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library("parallel")
setwd("//WDSENTINEL/share/CorpCDOs/R")
source("cds_utils.R")
source("cds_functions_generic.R")
source("index_definitions.R")
source("tranche_functions.R")
source("yieldcurve.R")
source("optimization.R")
cl <- makeCluster(6)
MarkitData <- getMarkitIRData()
L1m <- buildMarkitYC(MarkitData, dt = 1/12)
L2m <- buildMarkitYC(MarkitData, dt = 1/6)
L3m <- buildMarkitYC(MarkitData)
L6m <- buildMarkitYC(MarkitData, dt = 1/2)
setEvaluationDate(as.Date(MarkitData$effectiveasof))
## calibrate HY17
## calibrate the single names curves
singlenames.data <- read.table(file="clipboard", sep="\t", header=T)
nondefaulted <- singlenames.data[!singlenames.data$ticker %in% hy17$defaulted,]
bps <- 1e-4
cdsdates <- as.Date(character(0))
for(tenor in paste0(1:5, "y")){
cdsdates <- c(cdsdates, cdsMaturity(tenor))
}
## clusterEvalQ(cl, {setClass("abstractcurve")
## setClass("defaultcurve", contains="abstractcurve",
## representation(dates="Date", hazardrates="numeric"))
## setClass("creditcurve", representation(issuer="character", startdate="Date",
## recovery="numeric", curve="defaultcurve"))})
## clusterExport(cl, list("nondefaulted", "cdsdates", "cdshazardrate", "today",
## "bps", "couponSchedule", "nextIMMDate", "DiscountCurve", "L3m"))
## test <- parSapply(cl, 1:nrow(nondefaulted), parf)
## parf <- function(i){
## SC <- new("creditcurve",
## recovery=nondefaulted$recovery[i]/100,
## startdate=today(),
## issuer=as.character(nondefaulted$ticker[i]))
## quotes <- data.frame(maturity=cdsdates, upfront = as.numeric(nondefaulted[i,5:9])*0.01,
## running = rep(nondefaulted$running[i]*bps,5))
## return( cdshazardrate(quotes, nondefaulted$recovery[i]/100))
## }
hy17portfolio <- c()
for(i in 1:nrow(nondefaulted)){
SC <- new("creditcurve",
recovery=nondefaulted$recovery[i]/100,
startdate=today(),
issuer=as.character(nondefaulted$ticker[i]))
quotes <- data.frame(maturity=cdsdates, upfront = as.numeric(nondefaulted[i,5:9])*0.01,
running=rep(nondefaulted$running[i]*bps, 5))
SC@curve <- cdshazardrate(quotes, nondefaulted$recovery[i]/100)
hy17portfolio <- c(hy17portfolio, SC)
}
issuerweights <- rep(1/length(hy17portfolio), length(hy17portfolio))
hy17$indexref <- 1.0275
hy17portfolio.tweaked <- tweakcurves(hy17portfolio, hy17)
SurvProb <- SPmatrix(hy17portfolio.tweaked, hy17)
## load common parameters
K <- c(0, 0.15, 0.25, 0.35, 1)
Kmodified <- adjust.attachments(K, hy17$loss, hy17$factor)
tranche.upf <- c(48, 93.125, 105.8125, 114.625)
tranche.running <- c(0.05, 0.05, 0.05, 0.05)
lu <- 0.01
recov <- sapply(hy17portfolio.tweaked, attr, "recovery")
cs <- couponSchedule(nextIMMDate(today()), hy17$maturity,"Q", "FIXED", 0.05, 0)
## calibrate the tranches using base correlation
rhovec <- c()
f <- function(rho, ...){
temp <- BClossdistC(SurvProb, issuerweights, recov, rho, lu, 100)
return( abs(tranche.upf[i-1]-1/(Kmodified[i]-Kmodified[i-1])*
(tranche.bp(temp$L, temp$R, cs, 0, Kmodified[i])*Kmodified[i]-
tranche.bp(oldtemp$L, oldtemp$R, cs, 0, Kmodified[i-1])*Kmodified[i-1])) )
}
for(i in 2:length(Kmodified)){
rho <- optimize(f, interval=c(0,1),
SurvProb, issuerweights, recov, lu, tranche.upf, Kmodified, cs, oldtemp)$minimum
oldtemp <- BClossdistC(SurvProb, issuerweights, recov, rho, lu)
rhovec <- c(rhovec, rho)
}
#calibrate by modifying the factor distribution
bottomup <- 1:3
topdown <- 2:4
n.int <- 100
n.credit <- 96
errvec <- c()
quadrature <- gauss.quad.prob(n.int, "normal")
w <- quadrature$weights
Z <- quadrature$nodes
w.mod <- w
defaultprob <- 1 - SurvProb
p <- defaultprob
rho <- 0.45
clusterExport(cl, list("shockprob", "issuerweights", "rho", "Z", "lossrecovdist.term",
"lossrecovdist", "lossdistribC", "lu",
"tranche.bpvec", "tranche.bp", "tranche.pl", "tranche.cl",
"trancheloss", "trancherecov", "pos", "Kmodified", "cs"))
parf <- function(i){
pshocked <- apply(p, 2, shockprob, rho=rho, Z=Z[i])
S <- 1 - Rstoch[i,,]
dist <- lossrecovdist.term(pshocked, 0, issuerweights, S, lu)
return( tranche.bpvec(Kmodified, dist$L, dist$R, cs))
}
for(l in 1:100){
Rstoch <- array(0, dim=c(n.int, n.credit, ncol(SurvProb)))
for(t in 1:ncol(SurvProb)){
for(i in 1:n.credit){
Rstoch[,i,t] <- stochasticrecov(recov[i], 0, Z, w.mod, rho, defaultprob[i,t], p[i,t])
}
}
clusterExport(cl, list("Rstoch", "p"))
result <- parSapply(cl, 1:n.int, parf)
## solve the optimization problem
program <- KLfit(result[topdown,], w, tranche.upf[topdown])
err <- 0
for(i in 1:n.credit){
for(j in 1:ncol(p)){
err <- err + abs(crossprod(shockprob(p[i,j], rho, Z), program$weight) - defaultprob[i,j])
}
}
## update the new probabilities
for(i in 1:n.credit){
for(j in 1:ncol(p)){
p[i,j] <- fit.prob(Z, program$weight, rho, defaultprob[i,j])
}
}
errvec <- c(errvec, err)
w.mod <- program$weight
cat(err,"\n")
}
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