Apply it to your data 12
Which of the assets the largest contributor to the portfolio volatility?
Conner Vaillancourt
2022-09-19
# 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.
symbol <- c("JNJ", "TSLA", "XOM", "COST", "PLUG")
prices <- tq_get(x = symbol,
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"))## asset date returns
## "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 = symbol, values_from = monthly.returns) %>%
column_to_rownames(var = "date")
asset_returns_wide_tbl## COST JNJ PLUG TSLA XOM
## 2013-01-31 0.035911945 0.053060375 -0.105360542 0.102078114 0.0387543810
## 2013-02-28 -0.007647638 0.037163374 -0.973449109 -0.074128640 0.0016888002
## 2013-03-28 0.046488544 0.068791668 0.257829093 0.084208138 0.0062342603
## 2013-04-30 0.021628332 0.044382786 -0.318453748 0.354111531 -0.0125070739
## 2013-05-31 0.013793111 -0.004907716 0.864997447 0.593716684 0.0233882767
## 2013-06-28 0.008537951 0.019760926 0.000000000 0.093672163 -0.0013273565
## 2013-07-31 0.060108422 0.085242882 0.146603481 0.223739522 0.0369405138
## 2013-08-30 -0.045822553 -0.071351674 0.186102231 0.229971642 -0.0659982908
## 2013-09-30 0.029072171 0.003235024 0.333773247 0.134706620 -0.0129331028
## 2013-10-31 0.024275301 0.066058516 -0.243622124 -0.189806595 0.0407664885
## 2013-11-29 0.063598031 0.028851325 0.243622124 -0.228409405 0.0489499222
## 2013-12-31 -0.052456420 -0.032969376 0.739359980 0.167108541 0.0793508382
## 2014-01-31 -0.057583514 -0.034658801 0.667001925 0.187261714 -0.0935721860
## 2014-02-28 0.041454711 0.047598785 0.435902263 0.299722785 0.0506882216
## 2014-03-31 -0.044825579 0.064219652 0.418935683 -0.160783242 0.0145401954
## 2014-04-30 0.035190543 0.030675921 -0.423227531 -0.002690122 0.0472877339
## 2014-05-30 0.005991936 0.008622210 -0.066691417 -0.000577422 -0.0117624448
## 2014-06-30 -0.007440224 0.030670267 0.073122250 0.144457224 0.0014909652
## 2014-07-31 0.023482928 -0.044265229 0.146797756 -0.072372676 -0.0174333560
## 2014-08-29 0.029673008 0.042462723 0.029092933 0.188794007 0.0121839607
## 2014-09-30 0.034419187 0.027198663 -0.195308705 -0.105566477 -0.0559283600
## 2014-10-31 0.062256493 0.011102114 0.025807859 -0.004046477 0.0278899130
## 2014-11-28 0.066138010 0.010843296 -0.209437511 0.011599801 -0.0587311330
## 2014-12-31 -0.002606783 -0.034586860 -0.241638116 -0.094774519 0.0208763385
## 2015-01-30 0.008709936 -0.043287512 -0.116533788 -0.088365289 -0.0559440150
## 2015-02-27 0.062376904 0.030366817 0.142851071 -0.001277808 0.0202290224
## 2015-03-31 0.030425468 -0.018808108 -0.173271730 -0.074350051 -0.0408029952
## 2015-04-30 -0.054659548 -0.014014468 -0.019493777 0.180226808 0.0275004442
## 2015-05-29 -0.003220835 0.016696934 0.068467825 0.103899574 -0.0169135700
## 2015-06-30 -0.054254101 -0.027127029 -0.104543847 0.067300935 -0.0237542039
## 2015-07-31 0.073081374 0.027826034 0.055569799 -0.007896616 -0.0491444208
## 2015-08-31 -0.034055494 -0.056565176 -0.415164450 -0.066366250 -0.0421067432
## 2015-09-30 0.031764306 -0.006726420 0.067822597 -0.002653519 -0.0118993599
## 2015-10-30 0.089590378 0.079061674 0.275310793 -0.182659777 0.1069197068
## 2015-11-30 0.023236655 0.009421067 -0.091169401 0.106828586 -0.0044590586
## 2015-12-31 0.000495634 0.014513149 -0.041769484 0.041471519 -0.0464969892
## 2016-01-29 -0.066431125 0.016605781 -0.120749464 -0.227360626 -0.0012834676
## 2016-02-29 -0.004531572 0.014566706 0.106429424 0.003810669 0.0381512799
## 2016-03-31 0.049097941 0.028023049 -0.014528087 0.179948109 0.0420240108
## 2016-04-29 -0.058889334 0.035231539 0.004866185 0.046721797 0.0559477838
## 2016-05-31 0.004310947 0.012542223 -0.070380791 -0.075597968 0.0153586432
## 2016-06-30 0.054099094 0.073625891 -0.031748668 -0.050296440 0.0516637645
## 2016-07-29 0.062809806 0.031885289 -0.038360897 0.100785334 -0.0524503179
## 2016-08-31 -0.028474213 -0.041526631 -0.143960698 -0.102058091 -0.0120674199
## 2016-09-30 -0.060921480 -0.010190893 0.098238493 -0.038366372 0.0016055062
## 2016-10-31 -0.030896982 -0.018281574 -0.111225676 -0.031364583 -0.0464326934
## 2016-11-30 0.018108153 -0.034388299 -0.110456973 -0.043041267 0.0554778179
## 2016-12-30 0.064492836 0.034527321 -0.132489147 0.120665178 0.0333437737
## 2017-01-31 0.023700127 -0.017158823 -0.124052742 0.164624916 -0.0731881368
## 2017-02-28 0.080294594 0.082738354 0.018692227 -0.007730364 -0.0220116886
## 2017-03-31 -0.055049043 0.018966316 0.245122415 0.107278727 0.0084490079
## 2017-04-28 0.056966470 -0.008709072 0.484392374 0.120916212 -0.0043994828
## 2017-05-31 0.058779609 0.044592972 -0.185899375 0.082295892 -0.0047857724
## 2017-06-30 -0.120607271 0.031014799 0.092373294 0.058654468 0.0028535435
## 2017-07-31 -0.008917960 0.003245474 0.102415020 -0.111459860 -0.0085839596
## 2017-08-31 -0.008037436 0.003700190 -0.054558931 0.095543446 -0.0378142406
## 2017-09-29 0.047044880 -0.017989625 0.198544303 -0.042474144 0.0714090331
## 2017-10-31 -0.019731613 0.069807834 0.087968780 -0.028457409 0.0165733576
## 2017-11-30 0.138331510 0.005531422 -0.184429054 -0.070862541 0.0084926205
## 2017-12-29 0.009121520 0.002795224 -0.004228332 0.008061928 0.0041932493
## 2018-01-31 0.045941233 -0.011010908 -0.201141599 0.129254571 0.0428282851
## 2018-02-28 -0.017910822 -0.055635678 -0.036943480 -0.032266877 -0.1318700926
## 2018-03-29 -0.013022924 -0.013409802 0.016000326 -0.253920408 -0.0150319318
## 2018-04-30 0.045288973 -0.013037874 -0.032260831 0.099254576 0.0412241103
## 2018-05-31 0.008374727 -0.048454043 0.021622443 -0.031698139 0.0539941265
## 2018-06-29 0.052759860 0.014276412 0.077159069 0.186043257 0.0181745262
## 2018-07-31 0.045508118 0.088137051 -0.009950321 -0.140021491 -0.0148566356
## 2018-08-31 0.066328873 0.022884467 -0.015113623 0.011737389 -0.0063144542
## 2018-09-28 0.007478649 0.025508850 -0.025708394 -0.130439038 0.0587370041
## 2018-10-31 -0.026969518 0.013086218 -0.037139512 0.242170576 -0.0648680278
## 2018-11-30 0.013898104 0.054528986 -0.055569864 0.038271580 0.0078246164
## 2018-12-31 -0.126931673 -0.129552148 -0.344504401 -0.051761952 -0.1534590429
## 2019-01-31 0.052218144 0.030750664 0.099699356 -0.080628789 0.0719896506
## 2019-02-28 0.021665759 0.033009703 0.267404855 0.041032981 0.0865808465
## 2019-03-29 0.101632012 0.022791324 0.293253179 -0.133656413 0.0221494164
## 2019-04-30 0.013903040 0.010036162 0.036813937 -0.159123803 -0.0064561867
## 2019-05-31 -0.021834826 -0.067016839 0.027724522 -0.253945372 -0.1146884144
## 2019-06-28 0.098046114 0.060144721 -0.129077020 0.188012109 0.0795537178
## 2019-07-31 0.042125958 -0.067260935 -0.017937683 0.078092373 -0.0300704007
## 2019-08-30 0.069311756 -0.006921029 -0.018265330 -0.068516948 -0.0700193054
## 2019-09-30 -0.022819555 0.007914974 0.192256687 0.065449565 0.0306299919
## 2019-10-31 0.032930626 0.020351102 0.007575786 0.268061253 -0.0440077037
## 2019-11-29 0.009046621 0.047350747 0.386416902 0.046592176 0.0202411299
## 2019-12-31 -0.019841196 0.059164431 -0.210404523 0.237359743 0.0239300372
## 2020-01-31 0.038707452 0.020357529 0.202682423 0.441578342 -0.1162795950
## 2020-02-28 -0.081056209 -0.095301314 0.114619906 0.026424253 -0.1743976440
## 2020-03-31 0.014092527 -0.025226455 -0.203747667 -0.242781458 -0.3036196012
## 2020-04-30 0.063069435 0.134712865 0.166184489 0.400209535 0.2020105090
## 2020-05-29 0.017891754 -0.001726668 0.007151451 0.065730500 -0.0025503816
## 2020-06-30 -0.017198698 -0.056134685 0.667890271 0.257108657 -0.0166319552
## 2020-07-31 0.073177499 0.035829044 -0.062834736 0.281420674 -0.0608480629
## 2020-08-31 0.065770185 0.057786468 0.520891483 0.554719320 -0.0326587201
## 2020-09-30 0.020892692 -0.029973701 0.032591010 -0.149762306 -0.1513585487
## 2020-10-30 0.009273319 -0.082356971 0.043056644 -0.100371771 -0.0510942073
## 2020-11-30 0.091203888 0.060665570 0.633927798 0.380308519 0.1799161766
## 2020-12-31 -0.013156903 0.084138870 0.250724824 0.217730753 0.0779220303
## 2021-01-29 -0.066809312 0.035884212 0.622119524 0.117343701 0.0841770123
## 2021-02-26 -0.060761212 -0.022829160 -0.266742949 -0.161038203 0.2095516686
## 2021-03-31 0.062875809 0.036496449 -0.300021935 -0.011269836 0.0264988259
## 2021-04-30 0.056281313 -0.009906075 -0.228809681 0.060292565 0.0249415482
## 2021-05-28 0.016472559 0.045504138 0.074007768 -0.126372343 0.0340119026
## 2021-06-30 0.044972220 -0.027008371 0.107670484 0.083547955 0.0776017392
## 2021-07-30 0.084426259 0.044287858 -0.225779305 0.010973846 -0.0914021796
## 2021-08-31 0.058239810 0.011311017 -0.045752307 0.068224268 -0.0394118509
## 2021-09-30 -0.013571575 -0.069537533 -0.020155665 0.052632612 0.0759144218
## 2021-10-29 0.091357728 0.008508624 0.404420425 0.362230202 0.0917181731
## 2021-11-30 0.092877053 -0.037077899 0.040456056 0.027237848 -0.0611759092
## 2021-12-31 0.051173018 0.092665727 -0.344737141 -0.079968440 0.0223093348
## 2022-01-31 -0.116777672 0.007106151 -0.255269353 -0.120597475 0.2162230546
## 2022-02-28 0.029084111 -0.039444391 0.145293224 -0.073397011 0.0428972706
## 2022-03-31 0.103461692 0.074112259 0.123347232 0.213504290 0.0518094492
## 2022-04-29 -0.078104723 0.018060855 -0.308281941 -0.213125289 0.0316995694
## 2022-05-31 -0.131459191 0.001238598 -0.128785346 -0.138340062 0.1289512411
## 2022-06-30 0.027627456 -0.011315499 -0.109095228 -0.118657126 -0.1141958096
## 2022-07-29 0.123413124 -0.016987643 0.252989440 0.280480149 0.1238366852
## 2022-08-31 -0.036114416 -0.071828849 0.273048842 -0.075250271 -0.0042515463
## 2022-09-30 -0.100308418 0.012442495 -0.288633569 -0.038313992 -0.0906031875
## 2022-10-31 0.061856501 0.062926559 -0.273660614 -0.153346760 0.2383519633
## 2022-11-30 0.072575688 0.029334560 -0.001252317 -0.155866121 0.0127896120
## 2022-12-30 -0.166590582 -0.007613294 -0.254811417 -0.457813194 -0.0093845534
## 2023-01-31 0.113054825 -0.077846715 0.319114973 0.340915767 0.0504724961
## 2023-02-28 -0.052447579 -0.057021030 -0.135043397 0.171904973 -0.0463491311
## 2023-03-31 0.025871761 0.011289243 -0.238048946 0.008471140 -0.0022771981
## 2023-04-28 0.012698949 0.054610335 -0.260744469 -0.233183710 0.0761769980
## 2023-05-31 0.018521010 -0.046706649 -0.081890120 0.216021889 -0.1381857498
## 2023-06-30 0.051099779 0.065279818 0.222181620 0.249689452 0.0484265242
## 2023-07-31 0.040567717 0.012070541 0.233293937 0.021391609 -0.0000932604
## 2023-08-31 -0.018636354 -0.028309147 -0.438788597 -0.035588260 0.0443351667
## 2023-09-29 0.028146710 -0.037366981 -0.107200943 -0.030929027 0.0558785461
## 2023-10-31 -0.022410226 -0.048745265 -0.254892260 -0.219831983 -0.1050959627
## 2023-11-30 0.072244637 0.049698505 -0.377011292 0.178463656 -0.0207188490
## 2023-12-29 0.130090809 0.013359230 0.107832714 0.034390133 -0.0272311522
## 2024-01-31 0.051378159 0.013686624 -0.011173343 -0.282704160 0.0279123754
## 2024-02-29 0.069622951 0.023050881 -0.231606190 0.075015304 0.0257465663
## 2024-03-28 -0.015252350 -0.019965061 -0.025826375 -0.138383423 0.1062785612
## 2024-04-30 -0.011766129 -0.089894744 -0.398223988 0.041724969 0.0173130899
## 2024-05-31 0.113627500 0.022333968 0.365724781 -0.028782134 -0.0004867061
## 2024-06-28 0.048326090 -0.003483192 -0.357104046 0.105427913 -0.0184186892
## 2024-07-31 -0.032061480 0.076943326 0.058349927 0.159378271 0.0296970934
## 2024-08-30 0.082151670 0.057060246 -0.272946388 -0.080549177 0.0025225098
## 2024-09-30 -0.006588376 -0.023177441 0.184093035 0.200441406 -0.0061235293
## 2024-10-31 -0.014017432 -0.013668186 -0.142420316 -0.046070548 -0.0037607114
## 2024-11-19 0.063387472 -0.043860515 -0.020619329 0.325578013 0.0239011149calculate_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(0.2, 0.2, 0.2, 0.2, 0.2))## # A tibble: 1 × 5
## COST JNJ PLUG TSLA XOM
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.05 0.033 0.543 0.312 0.0626 Plot: Colum Chart of Component Contribution and Weight
plot_data <- asset_returns_wide_tbl %>%
calculate_component_contribution(w = c(0.2, 0.2, 0.2, 0.2, 0.2)) %>%
# Transform to long form
pivot_longer(cols = everything(), names_to = "Asset", values_to = "Contribution") %>%
# Add weights
add_column(weight = c(0.2, 0.2, 0.2, 0.2, 0.2)) %>%
# 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? PLUG is easily the largest contributor to portfolio volatility with TSLA following closely behind. PLUG contributes roughly 47% of the portfolio while TSLA holds around 26% making for a total of 73% of the overall portfolio. The portfolio is concentrated in two assets which make it a little safer than if PLUG was the main contributor, it would be smart to diversify to assetts more similar to TSLA.