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#!/usr/bin/Rscript
library("parallel")
if(.Platform$OS.type == "unix"){
root.dir <- "/home/share/CorpCDOs"
cl <- makeForkCluster(4)
}else{
root.dir <- "//WDSENTINEL/share/CorpCDOs"
cl <- makeCluster(6)
}
source(file.path(root.dir, "code", "R", "cds_utils.R"))
source(file.path(root.dir, "code", "R", "cds_functions_generic.R"))
source(file.path(root.dir, "code", "R", "index_definitions.R"))
source(file.path(root.dir, "code", "R", "tranche_functions.R"))
source(file.path(root.dir, "code", "R", "yieldcurve.R"))
source(file.path(root.dir, "code", "R", "optimization.R"))
#figure out the workdate
filenames <- list.files(file.path(root.dir, "Scenarios", "Calibration"),
pattern = "hy19_singlenames*")
dates <- strtrim(sapply(strsplit(filenames, "_"), function(x)x[3]), 10)
workdate <- as.Date(sort(dates, decreasing = TRUE)[1])
#retreive yield curve data
MarkitData <- getMarkitIRData(workdate)
L1m <- buildMarkitYC(MarkitData, dt = 1/12)
L2m <- buildMarkitYC(MarkitData, dt = 1/6)
L3m <- buildMarkitYC(MarkitData)
L6m <- buildMarkitYC(MarkitData, dt = 1/2)
setEvaluationDate(as.Date(MarkitData$effectiveasof))
## calibrate HY19
## calibrate the single names curves
singlenames.data <- read.csv(file.path(root.dir, "Scenarios", "Calibration",
paste0("hy19_singlenames_", workdate, ".csv")))
nondefaulted <- singlenames.data[!singlenames.data$ticker %in% hy19$defaulted,]
bps <- 1e-4
cdsdates <- as.Date(character(0))
for(tenor in paste0(1:5, "y")){
cdsdates <- c(cdsdates, cdsMaturity(tenor))
}
hy19portfolio <- 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,4:8]) /100,
running=rep(nondefaulted$running[i] * bps, 5))
SC@curve <- cdshazardrate(quotes, nondefaulted$recovery[i]/100)
hy19portfolio <- c(hy19portfolio, SC)
}
issuerweights <- rep(1/length(hy19portfolio), length(hy19portfolio))
## load tranche data
K <- c(0, 0.15, 0.25, 0.35, 1)
Kmodified <- adjust.attachments(K, hy19$loss, hy19$factor)
markit.data <- read.csv(file.path(root.dir, "Scenarios", "Calibration",
paste0("hy19_tranches_", workdate, ".csv")))
tranche.upf <- markit.data$Mid
tranche.running <- c(0.05, 0.05, 0.05, 0.05)
# get the index ref
hy19$indexref <- markit.data$bidRefPrice[1]/100
hy19portfolio.tweaked <- tweakcurves(hy19portfolio, hy19)
SurvProb <- SPmatrix(hy19portfolio.tweaked, hy19)
Ngrid <- 2 * nrow(nondefaulted) + 1
recov <- sapply(hy19portfolio.tweaked, attr, "recovery")
cs <- couponSchedule(nextIMMDate(workdate), hy19$maturity,"Q", "FIXED", 0.05, 0)
##calibrate by modifying the factor distribution
bottomup <- 1:3
topdown <- 2:4
n.int <- 500
n.credit <- length(hy19portfolio)
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("root.dir", "shockprob", "issuerweights", "rho", "Z", "lossrecovdist.term",
"lossrecovdist", "lossdistC", "Ngrid",
"tranche.pvvec", "tranche.pv", "tranche.pl", "tranche.cl",
"trancheloss", "trancherecov", "pos", "Kmodified", "cs"))
## TODO: investigate if this is the right thing w.r.t recovery
parf <- function(i){
pshocked <- apply(p, 2, shockprob, rho=rho, Z=Z[i])
S <- 1 - Rstoch[i,,]
dist <- lossrecovdist.term(pshocked, , issuerweights, S, Ngrid)
return( tranche.pvvec(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(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, Z), program$weight) - defaultprob[i,j])
}
}
errvec <- c(errvec, err)
## update the new probabilities
p <- MFupdate.prob(Z, program$weight, rho, defaultprob)
errvec <- c(errvec, err)
w.mod <- program$weight
cat(err,"\n")
}
write.table(data.frame(Z=Z, w=w.mod),
file=file.path(root.dir, "Scenarios", "Calibration",
paste0("calibration-", Sys.Date(), ".csv")),
col.names=T, row.names=F, sep=",")
save(singlenames.data, hy19, tranche.upf,
file = file.path(root.dir, "Scenarios", "Calibration", paste0("marketdata-", workdate, ".RData")))
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