Apply it to your data 12
Which of the assets the largest contributor to the portfolio volatility?
Jack Tortolani
2022-12-04
# 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("XOM", "QQQ", "SPY", "TSLA")
prices <- tq_get(x = symbols,
get = "stock.prices",
from = "2012-12-31")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## QQQ SPY TSLA XOM
## 2013-01-31 0.026365142 4.992222e-02 0.102078114 3.875395e-02
## 2013-02-28 0.003433704 1.267923e-02 -0.074128640 1.689077e-03
## 2013-03-28 0.029799114 3.726754e-02 0.084208138 6.233985e-03
## 2013-04-30 0.025056677 1.903053e-02 0.354111531 -1.250680e-02
## 2013-05-31 0.035149994 2.333515e-02 0.593716684 2.338800e-02
## 2013-06-28 -0.024244510 -1.343399e-02 0.093672163 -1.327425e-03
## 2013-07-31 0.061227254 5.038564e-02 0.223739522 3.694032e-02
## 2013-08-30 -0.003967347 -3.045125e-02 0.229971642 -6.599790e-02
## 2013-09-30 0.047190670 3.115566e-02 0.134706620 -1.293289e-02
## 2013-10-31 0.048379532 4.526690e-02 -0.189806595 4.076621e-02
## 2013-11-29 0.034895845 2.920682e-02 -0.228409405 4.894999e-02
## 2013-12-31 0.028851062 2.559597e-02 0.167108541 7.935114e-02
## 2014-01-31 -0.019400577 -3.588433e-02 0.187261714 -9.357268e-02
## 2014-02-28 0.050241079 4.451029e-02 0.299722785 5.068829e-02
## 2014-03-31 -0.027716593 8.261119e-03 -0.160783242 1.454020e-02
## 2014-04-30 -0.003198968 6.927568e-03 -0.002690122 4.728727e-02
## 2014-05-30 0.043879374 2.294118e-02 -0.000577422 -1.176162e-02
## 2014-06-30 0.030760363 2.043459e-02 0.144457224 1.491203e-03
## 2014-07-31 0.011750493 -1.352864e-02 -0.072372676 -1.743353e-02
## 2014-08-29 0.048880249 3.870474e-02 0.188794007 1.218402e-02
## 2014-09-30 -0.007595039 -1.389210e-02 -0.105566477 -5.592903e-02
## 2014-10-31 0.026076573 2.327769e-02 -0.004046477 2.789017e-02
## 2014-11-28 0.044460402 2.710122e-02 0.011599801 -5.873120e-02
## 2014-12-31 -0.022661092 -2.540014e-03 -0.094774519 2.087660e-02
## 2015-01-30 -0.021043112 -3.007645e-02 -0.088365289 -5.594434e-02
## 2015-02-27 0.069718056 5.468171e-02 -0.001277808 2.022942e-02
## 2015-03-31 -0.023872505 -1.583028e-02 -0.074350051 -4.080312e-02
## 2015-04-30 0.019041069 9.785864e-03 0.180226808 2.750017e-02
## 2015-05-29 0.022235322 1.277448e-02 0.103899574 -1.691337e-02
## 2015-06-30 -0.025154596 -2.052161e-02 0.067300935 -2.375406e-02
## 2015-07-31 0.044569331 2.233797e-02 -0.007896616 -4.914485e-02
## 2015-08-31 -0.070685055 -6.288659e-02 -0.066366250 -4.210637e-02
## 2015-09-30 -0.022320236 -2.584723e-02 -0.002653519 -1.189913e-02
## 2015-10-30 0.107686908 8.163487e-02 -0.182659777 1.069192e-01
## 2015-11-30 0.006069904 3.648448e-03 0.106828586 -4.458780e-03
## 2015-12-31 -0.016076296 -1.743331e-02 0.041471519 -4.649706e-02
## 2016-01-29 -0.071608159 -5.106856e-02 -0.227360626 -1.283761e-03
## 2016-02-29 -0.015777207 -8.266685e-04 0.003810669 3.815157e-02
## 2016-03-31 0.066280132 6.510016e-02 0.179948109 4.202374e-02
## 2016-04-29 -0.032386795 3.933394e-03 0.046721797 5.594812e-02
## 2016-05-31 0.042772388 1.686863e-02 -0.075597968 1.535839e-02
## 2016-06-30 -0.023043626 3.469884e-03 -0.050296440 5.166419e-02
## 2016-07-29 0.069067450 3.582199e-02 0.100785334 -5.245119e-02
## 2016-08-31 0.010445779 1.196897e-03 -0.102058091 -1.206679e-02
## 2016-09-30 0.021894002 5.773221e-05 -0.038366372 1.605124e-03
## 2016-10-31 -0.014679461 -1.748897e-02 -0.031364583 -4.643258e-02
## 2016-11-30 0.004350111 3.617630e-02 -0.043041267 5.547846e-02
## 2016-12-30 0.011258809 2.006862e-02 0.120665178 3.334333e-02
## 2017-01-31 0.050123501 1.773692e-02 0.164624916 -7.318781e-02
## 2017-02-28 0.042820488 3.853911e-02 -0.007730364 -2.201188e-02
## 2017-03-31 0.020065943 1.249160e-03 0.107278727 8.448874e-03
## 2017-04-28 0.026904931 9.877091e-03 0.120916212 -4.399149e-03
## 2017-05-31 0.038232596 1.401430e-02 0.082295892 -4.785570e-03
## 2017-06-30 -0.023451917 6.354618e-03 0.058654468 2.853207e-03
## 2017-07-31 0.039810305 2.034593e-02 -0.111459860 -8.583958e-03
## 2017-08-31 0.020523746 2.913517e-03 0.095543446 -3.781431e-02
## 2017-09-29 -0.002956932 1.994921e-02 -0.042474144 7.140890e-02
## 2017-10-31 0.045034658 2.329054e-02 -0.028457409 1.657368e-02
## 2017-11-30 0.019525581 3.010807e-02 -0.070862541 8.492045e-03
## 2017-12-29 0.006012867 1.205493e-02 0.008061928 4.193504e-03
## 2018-01-31 0.083946553 5.482843e-02 0.129254571 4.282859e-02
## 2018-02-28 -0.013012676 -3.703800e-02 -0.032266877 -1.318704e-01
## 2018-03-29 -0.041643135 -2.779331e-02 -0.253920408 -1.503172e-02
## 2018-04-30 0.005045558 5.154786e-03 0.099254576 4.122397e-02
## 2018-05-31 0.055178688 2.401880e-02 -0.031698139 5.399393e-02
## 2018-06-29 0.011385331 5.734108e-03 0.186043257 1.817478e-02
## 2018-07-31 0.027580174 3.637667e-02 -0.140021491 -1.485644e-02
## 2018-08-31 0.056197616 3.142120e-02 0.011737389 -6.314966e-03
## 2018-09-28 -0.002820477 5.928050e-03 -0.130439038 5.873714e-02
## 2018-10-31 -0.089877858 -7.160829e-02 0.242170576 -6.486771e-02
## 2018-11-30 -0.002653540 1.837915e-02 0.038271580 7.824423e-03
## 2018-12-31 -0.090588975 -9.216814e-02 -0.051761952 -1.534589e-01
## 2019-01-31 0.086276378 7.702157e-02 -0.080628789 7.198978e-02
## 2019-02-28 0.029473505 3.190186e-02 0.041032981 8.658063e-02
## 2019-03-29 0.038494998 1.793839e-02 -0.133656413 2.214942e-02
## 2019-04-30 0.053533776 4.003996e-02 -0.159123803 -6.456375e-03
## 2019-05-31 -0.085832031 -6.589530e-02 -0.253945372 -1.146881e-01
## 2019-06-28 0.073157939 6.727200e-02 0.188012109 7.955318e-02
## 2019-07-31 0.023079596 1.500629e-02 0.078092373 -3.007034e-02
## 2019-08-30 -0.019177936 -1.688479e-02 -0.068516948 -7.001911e-02
## 2019-09-30 0.009136685 1.927089e-02 0.065449565 3.063013e-02
## 2019-10-31 0.042868624 2.186375e-02 0.268061253 -4.400770e-02
## 2019-11-29 0.039887629 3.555857e-02 0.046592176 2.024120e-02
## 2019-12-31 0.038127846 2.864136e-02 0.237359743 2.392982e-02
## 2020-01-31 0.029931882 -4.039131e-04 0.441578342 -1.162797e-01
## 2020-02-28 -0.062486527 -8.247510e-02 0.026424253 -1.743976e-01
## 2020-03-31 -0.075648993 -1.333845e-01 -0.242781458 -3.036194e-01
## 2020-04-30 0.139533628 1.195448e-01 0.400209535 2.020104e-01
## 2020-05-29 0.063921640 4.654470e-02 0.065730500 -2.550278e-03
## 2020-06-30 0.060969701 1.757929e-02 0.257108657 -1.663195e-02
## 2020-07-31 0.070892034 5.722314e-02 0.281420674 -6.084817e-02
## 2020-08-31 0.103861716 6.746870e-02 0.554719320 -3.265860e-02
## 2020-09-30 -0.059522974 -3.816265e-02 -0.149762306 -1.513587e-01
## 2020-10-30 -0.030922178 -2.524971e-02 -0.100371771 -5.109407e-02
## 2020-11-30 0.106391730 1.032575e-01 0.380308519 1.799158e-01
## 2020-12-31 0.047860531 3.637859e-02 0.217730753 7.792216e-02
## 2021-01-29 0.002610451 -1.024278e-02 0.117343701 8.417702e-02
## 2021-02-26 -0.001336194 2.742591e-02 -0.161038203 2.095518e-01
## 2021-03-31 0.017022041 4.439901e-02 -0.011269836 2.649875e-02
## 2021-04-30 0.057417577 5.155812e-02 0.060292565 2.494163e-02
## 2021-05-28 -0.012084823 6.544649e-03 -0.126372343 3.401205e-02
## 2021-06-30 0.060738754 2.217969e-02 0.083547955 7.760166e-02
## 2021-07-30 0.028207919 2.411923e-02 0.010973846 -9.140217e-02
## 2021-08-31 0.041320993 2.932553e-02 0.068224268 -3.941177e-02
## 2021-09-30 -0.058510264 -4.772652e-02 0.052632612 7.591434e-02
## 2021-10-29 0.075701020 6.781166e-02 0.362230202 9.171810e-02
## 2021-11-30 0.019771518 -8.067367e-03 0.027237848 -6.117598e-02
## 2021-12-31 0.011457602 4.521017e-02 -0.079968440 2.230941e-02
## 2022-01-31 -0.091534449 -5.418290e-02 -0.120597475 2.162231e-01
## 2022-02-28 -0.045792429 -2.996135e-02 -0.073397011 4.289716e-02
## 2022-03-31 0.045622263 3.690103e-02 0.213504290 5.180956e-02
## 2022-04-29 -0.146133394 -9.186211e-02 -0.213125289 3.169957e-02
## 2022-05-31 -0.015993260 2.254680e-03 -0.138340062 1.289512e-01
## 2022-06-30 -0.093298697 -8.605956e-02 -0.118657126 -1.141955e-01
## 2022-07-29 0.118242744 8.809096e-02 0.280480149 1.238365e-01
## 2022-08-31 -0.052685826 -4.165765e-02 -0.075250271 -4.251460e-03
## 2022-09-30 -0.111328634 -9.700208e-02 -0.038313992 -9.060318e-02
## 2022-10-31 0.039219217 7.814131e-02 -0.153346760 2.383520e-01
## 2022-11-30 0.053959421 5.410126e-02 -0.155866121 1.278954e-02
## 2022-12-30 -0.094461036 -5.935544e-02 -0.457813194 -9.384627e-03
## 2023-01-31 0.101138009 6.098923e-02 0.340915767 5.047243e-02
## 2023-02-28 -0.003604316 -2.546427e-02 0.171904973 -4.634906e-02
## 2023-03-31 0.090687898 3.640716e-02 0.008471140 -2.277198e-03
## 2023-04-28 0.005066252 1.584862e-02 -0.233183710 7.617707e-02
## 2023-05-31 0.075884418 4.605462e-03 0.216021889 -1.381858e-01
## 2023-06-30 0.061131234 6.278689e-02 0.249689452 4.842652e-02
## 2023-07-31 0.037874673 3.220893e-02 0.021391609 -9.318555e-05
## 2023-08-31 -0.014941116 -1.638553e-02 -0.035588260 4.433509e-02
## 2023-09-29 -0.052134360 -4.859640e-02 -0.030929027 5.587855e-02
## 2023-10-31 -0.020871156 -2.194780e-02 -0.219831983 -1.050960e-01
## 2023-11-30 0.102726511 8.740992e-02 0.178463656 -2.071877e-02
## 2023-12-29 0.054364711 4.464380e-02 0.034390133 -2.723123e-02
## 2024-01-31 0.018028627 1.580099e-02 -0.282704160 2.791230e-02
## 2024-02-29 0.051485088 5.087070e-02 0.075015304 2.574664e-02
## 2024-03-28 0.012669115 3.217856e-02 -0.138383423 1.062786e-01
## 2024-04-30 -0.044723148 -4.115494e-02 0.041724969 1.731309e-02
## 2024-05-31 0.059700139 4.934204e-02 -0.028782134 -4.867061e-04
## 2024-06-28 0.062694255 3.467204e-02 0.105427913 -1.841869e-02
## 2024-07-31 -0.016923448 1.203641e-02 0.159378271 2.969709e-02
## 2024-08-30 0.010978186 2.309676e-02 -0.080549177 2.522510e-03
## 2024-09-30 0.025878211 2.078712e-02 0.200441406 -6.123529e-03
## 2024-10-31 -0.008683899 -8.963637e-03 -0.046070548 -3.760711e-03
## 2024-11-29 0.052125819 5.792306e-02 0.323147326 1.823731e-02
## 2024-12-12 0.032350559 2.949805e-03 0.191712565 -5.345514e-02# Covariance of asset returns
covariance_matrix <- cov(asset_returns_wide_tbl)
covariance_matrix## QQQ SPY TSLA XOM
## QQQ 0.002483360 0.001890446 0.0042708902 0.0011441004
## SPY 0.001890446 0.001723526 0.0028475598 0.0016497565
## TSLA 0.004270890 0.002847560 0.0292059200 0.0008456403
## XOM 0.001144100 0.001649756 0.0008456403 0.0052751114# 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, 0.25)
sd_portfolio <- sqrt(t(w) %*% covariance_matrix %*% w)
sd_portfolio## [,1]
## [1,] 0.06323799# 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## QQQ SPY TSLA XOM
## [1,] 0.00967456 0.008016629 0.03673623 0.008810574rowSums(component_contribution)## [1] 0.06323799# Component contribution in percentage
component_percentages <- (component_contribution / sd_portfolio[1,1]) %>%
round(3) %>%
as_tibble()
component_percentages## # A tibble: 1 × 4
## QQQ SPY TSLA XOM
## <dbl> <dbl> <dbl> <dbl>
## 1 0.153 0.127 0.581 0.139component_percentages %>%
as_tibble() %>%
gather(key = "asset", value = "contribution")## # A tibble: 4 × 2
## asset contribution
## <chr> <dbl>
## 1 QQQ 0.153
## 2 SPY 0.127
## 3 TSLA 0.581
## 4 XOM 0.1396 Plot: Column Chart of Component Contribution and Weight
asset_returns_wide_tbl <- asset_returns_tbl %>%
pivot_wider(names_from = asset, values_from = returns) %>%
column_to_rownames(var = "date")
asset_returns_wide_tbl## QQQ SPY TSLA XOM
## 2013-01-31 0.026365142 4.992222e-02 0.102078114 3.875395e-02
## 2013-02-28 0.003433704 1.267923e-02 -0.074128640 1.689077e-03
## 2013-03-28 0.029799114 3.726754e-02 0.084208138 6.233985e-03
## 2013-04-30 0.025056677 1.903053e-02 0.354111531 -1.250680e-02
## 2013-05-31 0.035149994 2.333515e-02 0.593716684 2.338800e-02
## 2013-06-28 -0.024244510 -1.343399e-02 0.093672163 -1.327425e-03
## 2013-07-31 0.061227254 5.038564e-02 0.223739522 3.694032e-02
## 2013-08-30 -0.003967347 -3.045125e-02 0.229971642 -6.599790e-02
## 2013-09-30 0.047190670 3.115566e-02 0.134706620 -1.293289e-02
## 2013-10-31 0.048379532 4.526690e-02 -0.189806595 4.076621e-02
## 2013-11-29 0.034895845 2.920682e-02 -0.228409405 4.894999e-02
## 2013-12-31 0.028851062 2.559597e-02 0.167108541 7.935114e-02
## 2014-01-31 -0.019400577 -3.588433e-02 0.187261714 -9.357268e-02
## 2014-02-28 0.050241079 4.451029e-02 0.299722785 5.068829e-02
## 2014-03-31 -0.027716593 8.261119e-03 -0.160783242 1.454020e-02
## 2014-04-30 -0.003198968 6.927568e-03 -0.002690122 4.728727e-02
## 2014-05-30 0.043879374 2.294118e-02 -0.000577422 -1.176162e-02
## 2014-06-30 0.030760363 2.043459e-02 0.144457224 1.491203e-03
## 2014-07-31 0.011750493 -1.352864e-02 -0.072372676 -1.743353e-02
## 2014-08-29 0.048880249 3.870474e-02 0.188794007 1.218402e-02
## 2014-09-30 -0.007595039 -1.389210e-02 -0.105566477 -5.592903e-02
## 2014-10-31 0.026076573 2.327769e-02 -0.004046477 2.789017e-02
## 2014-11-28 0.044460402 2.710122e-02 0.011599801 -5.873120e-02
## 2014-12-31 -0.022661092 -2.540014e-03 -0.094774519 2.087660e-02
## 2015-01-30 -0.021043112 -3.007645e-02 -0.088365289 -5.594434e-02
## 2015-02-27 0.069718056 5.468171e-02 -0.001277808 2.022942e-02
## 2015-03-31 -0.023872505 -1.583028e-02 -0.074350051 -4.080312e-02
## 2015-04-30 0.019041069 9.785864e-03 0.180226808 2.750017e-02
## 2015-05-29 0.022235322 1.277448e-02 0.103899574 -1.691337e-02
## 2015-06-30 -0.025154596 -2.052161e-02 0.067300935 -2.375406e-02
## 2015-07-31 0.044569331 2.233797e-02 -0.007896616 -4.914485e-02
## 2015-08-31 -0.070685055 -6.288659e-02 -0.066366250 -4.210637e-02
## 2015-09-30 -0.022320236 -2.584723e-02 -0.002653519 -1.189913e-02
## 2015-10-30 0.107686908 8.163487e-02 -0.182659777 1.069192e-01
## 2015-11-30 0.006069904 3.648448e-03 0.106828586 -4.458780e-03
## 2015-12-31 -0.016076296 -1.743331e-02 0.041471519 -4.649706e-02
## 2016-01-29 -0.071608159 -5.106856e-02 -0.227360626 -1.283761e-03
## 2016-02-29 -0.015777207 -8.266685e-04 0.003810669 3.815157e-02
## 2016-03-31 0.066280132 6.510016e-02 0.179948109 4.202374e-02
## 2016-04-29 -0.032386795 3.933394e-03 0.046721797 5.594812e-02
## 2016-05-31 0.042772388 1.686863e-02 -0.075597968 1.535839e-02
## 2016-06-30 -0.023043626 3.469884e-03 -0.050296440 5.166419e-02
## 2016-07-29 0.069067450 3.582199e-02 0.100785334 -5.245119e-02
## 2016-08-31 0.010445779 1.196897e-03 -0.102058091 -1.206679e-02
## 2016-09-30 0.021894002 5.773221e-05 -0.038366372 1.605124e-03
## 2016-10-31 -0.014679461 -1.748897e-02 -0.031364583 -4.643258e-02
## 2016-11-30 0.004350111 3.617630e-02 -0.043041267 5.547846e-02
## 2016-12-30 0.011258809 2.006862e-02 0.120665178 3.334333e-02
## 2017-01-31 0.050123501 1.773692e-02 0.164624916 -7.318781e-02
## 2017-02-28 0.042820488 3.853911e-02 -0.007730364 -2.201188e-02
## 2017-03-31 0.020065943 1.249160e-03 0.107278727 8.448874e-03
## 2017-04-28 0.026904931 9.877091e-03 0.120916212 -4.399149e-03
## 2017-05-31 0.038232596 1.401430e-02 0.082295892 -4.785570e-03
## 2017-06-30 -0.023451917 6.354618e-03 0.058654468 2.853207e-03
## 2017-07-31 0.039810305 2.034593e-02 -0.111459860 -8.583958e-03
## 2017-08-31 0.020523746 2.913517e-03 0.095543446 -3.781431e-02
## 2017-09-29 -0.002956932 1.994921e-02 -0.042474144 7.140890e-02
## 2017-10-31 0.045034658 2.329054e-02 -0.028457409 1.657368e-02
## 2017-11-30 0.019525581 3.010807e-02 -0.070862541 8.492045e-03
## 2017-12-29 0.006012867 1.205493e-02 0.008061928 4.193504e-03
## 2018-01-31 0.083946553 5.482843e-02 0.129254571 4.282859e-02
## 2018-02-28 -0.013012676 -3.703800e-02 -0.032266877 -1.318704e-01
## 2018-03-29 -0.041643135 -2.779331e-02 -0.253920408 -1.503172e-02
## 2018-04-30 0.005045558 5.154786e-03 0.099254576 4.122397e-02
## 2018-05-31 0.055178688 2.401880e-02 -0.031698139 5.399393e-02
## 2018-06-29 0.011385331 5.734108e-03 0.186043257 1.817478e-02
## 2018-07-31 0.027580174 3.637667e-02 -0.140021491 -1.485644e-02
## 2018-08-31 0.056197616 3.142120e-02 0.011737389 -6.314966e-03
## 2018-09-28 -0.002820477 5.928050e-03 -0.130439038 5.873714e-02
## 2018-10-31 -0.089877858 -7.160829e-02 0.242170576 -6.486771e-02
## 2018-11-30 -0.002653540 1.837915e-02 0.038271580 7.824423e-03
## 2018-12-31 -0.090588975 -9.216814e-02 -0.051761952 -1.534589e-01
## 2019-01-31 0.086276378 7.702157e-02 -0.080628789 7.198978e-02
## 2019-02-28 0.029473505 3.190186e-02 0.041032981 8.658063e-02
## 2019-03-29 0.038494998 1.793839e-02 -0.133656413 2.214942e-02
## 2019-04-30 0.053533776 4.003996e-02 -0.159123803 -6.456375e-03
## 2019-05-31 -0.085832031 -6.589530e-02 -0.253945372 -1.146881e-01
## 2019-06-28 0.073157939 6.727200e-02 0.188012109 7.955318e-02
## 2019-07-31 0.023079596 1.500629e-02 0.078092373 -3.007034e-02
## 2019-08-30 -0.019177936 -1.688479e-02 -0.068516948 -7.001911e-02
## 2019-09-30 0.009136685 1.927089e-02 0.065449565 3.063013e-02
## 2019-10-31 0.042868624 2.186375e-02 0.268061253 -4.400770e-02
## 2019-11-29 0.039887629 3.555857e-02 0.046592176 2.024120e-02
## 2019-12-31 0.038127846 2.864136e-02 0.237359743 2.392982e-02
## 2020-01-31 0.029931882 -4.039131e-04 0.441578342 -1.162797e-01
## 2020-02-28 -0.062486527 -8.247510e-02 0.026424253 -1.743976e-01
## 2020-03-31 -0.075648993 -1.333845e-01 -0.242781458 -3.036194e-01
## 2020-04-30 0.139533628 1.195448e-01 0.400209535 2.020104e-01
## 2020-05-29 0.063921640 4.654470e-02 0.065730500 -2.550278e-03
## 2020-06-30 0.060969701 1.757929e-02 0.257108657 -1.663195e-02
## 2020-07-31 0.070892034 5.722314e-02 0.281420674 -6.084817e-02
## 2020-08-31 0.103861716 6.746870e-02 0.554719320 -3.265860e-02
## 2020-09-30 -0.059522974 -3.816265e-02 -0.149762306 -1.513587e-01
## 2020-10-30 -0.030922178 -2.524971e-02 -0.100371771 -5.109407e-02
## 2020-11-30 0.106391730 1.032575e-01 0.380308519 1.799158e-01
## 2020-12-31 0.047860531 3.637859e-02 0.217730753 7.792216e-02
## 2021-01-29 0.002610451 -1.024278e-02 0.117343701 8.417702e-02
## 2021-02-26 -0.001336194 2.742591e-02 -0.161038203 2.095518e-01
## 2021-03-31 0.017022041 4.439901e-02 -0.011269836 2.649875e-02
## 2021-04-30 0.057417577 5.155812e-02 0.060292565 2.494163e-02
## 2021-05-28 -0.012084823 6.544649e-03 -0.126372343 3.401205e-02
## 2021-06-30 0.060738754 2.217969e-02 0.083547955 7.760166e-02
## 2021-07-30 0.028207919 2.411923e-02 0.010973846 -9.140217e-02
## 2021-08-31 0.041320993 2.932553e-02 0.068224268 -3.941177e-02
## 2021-09-30 -0.058510264 -4.772652e-02 0.052632612 7.591434e-02
## 2021-10-29 0.075701020 6.781166e-02 0.362230202 9.171810e-02
## 2021-11-30 0.019771518 -8.067367e-03 0.027237848 -6.117598e-02
## 2021-12-31 0.011457602 4.521017e-02 -0.079968440 2.230941e-02
## 2022-01-31 -0.091534449 -5.418290e-02 -0.120597475 2.162231e-01
## 2022-02-28 -0.045792429 -2.996135e-02 -0.073397011 4.289716e-02
## 2022-03-31 0.045622263 3.690103e-02 0.213504290 5.180956e-02
## 2022-04-29 -0.146133394 -9.186211e-02 -0.213125289 3.169957e-02
## 2022-05-31 -0.015993260 2.254680e-03 -0.138340062 1.289512e-01
## 2022-06-30 -0.093298697 -8.605956e-02 -0.118657126 -1.141955e-01
## 2022-07-29 0.118242744 8.809096e-02 0.280480149 1.238365e-01
## 2022-08-31 -0.052685826 -4.165765e-02 -0.075250271 -4.251460e-03
## 2022-09-30 -0.111328634 -9.700208e-02 -0.038313992 -9.060318e-02
## 2022-10-31 0.039219217 7.814131e-02 -0.153346760 2.383520e-01
## 2022-11-30 0.053959421 5.410126e-02 -0.155866121 1.278954e-02
## 2022-12-30 -0.094461036 -5.935544e-02 -0.457813194 -9.384627e-03
## 2023-01-31 0.101138009 6.098923e-02 0.340915767 5.047243e-02
## 2023-02-28 -0.003604316 -2.546427e-02 0.171904973 -4.634906e-02
## 2023-03-31 0.090687898 3.640716e-02 0.008471140 -2.277198e-03
## 2023-04-28 0.005066252 1.584862e-02 -0.233183710 7.617707e-02
## 2023-05-31 0.075884418 4.605462e-03 0.216021889 -1.381858e-01
## 2023-06-30 0.061131234 6.278689e-02 0.249689452 4.842652e-02
## 2023-07-31 0.037874673 3.220893e-02 0.021391609 -9.318555e-05
## 2023-08-31 -0.014941116 -1.638553e-02 -0.035588260 4.433509e-02
## 2023-09-29 -0.052134360 -4.859640e-02 -0.030929027 5.587855e-02
## 2023-10-31 -0.020871156 -2.194780e-02 -0.219831983 -1.050960e-01
## 2023-11-30 0.102726511 8.740992e-02 0.178463656 -2.071877e-02
## 2023-12-29 0.054364711 4.464380e-02 0.034390133 -2.723123e-02
## 2024-01-31 0.018028627 1.580099e-02 -0.282704160 2.791230e-02
## 2024-02-29 0.051485088 5.087070e-02 0.075015304 2.574664e-02
## 2024-03-28 0.012669115 3.217856e-02 -0.138383423 1.062786e-01
## 2024-04-30 -0.044723148 -4.115494e-02 0.041724969 1.731309e-02
## 2024-05-31 0.059700139 4.934204e-02 -0.028782134 -4.867061e-04
## 2024-06-28 0.062694255 3.467204e-02 0.105427913 -1.841869e-02
## 2024-07-31 -0.016923448 1.203641e-02 0.159378271 2.969709e-02
## 2024-08-30 0.010978186 2.309676e-02 -0.080549177 2.522510e-03
## 2024-09-30 0.025878211 2.078712e-02 0.200441406 -6.123529e-03
## 2024-10-31 -0.008683899 -8.963637e-03 -0.046070548 -3.760711e-03
## 2024-11-29 0.052125819 5.792306e-02 0.323147326 1.823731e-02
## 2024-12-12 0.032350559 2.949805e-03 0.191712565 -5.345514e-02calculate_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 on 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(0.25, 0.25, 0.25, 0.25))## # A tibble: 1 × 4
## QQQ SPY TSLA XOM
## <dbl> <dbl> <dbl> <dbl>
## 1 0.153 0.127 0.581 0.139plot_data <- asset_returns_wide_tbl %>%
calculate_component_contribution(w = c(0.25, 0.25, 0.25, 0.25)) %>%
# Transform to long form
pivot_longer(cols = everything(), names_to = "Asset", values_to = "Contribution") %>%
# Add weights
add_column(weight = c(0.25, 0.25, 0.25, 0.25)) %>%
# 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?
Based on the data the asset(s) in my portfolio which was the largest contributor to portfolio volatility was TSLA the reason behind that is be contribution to volatility was 58%!
Yes I do believe that the portfolio risk was mainly focused on one asset which was TSLA. This is because meanwhile every stock had the same weight in the portfolio each at 25% and TSLA was the only stock which it’s contribution to volatility was more than the it’s weight. The closest stock QQQ contribution to volatility was of 16%, SPY had a contribution of 12% and XOM had a contribution of 14%. There is an astronomical difference between TSLA and every other stock in the portfolio.