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library(doParallel)
library(yaml)
hostname <- system("hostname", intern=TRUE)
if(hostname=="debian"){
registerDoParallel(8)
}else{
registerDoParallel(4)
}
args <- commandArgs(trailingOnly=TRUE)
if(.Platform$OS.type == "unix"){
root.dir <- "/home/share/CorpCDOs"
}else{
root.dir <- "//WDSENTINEL/share/CorpCDOs"
}
if(length(args) >= 1){
workdate <- as.Date(args[1])
}else{
workdate <- Sys.Date()
}
if(length(args) >=2){
argslist <- strsplit(args[-1], ",")
dealnames <- unlist(lapply(argslist, function(x)x[1]))
reinvflags <- unlist(lapply(argslist, function(x)x[2]))
}else{
data <- read.table(file.path(root.dir, "scripts", "deals_to_price.txt"),
colClasses=c("character", "logical"))
dealnames <- data$V1
reinvflags <- data$V2
unlink(file.path(root.dir, "scripts", "deals_to_price.txt"))
}
source(file.path(root.dir, "code", "R", "intex_deal_functions.R"))
source(file.path(root.dir, "code", "R", "index_definitions.R"))
source(file.path(root.dir, "code", "R", "etdb.R"))
source(file.path(root.dir, "code", "R", "cds_functions_generic.R"))
source(file.path(root.dir, "code", "R", "cds_utils.R"))
get.reinvassets <- function(dealname, workdate){
r <- list()
sqlstr <- sprintf("select * from et_historicaldealinfo('%s', '%s') where ReinvFlag Is true",
dealname, workdate)
data <- dbGetQuery(dbCon, sqlstr)
if(nrow(data)>0){
for(i in 1:nrow(data)){
r[[data$issuername[i]]] <- list(coupontype=data$fixedorfloat[i], liborfloor=data$liborfloor[i])
}
}
return( r )
}
calibration.date <- prevBusDay(workdate)
calibration <- read.table(file.path(root.dir, "Scenarios", "Calibration",
paste0("calibration-", calibration.date,".csv")), sep=",", header=T)
Z <- calibration$Z
w <- calibration$w
Ngrid <- 201
MarkitData <- getMarkitIRData(calibration.date)
futurequotes <- read.csv(file.path(root.dir, "data", "Yield Curves",
sprintf("futures-%s.csv", calibration.date)), header=F)
setEvaluationDate(as.Date(MarkitData$effectiveasof))
setCalendarContext("UnitedStates/GovernmentBond")
L1m <- buildMarkitYC(MarkitData, futurequotes[,2], dt = 1/12)
L2m <- buildMarkitYC(MarkitData, futurequotes[,2], dt = 1/6)
L3m <- buildMarkitYC(MarkitData, futurequotes[,2])
L6m <- buildMarkitYC(MarkitData, futurequotes[,2], dt = 1/2)
L12m <- buildMarkitYC(MarkitData, futurequotes[,2], dt = 1)
support <- seq(0, 1, length = Ngrid)
recoverylag <- 90 ##days
## reinvestment parameters
## need to match parameters in build_portfolios.R
reinvspread <- 0.04
reinvfixed <- 0.07
basecase.rollingmaturity <- 84 ##months
reinvlag <- 3 ##months
n.scenarios <- 100
recov.adj <- 1
for(j in seq_along(dealnames)){
load(file.path(root.dir, "Scenarios", paste("Portfolios", workdate, sep="_"),
paste(dealnames[j], "RData", sep=".")))
if(is.na(deal.data$"Reinv End Date")){
reinvflags[j] <- FALSE
}
dp <- A$DP
pp <- A$PP
dpmod <- MFupdate.probC(Z, w, deal.portfolio$beta, dp)
ppmod <- MFupdate.probC(-Z, w, deal.portfolio$beta, pp)
dist.joint <- MFlossdist.prepay.joint(w, Z, deal.portfolio$beta, dp, dpmod, pp, ppmod,
deal.weights, 1-S, Ngrid)
distDR <- dist.transform(dist.joint)
## compute E(R|D)
R <- matrix(0, Ngrid, dim(distDR)[1])
for(t in 1:dim(distDR)[1]){
R[,t] <- sweep(distDR[t,,], 1, rowSums(distDR[t,,]), "/") %*% support
R[1,t] <- 0
if(t >= 2){
R[,t] <- pmax(R[,t], R[,t-1])
}
}
## compute scenariosd
scenariosd <- matrix(0, n.scenarios, dim(distDR)[1])
scenariosr <- matrix(0, n.scenarios, dim(distDR)[1])
percentiles <- seq(0, 1, 1/n.scenarios)
for(t in 1:dim(distDR)[1]){
D <- rowSums(distDR[t,,])
Dfun <- splinefun(c(0, cumsum(D)), c(0, support), "monoH.FC")
Rfun <- approxfun(support, R[,t], rule=2)
for(i in 1:n.scenarios){
## this is roughtly E(D|D is in ith percentile)
## using trapezoidal approximation
scenariosd[i,t] <- 0.5 * (Dfun((i-1)*0.01)+Dfun(i*0.01))
if(t>=2 && scenariosd[i,t] < scenariosd[i,t-1]){
scenariosd[i,t] <- scenariosd[i,t-1]
}
scenariosr[i,t] <- Rfun(scenariosd[i,t])
if(t>=2 && scenariosr[i,t] < scenariosr[i,t-1]){
scenariosr[i,t] <- scenariosr[i,t-1]
}
}
}
## we need to adjust the recovery because intex has some embedded amortization assumptions
## that we can't turn off (multiply by recov.adj)
intexrecov <- matrix(0, n.scenarios, dim(distDR)[1])
for(i in 1:dim(distDR)[1]){
if(i==1){
intexrecov[,i] <- recov.adj * (scenariosr[,i]/scenariosd[,1])
}else{
intexrecov[,i] <- recov.adj * (scenariosr[,i]-scenariosr[,i-1])/(scenariosd[,i]-scenariosd[,i-1])
}
}
cdr <- cdrfromscenarios(scenariosd, deal.dates)
## linear approximation for monthly scenarios
deal.data <- getdealdata(dealnames[j])
deal.datesmonthly <- getdealschedule(deal.data, "1 month")
deal.datesmonthlylagged <- getdealschedule(deal.data, "1 month", workdate, 92)
cdrmonthly <- matrix(0, n.scenarios, length(deal.datesmonthly))
recoverymonthly <- matrix(0, n.scenarios, length(deal.datesmonthly))
scenariosrmonthly <- matrix(0, n.scenarios, length(deal.datesmonthly))
scenariosdmonthly <- 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)$y
scenariosrmonthly[i,] <- approx(deal.dates, scenariosr[i,], deal.datesmonthly, rule=2)$y
scenariosdmonthly[i,] <- approx(deal.dates, scenariosd[i,], deal.datesmonthly, rule=2)$y
}
recoverymonthly <- pmin(recoverymonthly,1)
recoverymonthly[!is.finite(recoverymonthly)] <- 100
## compute reinvestment price
if(!is.na(deal.data$"Reinv End Date") && deal.data$"Reinv End Date" <= workdate){
## we cap rolling maturity at the current weighted average maturity of the portfolio
rollingmaturity <- (crossprod(sapply(deal.portfolio$SC, creditcurve.maturity),
deal.portfolio$notional)/sum(deal.portfolio$notional)
- as.numeric(workdate))/365*12
}else{
rollingmaturity <- basecase.rollingmaturity
}
## DC <- DiscountCurve(L3m$params, L3m$tsQuotes, yearFrac(L3m$params$tradeDate, deal.datesmonthlylagged))
## df <- DC$discounts
## forwards <- DC$forwards
cdrmonthly.dt <- data.table(date=deal.datesmonthly, t(cdrmonthly), key="date")
recoverymonthly.dt <- data.table(date=deal.datesmonthly, t(recoverymonthly), key="date")
reinvassets <- get.reinvassets(dealnames[j], workdate)
reinvprices <- list()
if(reinvflags[j] && length(reinvassets)>0){
for(assetname in names(reinvassets)){
asset <- reinvassets[[assetname]]
if(asset$coupontype=="FLOAT") {
coupon <- reinvspread
}else{
coupon <- reinvfixed
}
##reinvest tweak
#coupon <- coupon-0.01
reinvprices[[assetname]] <- foreach(date = iter(deal.datesmonthly), .combine=c) %dopar% {
100 * forwardportfolioprice2(cdrmonthly.dt, recoverymonthly.dt, date,
min(date+rollingmaturity*30, deal.data$maturity),
asset$coupontype, coupon, asset$liborfloor/100)
}
## reinvprices[[assetname]] <- foreach(date = iter(deal.datesmonthly), .combine=c) %dopar% {
## 100 * forwardportfolioprice(deal.portfolio, date,
## min(date+rollingmaturity*30, deal.data$maturity),
## asset$coupontype, coupon, asset$liborfloor/100)
## }
## for(i in seq_along(deal.datesmonthly)){
## date <- deal.datesmonthly[i]
## reinvprices[[assetname]] <-
## 100 * forwardportfolioprice2(cdrmonthly.dt, recoverymonthly.dt, date,
## min(date+rollingmaturity*30, deal.data$maturity),
## asset$coupontype, coupon, asset$liborfloor/100)
## }
}
}
save.dir <- file.path(root.dir, "Scenarios", paste("Intex curves", workdate, sep="_"), "csv")
if(!file.exists(save.dir)){
dir.create(save.dir, recursive = T)
}
write.table(cdrmonthly,
file= file.path(save.dir, paste0(dealnames[j],"-cdr.csv")),
row.names=F, col.names=F, sep=",")
write.table(100 * recoverymonthly,
file=file.path(save.dir, paste0(dealnames[j],"-recovery.csv")),
row.names=F, col.names=F, sep=",", na="NaN")
write.table(reinvprices, file = file.path(save.dir, paste0(dealnames[j], "-reinvprices.csv")),
row.names=F, col.names=T, sep=",")
configfile <- file.path(save.dir, paste0(dealnames[j], ".config"))
config <- list(rollingmat = as.integer(rollingmaturity),
reinvflag = reinvflags[j])
cat(as.yaml(config), file = configfile)
save(scenariosd, scenariosr, dist.joint, file=file.path(save.dir, paste0(dealnames[j], ".RData")),
compress="xz")
cat("generated scenarios for:", dealnames[j], "\n")
}
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