You will be able to calculated credit risk capital related quantities using this package. These reflect the standards outlined in APRA's credit risk prudential standards, and consequently reflects the Australian implementation of the Basel II and Basel III capital standards.
The following illustrates the capital ratios associated with retail exposures with different credit risk characteristics.
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
#> ✔ ggplot2 2.2.1 ✔ purrr 0.2.4
#> ✔ tibble 1.4.2 ✔ dplyr 0.7.4
#> ✔ tidyr 0.8.0 ✔ stringr 1.2.0
#> ✔ readr 1.1.1 ✔ forcats 0.2.0
#> ── Conflicts ────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
library(creditriskau)
x <- seq(0.01, 0.99, by = 0.03)
pd <- rep(x, 3)
lgd <- 0.20
sub_class <- rep(c("mortgage", "qrr", "other"), each = length(x))
k <- retail_capital(pd, lgd, sub_class)
df <- tibble(pd, lgd, sub_class, k)
ggplot(df, aes(x = pd, y = k, colour = sub_class)) +
geom_point() +
theme_minimal() +
labs(x = "PD", y = "Capital ratio", colour = "Sub-asset class")
The following illustrates the capital ratios associated with non-retail exposures with different credit risk characteristics.
size <- rep(c(1, 10, 100), each = length(x))
k <- non_retail_capital(pd, lgd, size, 1, FALSE)
df <- tibble(pd, lgd, size, k)
ggplot(df, aes(x = pd, y = k, colour = as.character(size))) +
geom_point() +
theme_minimal() +
labs(x = "PD", y = "Capital ratio", colour = "Size (A$m)")
Capital ratios for specialised lending facilities subject to slotting can be accessed using the slotting_capital()
function.
To be completed.
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