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#!/usr/bin/Rscript
require(methods)
options(warn=2)
args <- commandArgs(trailingOnly=TRUE)
if(.Platform$OS.type == "unix"){
root.dir <- "/home/share/CorpCDOs"
}else{
root.dir <- "//WDSENTINEL/share/CorpCDOs"
}
source(file.path(root.dir, "code", "R", "cds_utils.R"))
source(file.path(root.dir, "code", "R", "cds_functions_generic.R"))
source(file.path(root.dir, "code", "R", "index_definitions.R"))
source(file.path(root.dir, "code", "R", "tranche_functions.R"))
source(file.path(root.dir, "code", "R", "yieldcurve.R"))
source(file.path(root.dir, "code", "R", "optimization.R"))
##figure out the workdate
if(length(args) >= 1){
workdate <- as.Date(args[1])
}else{
workdate <- Sys.Date()
}
futurequotes <- read.csv(file.path(root.dir, "data", "Yield Curves",
sprintf("futures-%s.csv", workdate)), header=F)
#retrieve yield curve data
MarkitData <- getMarkitIRData(workdate)
L1m <- buildMarkitYC(MarkitData, futurequotes[,2], dt = 1/12)
L2m <- buildMarkitYC(MarkitData, futurequotes[,2], dt = 1/6)
L3m <- buildMarkitYC(MarkitData, futurequotes[,2])
L6m <- buildMarkitYC(MarkitData, futurequotes[,2], dt = 1/2)
setEvaluationDate(as.Date(MarkitData$effectiveasof))
setCalendarContext("UnitedStates/GovernmentBond")
## calibrate HY21
## calibrate the single names curves
singlenames.data <- read.csv(file.path(root.dir, "Scenarios", "Calibration",
paste0("hy21_singlenames_", workdate, ".csv")))
nondefaulted <- singlenames.data[!singlenames.data$ticker %in% hy21$defaulted,]
bps <- 1e-4
cdsdates <- as.Date(character(0))
for(tenor in paste0(1:5, "y")){
cdsdates <- c(cdsdates, cdsMaturity(tenor, date=workdate+1))
}
hy21portfolio <- c()
for(i in 1:nrow(nondefaulted)){
SC <- new("creditcurve",
recovery=nondefaulted$recovery[i]/100,
startdate=workdate,
issuer=as.character(nondefaulted$ticker[i]))
quotes <- data.frame(maturity=cdsdates, upfront = as.numeric(nondefaulted[i,4:8]) /100,
running=rep(nondefaulted$running[i] * bps, 5))
SC@curve <- cdshazardrate(quotes, nondefaulted$recovery[i]/100, workdate)
hy21portfolio <- c(hy21portfolio, SC)
}
issuerweights <- rep(1/length(hy21portfolio), length(hy21portfolio))
## load tranche data
K <- c(0, 0.15, 0.25, 0.35, 1)
Kmodified <- adjust.attachments(K, hy21$loss, hy21$factor)
markit.data <- read.csv(file.path(root.dir, "Scenarios", "Calibration",
paste0("hy21_tranches_", workdate, ".csv")))
tranche.upf <- markit.data$Mid
tranche.running <- c(0.05, 0.05, 0.05, 0.05)
# get the index ref
hy21$indexref <- markit.data$bidRefPrice[1]/100
hy21portfolio.tweaked <- tweakcurves(hy21portfolio, hy21, workdate)
SurvProb <- SPmatrix(hy21portfolio.tweaked, hy21)
Ngrid <- 2 * nrow(nondefaulted) + 1
recov <- sapply(hy21portfolio.tweaked, attr, "recovery")
cs <- couponSchedule(nextIMMDate(workdate), hy21$maturity,"Q", "FIXED", 0.05, 0)
##calibrate by modifying the factor distribution
bottomup <- 1:3
topdown <- 2:4
n.int <- 500
n.credit <- length(hy21portfolio)
errvec <- c()
quadrature <- gauss.quad.prob(n.int, "normal")
w <- quadrature$weights
Z <- quadrature$nodes
w.mod <- w
defaultprob <- 1 - SurvProb
p <- defaultprob
rho <- rep(0.45, n.credit)
result <- matrix(0, 4, n.int)
for(l in 1:150){
Rstoch <- array(0, dim=c(n.credit, n.int, ncol(SurvProb)))
for(t in 1:ncol(SurvProb)){
for(i in 1:n.credit){
Rstoch[i,,t] <- stochasticrecovC(recov[i], 0, Z, w.mod, rho[i], defaultprob[i,t], p[i,t])
}
}
L <- array(0, dim=c(n.int, Ngrid, ncol(defaultprob)))
R <- array(0, dim=c(n.int, Ngrid, ncol(defaultprob)))
for(t in 1:ncol(defaultprob)){
S <- 1 - Rstoch[,,t]
L[,,t] <- t(lossdistCZ(p[,t], issuerweights, S, Ngrid, 0, rho, Z))
R[,,t] <- t(lossdistCZ(p[,t], issuerweights, 1-S, Ngrid, 0, rho, Z))
}
for(i in 1:n.int){
result[,i] <- tranche.pvvec(Kmodified, L[i,,], R[i,,], cs)
}
## solve the optimization problem
program <- KLfit(100*(result[bottomup,]+1), w, tranche.upf[bottomup])
err <- 0
for(i in 1:n.credit){
for(j in 1:ncol(p)){
err <- err + abs(crossprod(shockprob(p[i,j], rho[i], Z), program$weight) - defaultprob[i,j])
}
}
errvec <- c(errvec, err)
## update the new probabilities
p <- MFupdate.probC(Z, program$weight, rho, defaultprob)
errvec <- c(errvec, err)
w.mod <- program$weight
cat(err,"\n")
}
Rstoch <- array(0, dim=c(n.credit, n.int, ncol(SurvProb)))
for(t in 1:ncol(SurvProb)){
for(i in 1:n.credit){
Rstoch[i,,t] <- stochasticrecovC(recov[i], 0, Z, w.mod, rho[i], defaultprob[i,t], p[i,t])
}
}
Lw <- matrix(0, Ngrid, n.int)
Rw <- matrix(0, Ngrid, n.int)
L <- matrix(0, Ngrid, ncol(defaultprob))
R <- matrix(0, Ngrid, ncol(defaultprob))
for(t in 1:ncol(defaultprob)){
S <- 1 - Rstoch[,,t]
Lw <- lossdistCZ(p[,t], issuerweights, S, Ngrid, 0, rho, Z)
Rw <- lossdistCZ(p[,t], issuerweights, 1-S, Ngrid, 0, rho, Z)
L[,t] <- Lw%*%w.mod
R[,t] <- Rw%*%w.mod
}
dist <- list(L=L, R=R)
write.table(data.frame(Z=Z, w=w.mod),
file=file.path(root.dir, "Scenarios", "Calibration",
paste0("calibration-", workdate, ".csv")),
col.names=T, row.names=F, sep=",")
save(singlenames.data, hy21, tranche.upf, dist,
file = file.path(root.dir, "Scenarios", "Calibration",
paste0("marketdata-", workdate, ".RData")), compress="xz")
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