diff options
| -rw-r--r-- | R/calibrate_tranches_MF.R | 51 | ||||
| -rw-r--r-- | R/calibration.R | 46 |
2 files changed, 50 insertions, 47 deletions
diff --git a/R/calibrate_tranches_MF.R b/R/calibrate_tranches_MF.R index 95a9082e..32396a88 100644 --- a/R/calibrate_tranches_MF.R +++ b/R/calibrate_tranches_MF.R @@ -30,59 +30,16 @@ if(length(args) >= 1){ exportYC(tradedate)
## calibrate HY21
## calibrate the single names curves
-n.int <- 250
+n.int <- 100
list2env(GHquad(n.int), envir=parent.frame())
Ngrid <- 201
index <- load.index("hy21", date=tradedate, "5yr", Z, w, Ngrid)
index <- set.singlenamesdata(index, tradedate)
-index <- set.tranchedata(index, tradedate)
-
## load tranche data
-n.credit <- length(index$portfolio)
-index$cs$coupons <- index$cs$coupons*0.05
+index <- set.tranchedata(index, tradedate)
##calibrate by modifying the factor distribution
-bottomup <- 1:3
-topdown <- 2:4
-
-index$w.mod <- w
-p <- index$defaultprob
-rho <- rep(0.45, n.credit)
-result <- matrix(0, 4, n.int)
-err <- Inf
-errvec <- c()
-while(err >0.01){
- Rstoch <- MFrecovery(index, p)
- L <- array(0, dim=c(index$N, n.int, ncol(index$defaultprob)))
- R <- array(0, dim=c(index$N, n.int, ncol(index$defaultprob)))
- for(t in 1:ncol(index$defaultprob)){
- S <- 1 - Rstoch[,,t]
- L[,,t] <- lossdistCZ(p[,t], index$issuerweights, S, index$N, 0, rho, index$Z)
- R[,,t] <- lossdistCZ(p[,t], index$issuerweights, 1-S, index$N, 0, rho, index$Z)
- }
- for(i in 1:n.int){
- result[,i] <- tranche.pvvec(index$K, L[,i,], R[,i,], index$cs)
- }
- ## solve the optimization problem
- program <- KLfit(-result[bottomup,], w, index$tranche.quotes[bottomup])
-
- err <- 0
- for(i in 1:n.credit){
- for(j in 1:ncol(p)){
- err <- err + abs(crossprod(shockprob(p[i,j], rho[i], Z), program$weight) - index$defaultprob[i,j])
- }
- }
- errvec <- c(errvec, err)
-
- ## update the new probabilities
- p <- MFupdate.probC(Z, program$weight, rho, index$defaultprob)
-
- errvec <- c(errvec, err)
- index$w.mod <- program$weight
- cat(err,"\n")
-}
-
-dist <- MFdist(index)
-
+index$w.mod <- build.MFdist(index)
+dist <- MFlossdist(index)
write.table(data.frame(Z=Z, w=index$w.mod),
file=file.path(root.dir, "Scenarios", "Calibration",
paste0("calibration-", tradedate, ".csv")),
diff --git a/R/calibration.R b/R/calibration.R index 1b802d99..b8dc9295 100644 --- a/R/calibration.R +++ b/R/calibration.R @@ -118,3 +118,49 @@ build.skew <- function(index, type="bottomup"){ } return( rhovec ) } + +build.MFdist <- function(index, type="bottomup", tol=1e-2){ + index$w.mod <- w + p <- index$defaultprob + n.credit <- length(index$issuerweights) + rho <- rep(0.45, n.credit) + result <- matrix(0, 4, n.int) + n.tranches <- length(index$K)-1 + err <- Inf + if(type=="bottomup"){ + select <- 1:(n.tranches-1) + }else if(type=="topdown"){ + select <- 2:n.tranches + } + while(err > tol){ + Rstoch <- MFrecovery(index, p) + L <- array(0, dim=c(index$N, n.int, ncol(index$defaultprob))) + R <- array(0, dim=c(index$N, n.int, ncol(index$defaultprob))) + for(t in 1:ncol(index$defaultprob)){ + S <- 1 - Rstoch[,,t] + L[,,t] <- lossdistCZ(p[,t], index$issuerweights, S, index$N, 0, rho, index$Z) + R[,,t] <- lossdistCZ(p[,t], index$issuerweights, 1-S, index$N, 0, rho, index$Z) + } + for(i in 1:n.int){ + dist <- list(L=L[,i,], R=R[,i,]) + result[,i] <- MFtranche.pv(index, dist, protection=TRUE)$bp + } + ## solve the optimization problem + program <- KLfit(result[select,], w, index$tranche.quotes[select]) + + err <- 0 + for(i in 1:n.credit){ + for(j in 1:ncol(p)){ + err <- err + abs(crossprod(shockprob(p[i,j], rho[i], Z), program$weight) - index$defaultprob[i,j]) + } + } + + ## update the new probabilities + p <- MFupdate.probC(index$Z, program$weight, rho, index$defaultprob) + + index$w.mod <- program$weight + cat("=") + } + cat("\n") + return(index$w.mod) +} |