| cm.val {CreditMetrics} | R Documentation |
cm.val performs a valuation for the credit positions of each scenario.
This is an allocation in rating classes identification of the credit position
values.
cm.val(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 |
According to the value V_t the company is located in an other rating class. This location is performed with the migration matrix by determining the thresholds. In order to implement a valuation at time t, the credit spreads must be computed. With these the nominal is risk adjusted calculated. For a portfolio with many credits correlations are included by simulating correlated company yield returns. So the simulated ratings for each firm at time t = 1 can be computed.
Simulated values of the firms for each rating of each scenario.
Andreas Wittmann andreas_wittmann@gmx.de
Glasserman, Paul, Monte Carlo Methods in Financial Engineering, Springer 2004
cm.matrix, eigen, cm.state, cm.quantile,
cm.rnorm.cor
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.val(M, lgd, ead, N, n, r, rho, rating)