577577 -0.067460640  0.121817720
## 2019-08-30 -0.2363888565 -0.0306857138  0.035114223 -0.018493176  0.023339237
## 2019-09-30  0.0814027556  0.0067757146 -0.074662171  0.037683723 -0.088066478
## 2019-10-31  0.1100475562  0.0051931566  0.074363608  0.112059663 -0.044636755
## 2019-11-29  0.1215613955  0.0539268220  0.102582303 -0.057090726  0.015222209
## 2019-12-31 -0.0089022333 -0.0248506024 -0.003904282 -0.077999143  0.028729961
## 2020-01-31  0.1093846417 -0.0277753410  0.101635429  0.134925045 -0.035778109
## 2020-02-28 -0.1347943885  0.0930062057  0.035223063 -0.010474105 -0.073792941
## 2020-03-31 -0.3140203861  0.0842419861 -0.022177716 -0.035339337 -0.176498736
## 2020-04-30 -0.1549906539  0.1165339265  0.123672071  0.101479300  0.154585135
## 2020-05-29 -0.0344088107 -0.0762900511  0.007669778  0.045936645  0.021923266
## 2020-06-30  0.0403089746 -0.0026385843  0.015000043  0.014016220 -0.058071607
## 2020-07-31 -0.1179659991 -0.1012675788  0.118933963  0.211994070  0.039173686
## 2020-08-31  0.0435201567 -0.0408040510 -0.005032764  0.052461526  0.104138694
## 2020-09-30 -0.0158510739 -0.0443851012 -0.045182656 -0.017379102  0.017020248
## 2020-10-30  0.1748028436 -0.0831202722 -0.019643446  0.065357204  0.012031481
## 2020-11-30  0.3162458991  0.0424235461  0.159232328 -0.008318783  0.124427828
## 2020-12-31  0.0600407018 -0.0292790330  0.086764843  0.024615752  0.087470278
## 2021-01-29 -0.0111733753  0.1186601011 -0.121861645 -0.081280509 -0.099891681
## 2021-02-26  0.1605506660 -0.0661658716  0.049253390 -0.080277458  0.113907173
## 2021-03-31  0.0467135429  0.0625863030  0.011417738  0.036991882  0.011412812
## 2021-04-30 -0.0007620029 -0.0181112502  0.147196287  0.079819260  0.046665566
## 2021-05-28  0.0691961405  0.0407526252 -0.035292818 -0.087286688 -0.001371345
## 2021-06-30 -0.0428508771  0.0511331912  0.129925434  0.003221519 -0.018344234
## 2021-07-30 -0.0386265986 -0.0083120882  0.111502058  0.114007485  0.082536982
## 2021-08-31  0.0173203349  0.0636778342  0.064455529  0.017373732 -0.029110901
## 2021-09-30 -0.0220600964 -0.0310619609 -0.042229579 -0.096775667 -0.063058697
## 2021-10-29  0.0177012777 -0.0738107841  0.088872695 -0.002834499 -0.039195313
## 2021-11-30 -0.0989498363  0.0605522102 -0.053165921 -0.051893652  0.037405586
## 2021-12-31 -0.0046140982  0.0621172485 -0.026987214  0.027561368  0.064715152
## 2022-01-31  0.0001058190 -0.0556444703 -0.133501159 -0.103381227 -0.173689974
## 2022-02-28  0.0108428512 -0.1284689962 -0.064460490  0.059389116 -0.063666653
## 2022-03-31 -0.0419942411 -0.0031874014  0.002369132  0.071543438 -0.008973476
## 2022-04-29 -0.2048687715 -0.0018520226 -0.177125145 -0.044036712 -0.197853254
## 2022-05-31  0.0489497466  0.0887932933  0.051563270  0.058609314  0.057440723
## 2022-06-30 -0.2055131015 -0.0355933891 -0.070714140 -0.015345551 -0.027246970
## 2022-07-29  0.1491347717 -0.0338960371  0.155186530  0.099415121  0.104207927
## 2022-08-31 -0.0063794377  0.0603982012 -0.066572838 -0.130547808 -0.002728313
## 2022-09-30 -0.1695469356 -0.0171686626 -0.063049665  0.026886618  0.002257528
## 2022-10-31  0.2285882144  0.2404671010  0.105796643  0.193409473  0.027276930
## 2022-11-30  0.0997284779  0.1128142227  0.082517618 -0.036653271  0.171478081

        calculate_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
##      GE  GILD  MSCI   ROL  SBUX
##   <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.371 0.232 0.208  0.12 0.069

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 to long 
    pivot_longer(cols = everything(), names_to = "Asset", values_to = "Contribution") %>%
    #add weight
    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() +
    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 the 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 competitor to risk is definitely in GE. Given the slightly higher concentration of the asset in my portfolio it brings a large portion of the portfolio risk.