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Bank of Japan(BOJ) publish research paper regularly.
And, they issued very interesting paper about valuation of CDO recently.

They introduced copula for pricing of CDO,and discussed how different CDO spreads were with using different copula for pricing.
I would like to reproduce their result (especially,P23-Table7)

The condition of calculation is following that
• number of debt（NUM.REFDEBT）:=100
• maturity(MATURITY):=5 year
• recovery rate(RECOVERY.RATE):=40%（constant value）
• probability of default (DEFAULT.PROBABILITY):=5％（in 5 years）
• parameter of nomal copula ρ:=0.15
• parameter of clayton copula α:=0.21
They apporoximated their valuation formula for easy calculation(equation (27))
(They assumed that CDO spread were paid as discounted bond at the begging.)

I simulated valuation of CDO with their method.
The result is following that
copula/trancheEquitymezzanineseniorsuper senior
normal1,145.4262.490.520.000
t(20)1,055.2886.072.180.004
t(6)896.74126.448.560.044
t(3)733.31165.9023.560.191
clayton857.64135.7312.830.084
This table reproduce their result(P23-Table7).
And, In senior or super senior,you can understand that the CDO spread which is evaluated by normal copula is lower than the others. It means that normal copula is inadequate in financial crisis.
I show you my programming code（by R language).
If you copy and run my source code, you can duplicate my result easily.
Before you run, please install “copula”package.

```library(copula)
recovery.rate, attachment, detachment, num.path, num.refdebt)
{
random.copula <- rcopula(copula,num.path)
num.default <- rowSums(random.copula < default.probability)
loss.refdebt <- (1-recovery.rate)/num.refdebt*num.default
loss.tranche <- (pmax(loss.refdebt - attachment,0)-pmax(loss.refdebt - detachment,0))/(detachment-attachment)
expectation.loss.tranche <- sum(loss.tranche)/num.path
}
################  main  ##################
#parameter
NUM.PATH <- 10^3
NUM.REFDEBT <- 100
DEFAULT.PROBABILITY <- 0.05
MATURITY <- 5
RECOVERY.RATE <- 0.4
#copulas which I want to compare.
COPULA <- list(normalCopula(0.15, dim = NUM.REFDEBT),
tCopula(0.15, dim = NUM.REFDEBT, df = 20),
tCopula(0.15, dim = NUM.REFDEBT, df = 6),
tCopula(0.15, dim = NUM.REFDEBT, df = 3),
claytonCopula(0.21, dim = NUM.REFDEBT)
)
#define for easy programming
RECOVERY.RATE, attachment, detachment, NUM.PATH, NUM.REFDEBT)
}
result <- list()
#tranche:equity
#tranche:mezzanine
#tranche:senior
#tranche:super senior
#convert to matrix, and chenge unit to "bp"
result <- 10^4*do.call("cbind", result)
colnames(result) <- c("equity","mezzanine","senior","super senior")
rownames(result) <- c("normal","t(20)","t(6)","t(3)","clayton")
result```