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library("RQuantLib")
library("parallel")
root = "//WDSENTINEL/share/CorpCDOs"
source(file.path(root, "R", "intex_deals_functions.R"))
MarkitData <- getMarkitIRData()
L1m <- buildMarkitYC(MarkitData, dt = 1/12)
L2m <- buildMarkitYC(MarkitData, dt = 1/6)
L3m <- buildMarkitYC(MarkitData)
L6m <- buildMarkitYC(MarkitData, dt = 1/2)
L12m <- buildMarkitYC(MarkitData, dt = 1)
setEvaluationDate(as.Date(MarkitData$effectiveasof))
today <- function(){
return(as.Date(MarkitData$effectiveasof))
}
bps <- 1e-4
global.params <- list()
global.params$recovery.assumptions <- list("Loan"=0.7,
"SecondLien"=0.3,
"Bond"=0.4,
"Mezzanine"=0.15,
"Adj_Covlite"=0.1)
global.params$cdoprices <- list("Aaa"=90,
"Aa"=80,
"A"=70,
"Baa"=60,
"Ba"=50,
"B"=40,
"NR"=40)
#reinvest in 7 years assets
global.params$rollingmaturity <- 7 * 365
global.params$defaultedlag <- 90
global.params$defaultcorr <- 0.4
global.params$defaultbondhazardrate <- 500 * bps
global.params$defaultloanhazardrate <- 400 * bps
global.params$alpha <- 0.25
global.params$beta <- 15
global.params$shape <- function(T)0.25+(1-exp(-T/5))
## dealnames <- c("stonln1", "babs072", "symph4", "flags5", "cent11", "wasatl", "oceant2", "acacl071", "limes")
dealnames <- c("acacl071", "limes")
calibration.date <- "2012-11-08"
calibration <- read.table(file.path(root, "Scenarios", paste0("calibration-",calibration.date,".csv")),
sep=",", header=T)
Z <- calibration$Z
w <- calibration$w
rho <- 0.45
Ngrid <- 201
cl <- makeCluster(6)
clusterExport(cl, list("shockprob", "lossdistC.prepay.joint", "lossrecovdist.joint.term", "lossdistC.joint"))
support <- seq(0, 1, length=Ngrid)
for(deal.name in dealnames){
deal.portfolio <- buildSC.portfolio(deal.name, global.params)
deal.data <- getdealdata(deal.name)
A <- SPmatrix2(deal.portfolio$SC, deal.data, freq="3 months")
S <- 1 - sapply(deal.portfolio$SC, attr, "recov")
deal.weights <- deal.portfolio$notional/sum(deal.portfolio$notional)
clusterExport(cl, list("deal.weights"))
deal.dates <- getdealschedule(deal.data)
## compute reinvestment price
reinvloanprice <- rep(0, length(deal.dates))
reinvmezzprice <- rep(0, length(deal.dates))
for(i in 1:length(deal.dates)){
reinvloanprice[i] <- forwardportfolioprice(deal.portfolio, deal.dates[i], global.params$rollingmaturity, "FLOAT", 0.0235)
reinvmezzprice[i] <- forwardportfolioprice(deal.portfolio, deal.dates[i], global.params$rollingmaturity, "FLOAT", 0.0385)
}
dp <- A$DP
pp <- A$PP
Smat <- matrix(S, length(deal.weights), ncol(dp))
##no correlation setup
##test <- lossrecovdist.joint.term(dp, pp, issuerweights, Smat, Ngrid)
dpmod <- MFupdate.prob(Z, w, rho, dp)
ppmod <- MFupdate.prob(-Z, w, rho, pp)
## dist <- MFlossrecovdist.prepay(w, Z, rho, dp, dpmod, pp, ppmod, deal.weights, 1-S, Ngrid, TRUE)
dist.joint <- MFlossdist.prepay.joint(cl, w, Z, rho, dp, dpmod,
pp, ppmod, deal.weights, 1-S, Ngrid, FALSE, n.chunks=4)
clusterCall(cl, gc)
gc()
## if don't want to use cluster (use less memory)
## dist.joint <- MFlossdist.prepay.joint(NULL, w, Z, rho, dp, dpmod,
## pp, ppmod, issuerweights, 1-S, Ngrid=201, FALSE)
## two ways to compute the joint (D, R) distribution
## first using the actual function (numerically instable)
## dist.joint2 <- MFlossdist.prepay.joint(cl, w, Z, rho, dp, dpmod,
## pp, ppmod, issuerweights, 1-S, Ngrid=201, TRUE)
## second, by doing a change of variable seems to work better for now
distDR <- dist.transform(dist.joint)
## compute E(R|D)
R <- matrix(0, Ngrid, ncol(dp))
for(t in 1:ncol(dp)){
R[,t] <- (sweep(distDR[t,,], 1, rowSums(distDR[t,,]), "/") %*% support)/support
}
R[1,] <- 0
n.scenarios <- 100
percentiles <- (seq(0, 1, length=n.scenarios+1)[-1]+
seq(0, 1, length=n.scenarios+1)[-(n.scenarios+1)])/2
## compute scenariosd
scenariosd <- matrix(0, n.scenarios, ncol(dp))
for(t in 1:ncol(dp)){
D <- rowSums(distDR[t,,])
D <- D/sum(D)
Dfun <- splinefun(c(0, cumsum(D)), c(0, support), "monoH.FC")
## dvallow <- floor(Dfun(percentiles)*(Ngrid-1))
## dvalup <- ceil(Dfun(percentiles)*(Ngrid-1))
scenariosd[,t] <- Dfun(percentiles)
}
## compute scenariosr
scenariosr <- matrix(0, n.scenarios, ncol(dp))
for(t in 1:ncol(dp)){
Rfun <- approxfun(support, R[,t], rule=2)
scenariosr[,t] <- Rfun(scenariosd[,t])
}
cdr <- cdrfromscenarios(scenariosd, deal.dates)
intexrecov <- recoveryfromscenarios(scenariosd, scenariosr)
## linear approximation for monthly scenarios
deal.datesmonthly <- getdealschedule(deal.data, "1 month")
cdrmonthly <- matrix(0, n.scenarios, length(deal.datesmonthly))
recoverymonthly <- matrix(0, n.scenarios, length(deal.datesmonthly))
for(i in 1:n.scenarios){
cdrmonthly[i,] <- approx(deal.dates, cdr[i,], deal.datesmonthly, rule=2)$y
recoverymonthly[i,] <- approx(deal.dates, intexrecov[i,], deal.datesmonthly, rule=2)$y
}
write.table(cdrmonthly, file=file.path(root, "Scenarios", paste0(deal.name,"-cdr.csv")), row.names=F, col.names=F, sep=",")
write.table(recoverymonthly * 100, file=file.path(root, "Scenarios", paste0(deal.name,"-recovery.csv")), row.names=F, col.names=F, sep=",")
cat("generated scenarios for:", deal.name, "\n")
}
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