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