diff options
Diffstat (limited to 'R')
| -rw-r--r-- | R/calibrate_tranches.R | 71 | ||||
| -rw-r--r-- | R/script_calibrate_tranches.R | 131 |
2 files changed, 157 insertions, 45 deletions
diff --git a/R/calibrate_tranches.R b/R/calibrate_tranches.R index 55315587..52802685 100644 --- a/R/calibrate_tranches.R +++ b/R/calibrate_tranches.R @@ -27,25 +27,6 @@ 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))
-## }
-
hy19portfolio <- c()
for(i in 1:nrow(nondefaulted)){
SC <- new("creditcurve",
@@ -59,43 +40,20 @@ for(i in 1:nrow(nondefaulted)){ }
issuerweights <- rep(1/length(hy19portfolio), length(hy19portfolio))
-hy19$indexref <- 1.025
+hy19$indexref <- 1.0275
hy19portfolio.tweaked <- tweakcurves(hy19portfolio, hy19)
SurvProb <- SPmatrix(hy19portfolio.tweaked, hy19)
## load common parameters
K <- c(0, 0.15, 0.25, 0.35, 1)
Kmodified <- adjust.attachments(K, hy19$loss, hy19$factor)
-tranche.upf <- c(42.25, 95.375, 108.28125, 116.8125)
+tranche.upf <- c(43.375, 95.125, 108.25, 116.8125)
tranche.running <- c(0.05, 0.05, 0.05, 0.05)
-Ngrid <- 2*nrow(nondefaulted)+1
+Ngrid <- 2 * nrow(nondefaulted)+1
recov <- sapply(hy19portfolio.tweaked, attr, "recovery")
cs <- couponSchedule(nextIMMDate(today()), hy19$maturity,"Q", "FIXED", 0.05, 0)
-## calibrate the tranches using base correlation
-rhovec <- c()
-f <- function(rho, ...){
- temp <- BClossdistC(SurvProb, issuerweights, recov, rho, Ngrid)
- bp <- 100*(1+1/(Kmodified[i]-Kmodified[i-1]) *
- (tranche.pv(temp$L, temp$R, cs, 0, Kmodified[i], Ngrid) -
- tranche.pv(oldtemp$L, oldtemp$R, cs, 0, Kmodified[i-1], Ngrid)))
- return( abs(tranche.upf[i-1]-bp))
-}
-
-for(i in 2:length(Kmodified)){
- rho <- optimize(f, interval=c(0,1),
- SurvProb, issuerweights, recov, Ngrid, tranche.upf, Kmodified, cs, oldtemp)$minimum
- oldtemp <- BClossdistC(SurvProb, issuerweights, recov, rho, Ngrid)
- rhovec <- c(rhovec, rho)
-}
-
-rhovec <- c(0, rhovec)
-deltas <- c()
-for(i in 2:5){
- deltas <- c(deltas, BCtranche.delta(hy19portfolio.tweaked, hy19, 0.05, K[i-1], K[i], rhovec[i-1], rhovec[i], Ngrid))
-}
-
##calibrate by modifying the factor distribution
bottomup <- 1:3
topdown <- 2:4
@@ -155,6 +113,29 @@ for(l in 1:100){ 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-", Sys.Date(), ".RData")))
+## calibrate the tranches using base correlation
+rhovec <- c()
+f <- function(rho, ...){
+ temp <- BClossdistC(SurvProb, issuerweights, recov, rho, Ngrid)
+ bp <- 100*(1+1/(Kmodified[i]-Kmodified[i-1]) *
+ (tranche.pv(temp$L, temp$R, cs, 0, Kmodified[i], Ngrid) -
+ tranche.pv(oldtemp$L, oldtemp$R, cs, 0, Kmodified[i-1], Ngrid)))
+ return( abs(tranche.upf[i-1]-bp))
+}
+
+for(i in 2:length(Kmodified)){
+ rho <- optimize(f, interval=c(0,1),
+ SurvProb, issuerweights, recov, Ngrid, tranche.upf, Kmodified, cs, oldtemp)$minimum
+ oldtemp <- BClossdistC(SurvProb, issuerweights, recov, rho, Ngrid)
+ rhovec <- c(rhovec, rho)
+}
+
+rhovec <- c(0, rhovec)
+deltas <- c()
+for(i in 2:5){
+ deltas <- c(deltas, BCtranche.delta(hy19portfolio.tweaked, hy19, 0.05, K[i-1], K[i], rhovec[i-1], rhovec[i], Ngrid))
+}
+
## computes MFdeltas
newportf <- hy19portfolio.tweaked
eps <- 1e-4
diff --git a/R/script_calibrate_tranches.R b/R/script_calibrate_tranches.R new file mode 100644 index 00000000..60578c95 --- /dev/null +++ b/R/script_calibrate_tranches.R @@ -0,0 +1,131 @@ +#!/usr/bin/Rscript
+
+library("parallel")
+
+if(.Platform$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"))
+
+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$effectiveasof))
+
+## calibrate HY19
+## calibrate the single names curves
+singlenames.data <- read.csv(file.path(root.dir, "Scenarios", "Calibration",
+ paste0("hy19_singlenames_", Sys.Date(), ".csv")))
+nondefaulted <- singlenames.data[!singlenames.data$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$factor)
+markit.data <- read.csv(file.path(root.dir, "Scenarios", "Calibration",
+ paste0("hy19_tranches_", Sys.Date(), ".csv")))
+
+tranche.upf <- markit.data$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(today()), 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-", Sys.Date(), ".RData")))
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