cm.gain {CreditMetrics} | R Documentation |
cm.gain
computes the profits or losses, this is done by building the
difference from the reference value and the simulated portfolio values of the
credit positions.
cm.gain(M, lgd, ead, N, n, r, rho, 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 |
rating |
rating of companies |
This function uses cm.portfolio
and cm.ref
. By building the
difference of these functions, one gets the profits, if the difference is
positive, or the losses, if the difference is negative.
This functions returns the simulated profits or losses.
Andreas Wittmann andreas_wittmann@gmx.de
Glasserman, Paul, Monte Carlo Methods in Financial Engineering, Springer 2004
cm.matrix
, cm.ref
, cm.portfolio
N <- 3 n <- 50000 r <- 0.03 ead <- c(4000000, 1000000, 10000000) lgd <- 0.45 rating <- c("BBB", "AA", "B") firmnames <- c("firm 1", "firm 2", "firm 3") # 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 form 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.gain(M, lgd, ead, N, n, r, rho, rating)