diff options
Diffstat (limited to 'build_SC.R')
| -rw-r--r-- | build_SC.R | 140 |
1 files changed, 0 insertions, 140 deletions
diff --git a/build_SC.R b/build_SC.R deleted file mode 100644 index 11e2ec7b..00000000 --- a/build_SC.R +++ /dev/null @@ -1,140 +0,0 @@ -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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