Apply it to your data 12
Which of the assets the largest contributor to the portfolio volatility?
Daniel Lee
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.
symbols <- c("SPY", "EFA", "IJS", "EEM", "AGG")
prices <- tq_get(x = symbols,
get = "stock.prices",
from = "2012-12-31",
to = "2024-12-31")2 Convert prices to returns (monthly)
2 Convert prices to returns
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 Component Contribution 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## AGG EEM EFA IJS SPY
## 2013-01-31 -6.230998e-03 -0.0029351456 0.0366062193 0.052133978 4.992285e-02
## 2013-02-28 5.891154e-03 -0.0231051102 -0.0129695702 0.016174477 1.267837e-02
## 2013-03-28 9.847196e-04 -0.0102355802 0.0129695702 0.040258103 3.726830e-02
## 2013-04-30 9.639292e-03 0.0120853593 0.0489677151 0.001222949 1.902963e-02
## 2013-05-31 -2.021418e-02 -0.0494838714 -0.0306556847 0.041976188 2.333561e-02
## 2013-06-28 -1.577813e-02 -0.0547284119 -0.0271441554 -0.001403001 -1.343431e-02
## 2013-07-31 2.687802e-03 0.0131597360 0.0518600275 0.063541705 5.038582e-02
## 2013-08-30 -8.297879e-03 -0.0257053847 -0.0197461523 -0.034744044 -3.045122e-02
## 2013-09-30 1.114374e-02 0.0695886461 0.0753386042 0.063874089 3.115592e-02
## 2013-10-31 8.291704e-03 0.0408613580 0.0320815724 0.034233640 4.526683e-02
## 2013-11-29 -2.509229e-03 -0.0025943101 0.0054495182 0.041661257 2.920648e-02
## 2013-12-31 -5.583236e-03 -0.0040742400 0.0215280909 0.012892051 2.559647e-02
## 2014-01-31 1.529198e-02 -0.0903224834 -0.0534134413 -0.035775064 -3.588445e-02
## 2014-02-28 3.756455e-03 0.0332206084 0.0595053354 0.045257347 4.451034e-02
## 2014-03-31 -1.481362e-03 0.0380215764 -0.0046027323 0.013315232 8.260901e-03
## 2014-04-30 8.183162e-03 0.0077727927 0.0165294236 -0.023184110 6.927754e-03
## 2014-05-30 1.172146e-02 0.0290910768 0.0158285994 0.006205449 2.294126e-02
## 2014-06-30 -5.761243e-04 0.0237341703 0.0091652618 0.037718726 2.043464e-02
## 2014-07-31 -2.511491e-03 0.0135554634 -0.0263798920 -0.052009490 -1.352860e-02
## 2014-08-29 1.143034e-02 0.0279049320 0.0018005554 0.043657701 3.870467e-02
## 2014-09-30 -6.167259e-03 -0.0808569866 -0.0395986214 -0.061260146 -1.389237e-02
## 2014-10-31 1.058436e-02 0.0140964557 -0.0026546884 0.068874473 2.327771e-02
## 2014-11-28 6.549175e-03 -0.0155413256 0.0006254113 0.004773887 2.710141e-02
## 2014-12-31 1.474693e-03 -0.0404419346 -0.0407468111 0.025295829 -2.539647e-03
## 2015-01-30 2.031536e-02 -0.0068960644 0.0062264509 -0.054628281 -3.007719e-02
## 2015-02-27 -8.987842e-03 0.0431360909 0.0614505880 0.056914695 5.468179e-02
## 2015-03-31 3.739801e-03 -0.0150863045 -0.0143887132 0.010156769 -1.583015e-02
## 2015-04-30 -3.232960e-03 0.0662814793 0.0358165791 -0.018417951 9.786049e-03
## 2015-05-29 -4.383719e-03 -0.0419110434 0.0019526336 0.007509785 1.277407e-02
## 2015-06-30 -1.082504e-02 -0.0297462277 -0.0316788424 0.004171512 -2.052141e-02
## 2015-07-31 8.584404e-03 -0.0651783188 0.0201144955 -0.027375424 2.233802e-02
## 2015-08-31 -3.363876e-03 -0.0925123205 -0.0771524198 -0.047268269 -6.288681e-02
## 2015-09-30 8.081346e-03 -0.0318251723 -0.0451948457 -0.038464666 -2.584726e-02
## 2015-10-30 6.852697e-04 0.0618084883 0.0640259062 0.063589355 8.163527e-02
## 2015-11-30 -3.897800e-03 -0.0255605417 -0.0075558957 0.024415433 3.648508e-03
## 2015-12-31 -1.918716e-03 -0.0389470640 -0.0235950520 -0.052157170 -1.743359e-02
## 2016-01-29 1.232929e-02 -0.0516365427 -0.0567578828 -0.060306734 -5.106850e-02
## 2016-02-29 8.831409e-03 -0.0082117681 -0.0339138341 0.020605215 -8.266144e-04
## 2016-03-31 8.708870e-03 0.1218790281 0.0637456680 0.089910306 6.510026e-02
## 2016-04-29 2.546476e-03 0.0040794458 0.0219751047 0.021044322 3.933602e-03
## 2016-05-31 1.356884e-04 -0.0376285243 -0.0008559985 0.004397027 1.686850e-02
## 2016-06-30 1.916680e-02 0.0445820084 -0.0244915251 0.008292474 3.469675e-03
## 2016-07-29 5.429594e-03 0.0524423019 0.0390004091 0.049348420 3.582200e-02
## 2016-08-31 -2.156091e-03 0.0087986623 0.0053266739 0.011260773 1.196974e-03
## 2016-09-30 5.156932e-04 0.0248727288 0.0132791624 0.008614736 5.778028e-05
## 2016-10-31 -8.205543e-03 -0.0083120768 -0.0224037498 -0.038134962 -1.748931e-02
## 2016-11-30 -2.598927e-02 -0.0451619493 -0.0179745747 0.125246548 3.617633e-02
## 2016-12-30 2.538237e-03 -0.0025300492 0.0267029049 0.031491805 2.006937e-02
## 2017-01-31 2.126037e-03 0.0644317341 0.0323818322 -0.012144084 1.773625e-02
## 2017-02-28 6.437781e-03 0.0172577120 0.0118365958 0.013428836 3.853933e-02
## 2017-03-31 -5.529494e-04 0.0361890901 0.0318056710 -0.006532682 1.249270e-03
## 2017-04-28 9.029524e-03 0.0168665207 0.0239522525 0.005107889 9.876859e-03
## 2017-05-31 6.847093e-03 0.0280595578 0.0348101442 -0.022863116 1.401442e-02
## 2017-06-30 -1.820372e-04 0.0092238832 0.0029558521 0.029151956 6.354722e-03
## 2017-07-31 3.333702e-03 0.0565945288 0.0261880279 0.007481651 2.034580e-02
## 2017-08-31 9.369593e-03 0.0232435095 -0.0004482150 -0.027564707 2.913287e-03
## 2017-09-29 -5.732264e-03 -0.0004459528 0.0233426686 0.082321663 1.994921e-02
## 2017-10-31 9.775654e-04 0.0322786957 0.0166535135 0.005916098 2.329068e-02
## 2017-11-30 -1.483798e-03 -0.0038971170 0.0068703072 0.036913480 3.010815e-02
## 2017-12-29 4.740250e-03 0.0369253201 0.0133982058 -0.003731527 1.205488e-02
## 2018-01-31 -1.131416e-02 0.0797160686 0.0489863233 0.013195887 5.482864e-02
## 2018-02-28 -1.013849e-02 -0.0607960007 -0.0495554117 -0.041260945 -3.703825e-02
## 2018-03-29 6.688896e-03 0.0053999327 -0.0084314301 0.013535042 -2.779341e-02
## 2018-04-30 -9.454674e-03 -0.0285735948 0.0150977345 0.017481022 5.155436e-03
## 2018-05-31 6.596403e-03 -0.0265642414 -0.0191241849 0.057038837 2.401823e-02
## 2018-06-29 1.025591e-03 -0.0465225383 -0.0159681228 0.007965070 5.734314e-03
## 2018-07-31 -2.918947e-04 0.0347011860 0.0281210571 0.025817158 3.637692e-02
## 2018-08-31 5.664184e-03 -0.0384007139 -0.0226112036 0.030912592 3.142050e-02
## 2018-09-28 -6.216974e-03 -0.0058079021 0.0096060840 -0.031022083 5.928153e-03
## 2018-10-31 -6.460989e-03 -0.0916821695 -0.0848343016 -0.104674930 -7.160814e-02
## 2018-11-30 7.685655e-03 0.0478656579 0.0049511788 0.005704097 1.837956e-02
## 2018-12-31 1.961809e-02 -0.0355119927 -0.0549818372 -0.132288322 -9.216852e-02
## 2019-01-31 9.067767e-03 0.0984238762 0.0642407077 0.115614962 7.702186e-02
## 2019-02-28 -1.138910e-03 -0.0154317205 0.0250505841 0.040516451 3.190121e-02
## 2019-03-29 2.100423e-02 0.0112467903 0.0091379600 -0.039919733 1.793905e-02
## 2019-04-30 -2.005543e-03 0.0232593173 0.0288730112 0.042268388 4.003977e-02
## 2019-05-31 1.892793e-02 -0.0761234459 -0.0516401691 -0.105136606 -6.589513e-02
## 2019-06-28 1.092624e-02 0.0602266274 0.0574349472 0.073797669 6.727197e-02
## 2019-07-31 1.816181e-03 -0.0269263853 -0.0196657269 0.012902297 1.500602e-02
## 2019-08-30 2.744791e-02 -0.0385601615 -0.0194270500 -0.052780475 -1.688502e-02
## 2019-09-30 -6.140045e-03 0.0167779440 0.0311502929 0.054209481 1.927098e-02
## 2019-10-31 2.078808e-03 0.0409881630 0.0333288569 0.019842926 2.186398e-02
## 2019-11-29 -3.451026e-04 -0.0009397023 0.0112094909 0.026728301 3.555852e-02
## 2019-12-31 -4.682642e-04 0.0744607861 0.0295142535 0.027995203 2.864140e-02
## 2020-01-31 2.008713e-02 -0.0634844295 -0.0286319795 -0.065135508 -4.039740e-04
## 2020-02-28 1.572293e-02 -0.0384894538 -0.0808332274 -0.107014214 -8.247527e-02
## 2020-03-31 -5.282343e-03 -0.1716190194 -0.1520643188 -0.297850039 -1.333842e-01
## 2020-04-30 1.704448e-02 0.0709637977 0.0565451265 0.130048150 1.195446e-01
## 2020-05-29 6.703300e-03 0.0293151157 0.0528477015 0.025159619 4.654515e-02
## 2020-06-30 6.569591e-03 0.0639594340 0.0345095761 0.035215346 1.757893e-02
## 2020-07-31 1.324359e-02 0.0792922441 0.0192001108 0.025427814 5.722311e-02
## 2020-08-31 -8.269947e-03 0.0284658570 0.0461390359 0.049457108 6.746883e-02
## 2020-09-30 -9.740481e-04 -0.0101546118 -0.0206802686 -0.053334640 -3.816258e-02
## 2020-10-30 -5.598560e-03 0.0139640640 -0.0361523173 0.035941739 -2.524972e-02
## 2020-11-30 1.199567e-02 0.0860977364 0.1333888185 0.175732806 1.032575e-01
## 2020-12-31 8.565544e-04 0.0688678335 0.0489367934 0.072283948 3.637857e-02
## 2021-01-29 -7.473243e-03 0.0312465370 -0.0078431718 0.060603054 -1.024269e-02
## 2021-02-26 -1.529780e-02 0.0078475008 0.0221321471 0.102555285 2.742590e-02
## 2021-03-31 -1.152950e-02 -0.0072849181 0.0248210432 0.054641596 4.439889e-02
## 2021-04-30 7.293286e-03 0.0119271346 0.0290968037 0.016734426 5.155851e-02
## 2021-05-28 2.036374e-03 0.0163529911 0.0342301433 0.038210221 6.544362e-03
## 2021-06-30 8.244842e-03 0.0094591693 -0.0108388345 -0.007172743 2.217954e-02
## 2021-07-30 1.110409e-02 -0.0665349405 0.0077036522 -0.044611422 2.411947e-02
## 2021-08-31 -2.005483e-03 0.0155756623 0.0143635657 0.018084019 2.932554e-02
## 2021-09-30 -9.214583e-03 -0.0395030932 -0.0331577540 -0.014646663 -4.772634e-02
## 2021-10-29 -8.708432e-05 0.0106615322 0.0312958948 0.028206867 6.781130e-02
## 2021-11-30 2.704470e-03 -0.0417061648 -0.0464075548 -0.026519526 -8.067350e-03
## 2021-12-31 -3.593603e-03 0.0149583224 0.0429726115 0.040315701 4.521030e-02
## 2022-01-31 -2.018825e-02 -0.0002047545 -0.0370267653 -0.045021818 -5.418308e-02
## 2022-02-28 -1.152265e-02 -0.0441632762 -0.0348936815 0.022568474 -2.996136e-02
## 2022-03-31 -2.852506e-02 -0.0343962634 0.0051764963 0.004963339 3.690119e-02
## 2022-04-29 -3.880181e-02 -0.0633134901 -0.0697695730 -0.065900394 -9.186213e-02
## 2022-05-31 7.576793e-03 0.0061161041 0.0197624897 0.022894673 2.254569e-03
## 2022-06-30 -1.565185e-02 -0.0529542660 -0.0917494794 -0.093939189 -8.605932e-02
## 2022-07-29 2.506386e-02 -0.0034973025 0.0503967729 0.081812576 8.809091e-02
## 2022-08-31 -3.089481e-02 -0.0133520631 -0.0631193189 -0.042496820 -4.165785e-02
## 2022-09-30 -4.233392e-02 -0.1226133888 -0.0967538044 -0.110038647 -9.700210e-02
## 2022-10-31 -1.286732e-02 -0.0199805048 0.0572476645 0.134501412 7.814146e-02
## 2022-11-30 3.737729e-02 0.1448737381 0.1237041250 0.037355273 5.410122e-02
## 2022-12-30 -8.742996e-03 -0.0267289948 -0.0182952543 -0.067057685 -5.935537e-02
## 2023-01-31 3.276003e-02 0.0873631512 0.0862113097 0.112867138 6.098916e-02
## 2023-02-28 -2.701847e-02 -0.0786936984 -0.0312304105 -0.017467379 -2.546413e-02
## 2023-03-31 2.606940e-02 0.0316668558 0.0308108962 -0.067070023 3.640678e-02
## 2023-04-28 5.738653e-03 -0.0083978748 0.0289396471 -0.024225262 1.584888e-02
## 2023-05-31 -1.150899e-02 -0.0243159259 -0.0408956444 -0.037247980 4.605404e-03
## 2023-06-30 -3.689094e-03 0.0430486360 0.0437302568 0.081591691 6.278691e-02
## 2023-07-31 -1.535254e-04 0.0586599793 0.0266754783 0.058513067 3.220894e-02
## 2023-08-31 -6.333006e-03 -0.0685673490 -0.0401450615 -0.051387245 -1.638546e-02
## 2023-09-29 -2.623729e-02 -0.0316416702 -0.0371704416 -0.065000099 -4.859644e-02
## 2023-10-31 -1.583114e-02 -0.0334927947 -0.0294485330 -0.065659749 -2.194771e-02
## 2023-11-30 4.490579e-02 0.0750418345 0.0789845517 0.086841606 8.740968e-02
## 2023-12-29 3.630912e-02 0.0350323346 0.0521273007 0.125245678 4.464393e-02
## 2024-01-31 -1.512499e-03 -0.0463187983 -0.0045224161 -0.056069094 1.580103e-02
## 2024-02-29 -1.485561e-02 0.0408325878 0.0294254056 0.023427960 5.087057e-02
## 2024-03-28 8.984301e-03 0.0268919829 0.0332281663 0.033267598 3.217872e-02
## 2024-04-30 -2.509447e-02 -0.0021933378 -0.0329693590 -0.067214981 -4.115499e-02
## 2024-05-31 1.655772e-02 0.0193289287 0.0493632161 0.044578498 4.934208e-02
## 2024-06-28 8.815900e-03 0.0258433718 -0.0184362782 -0.027426544 3.467199e-02
## 2024-07-31 2.390370e-02 0.0084171962 0.0255857949 0.111469097 1.203652e-02
## 2024-08-30 1.450878e-02 0.0097313307 0.0320830856 -0.013890389 2.309676e-02
## 2024-09-30 1.324018e-02 0.0558253157 0.0078026286 0.009172770 2.078700e-02
## 2024-10-31 -2.550989e-02 -0.0312283091 -0.0541734661 -0.017711253 -8.963702e-03
## 2024-11-29 1.104842e-02 -0.0271366314 -0.0031607404 0.102273552 5.792310e-02
## 2024-12-30 -1.584133e-02 -0.0137702427 -0.0299455782 -0.074997011 -2.070951e-02# Covariance of asset returns
covariance_matrix <- cov(asset_returns_wide_tbl)
covariance_matrix## AGG EEM EFA IJS SPY
## AGG 0.0001942940 0.0002651849 0.0002509926 0.0001813495 0.000209118
## EEM 0.0002651849 0.0022992801 0.0016457968 0.0017858382 0.001357489
## EFA 0.0002509926 0.0016457968 0.0017941290 0.0019197080 0.001515584
## IJS 0.0001813495 0.0017858382 0.0019197080 0.0036919743 0.002065790
## SPY 0.0002091180 0.0013574889 0.0015155836 0.0020657900 0.001730244# 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,] 0.03445726# 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## AGG EEM EFA IJS SPY
## [1,] 0.001612505 0.01061599 0.007943308 0.01056795 0.003717506rowSums(component_contribution)## [1] 0.03445726# Component contribution in percentage
component_percentages <- (component_contribution / sd_portfolio[1,1]) %>%
round(3) %>%
as_tibble()
component_percentages## # A tibble: 1 × 5
## AGG EEM EFA IJS SPY
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.047 0.308 0.231 0.307 0.108component_percentages %>%
as_tibble() %>%
gather(key = "asset", value = "contribution")## # A tibble: 5 × 2
## asset contribution
## <chr> <dbl>
## 1 AGG 0.047
## 2 EEM 0.308
## 3 EFA 0.231
## 4 IJS 0.307
## 5 SPY 0.1084 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")
# Custom function
calculate_component_contribution <- function(asset_returns_wide_tbl, w) {
# Covariance of asset returns
covariance_matrix <- cov(asset_returns_wide_tbl)
# Standard deviation of portfolio
sd_portfolio <- sqrt(t(w) %*% covariance_matrix %*% w)
# Component contribution
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(0.25,0.25,0.2,0.2,0.1))## # A tibble: 1 × 5
## AGG EEM EFA IJS SPY
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.047 0.308 0.231 0.307 0.1085 Visualizing Component Contribution
# Figure 10.1 Contribution to Standard Deviation ----
asset_returns_wide_tbl %>%
calculate_component_contribution(w = c(0.25,0.25,0.2,0.2,0.1)) %>%
gather(key = "asset", value = "contribution") %>%
ggplot(aes(asset, contribution)) +
geom_col(fill = "cornflowerblue") +
theme(plot.title = element_text(hjust = 0.5)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
labs(title = "Percent Contribution to Portfolio Standard Deviation",
y = "Percent Contribution to Risk",
x = NULL)
# Figure 10.2 Weight versus Contribution ----
asset_returns_wide_tbl %>%
calculate_component_contribution(w = c(0.25,0.25,0.2,0.2,0.1)) %>%
gather(key = "asset", value = "contribution") %>%
add_column(weights = c(0.25,0.25,0.2,0.2,0.1)) %>%
pivot_longer(cols = c(contribution, weights), names_to = "type", values_to = "value") %>%
ggplot(aes(asset, value, fill = type)) +
geom_col(position = "dodge") +
theme(plot.title = element_text(hjust = 0.5)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
theme_tq() +
scale_fill_tq() +
labs(title = "Percent Contribution to Volatility",
y = "percent",
x = "asset") 
6 Rolling Component Contribution
calculate_comp_contrib_by_window <- function(asset_returns_wide_tbl,
start = 1,
window = 24,
weights) {
# 1 Define start date
start_date <- rownames(asset_returns_wide_tbl)[start]
# 2 Define end date
end_date <- rownames(asset_returns_wide_tbl)[start + window]
# 3 Subset df
df_subset <- asset_returns_wide_tbl %>%
rownames_to_column(var = "date") %>%
filter(date >= start_date & date < end_date) %>%
column_to_rownames(var = "date")
# 4 Calculate component contribution
component_percentages <-df_subset %>%
calculate_component_contribution(w = weights)
# 5 Add end date to df
component_percentages %>%
mutate(date = ymd(end_date)) %>%
select(date, everything())
}
# Check the custom function
asset_returns_wide_tbl %>% calculate_comp_contrib_by_window(start = 1, window = 24,
w = c(0.25,0.25,0.2,0.2,0.1))## # A tibble: 1 × 6
## date AGG EEM EFA IJS SPY
## <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015-01-30 0.039 0.372 0.256 0.245 0.088asset_returns_wide_tbl %>% calculate_comp_contrib_by_window(start = 2, window = 24,
w = c(0.25,0.25,0.2,0.2,0.1))## # A tibble: 1 × 6
## date AGG EEM EFA IJS SPY
## <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015-02-27 0.036 0.374 0.249 0.252 0.089dump(list = c("calculate_component_contribution",
"calculate_comp_contrib_by_window"),
file = "../00_scripts/calculate_comp_contrib_to_portfolio_volatility.R")# Iterate the custom function
w <- c(0.25,0.25,0.2,0.2,0.1)
window <- 24
rolling_comp_contrib_tbl <- 1:(nrow(asset_returns_wide_tbl) - window) %>%
map_df(.x = ., .f = ~calculate_comp_contrib_by_window(asset_returns_wide_tbl,
start = .x,
weights = w,
window = window))
rolling_comp_contrib_tbl## # A tibble: 120 × 6
## date AGG EEM EFA IJS SPY
## <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015-01-30 0.039 0.372 0.256 0.245 0.088
## 2 2015-02-27 0.036 0.374 0.249 0.252 0.089
## 3 2015-03-31 0.027 0.37 0.255 0.255 0.092
## 4 2015-04-30 0.027 0.372 0.256 0.252 0.093
## 5 2015-05-29 0.024 0.385 0.254 0.246 0.092
## 6 2015-06-30 0.018 0.383 0.248 0.257 0.094
## 7 2015-07-31 0.014 0.375 0.253 0.261 0.097
## 8 2015-08-31 0.013 0.404 0.237 0.257 0.09
## 9 2015-09-30 0.012 0.407 0.248 0.238 0.094
## 10 2015-10-30 0.003 0.405 0.244 0.243 0.105
## # ℹ 110 more rows# Figure 10.3 Component Contribution ggplot ----
rolling_comp_contrib_tbl %>%
# Transform data to long form
pivot_longer(cols = -date, names_to = "asset", values_to = "contribution") %>%
# Plot
ggplot(aes(date, contribution, color = asset)) +
geom_line() +
scale_x_date(breaks = scales::pretty_breaks(n = 7)) +
scale_y_continuous(labels = scales::percent_format()) +
annotate(geom = "text",
x = as.Date("2016-07-01"),
y = 0.03,
color = "red", size = 5,
label = str_glue("AGG dips below zero sometimes, indicating
it reduces the portfolio volatility."))
# Figure 10.4 Stacked Component Contribution ggplot ----
rolling_comp_contrib_tbl %>%
# Transform data to long form
pivot_longer(cols = -date, names_to = "asset", values_to = "contribution") %>%
# Plot
ggplot(aes(date, contribution, fill = asset)) +
geom_area() +
scale_x_date(breaks = scales::pretty_breaks(n = 7)) +
scale_y_continuous(labels = scales::percent_format()) +
annotate(geom = "text",
x = as.Date("2016-07-01"),
y = 0.08,
color = "red", size = 5,
label = str_glue("AGG dips below zero sometimes, indicating
it reduces the portfolio volatility."))
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? EEM and IJS both equally contribute the most to the portfolios volatility. I don’t think my portfolio is overly concentrated in one asset, as it is pretty evenly distributed.