cm.CVaR {CreditMetrics} | R Documentation |
cm.CVaR
computes the credit value at risk for the simulated profits and
losses.
cm.CVaR(M, lgd, ead, N, n, r, rho, alpha, rating)
M |
one year empirical migration matrix, where the last row gives the default class. |
lgd |
loss given default |
ead |
exposure at default |
N |
number of companies |
n |
number of simulated random numbers |
r |
riskless interest rate |
rho |
correlation matrix |
alpha |
confidence level |
rating |
rating of companies |
With function cm.gain
one gets the profit and loss distribution of the credit
positions. By building the quantile at confidence level α the credit value
at risk can be reached.
Return value is the credit value at risk at confidence level α.
Andreas Wittmann andreas\_wittmann@gmx.de
Glasserman, Paul, Monte Carlo Methods in Financial Engineering, Springer 2004
N <- 3 n <- 50000 r <- 0.03 ead <- c(4000000, 1000000, 10000000) rc <- c("AAA", "AA", "A", "BBB", "BB", "B", "CCC", "D") lgd <- 0.45 rating <- c("BBB", "AA", "B") firmnames <- c("firm 1", "firm 2", "firm 3") alpha <- 0.99 # correlation matrix rho <- matrix(c( 1, 0.4, 0.6, 0.4, 1, 0.5, 0.6, 0.5, 1), 3, 3, dimnames = list(firmnames, firmnames), byrow = TRUE) # one year empirical migration matrix from standard&poors website rc <- c("AAA", "AA", "A", "BBB", "BB", "B", "CCC", "D") M <- matrix(c(90.81, 8.33, 0.68, 0.06, 0.08, 0.02, 0.01, 0.01, 0.70, 90.65, 7.79, 0.64, 0.06, 0.13, 0.02, 0.01, 0.09, 2.27, 91.05, 5.52, 0.74, 0.26, 0.01, 0.06, 0.02, 0.33, 5.95, 85.93, 5.30, 1.17, 1.12, 0.18, 0.03, 0.14, 0.67, 7.73, 80.53, 8.84, 1.00, 1.06, 0.01, 0.11, 0.24, 0.43, 6.48, 83.46, 4.07, 5.20, 0.21, 0, 0.22, 1.30, 2.38, 11.24, 64.86, 19.79, 0, 0, 0, 0, 0, 0, 0, 100 )/100, 8, 8, dimnames = list(rc, rc), byrow = TRUE) cm.CVaR(M, lgd, ead, N, n, r, rho, alpha, rating)