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source("cds_functions_generic.R")
buildSC <- function(quote, cs, cdsdates){
SC <- new("creditcurve",
recovery=quote$recovery,
startdate=tradedate,
issuer=quote$ticker)
quotes <- data.frame(maturity=cdsdates, upfront = quote$upfront,
running=quote$running)
SC@curve <- cdshazardrate(quotes, SC@recovery, tradedate, cs)
return( SC )
}
get.cdsSchedule <- function(tradedate, tenors=c(1:5, 7, 10)) {
cdsdates <- as.Date(character(0))
for(tenor in paste0(tenors, "y")){
newdate <- cdsMaturity(tenor, date=tradedate)
cdsdates <- c(cdsdates, newdate)
}
return( list(cs=couponSchedule(IMMDate(tradedate, noadj=TRUE), cdsdates[length(cdsdates)],
"Q", "FIXED", 1, 0, tradedate, IMMDate(tradedate, "prev")),
cdsdates=cdsdates) )
}
set.singlenamesdata <- function(index, tradedate, recov=NULL) {
cds.cs <- if (index$type %in% c("HY", "XO")) { # no need to build the full 10y curve
get.cdsSchedule(tradedate, 1:5)
} else {
get.cdsSchedule(tradedate)
}
quotes <- get.singlenamesquotes(index$name, tradedate)
tenor <- names(cds.cs$cdsdates)
index$portfolio <- list()
for(i in seq_along(quotes$tickers)){
if(quotes$ticker[i] %in% c("IACI", "TITANC", "ESPSAN", "CBRY")){
quotes$spread_curve[i,] <- rep(0.001, 8)
quotes$upfront_curve[i,] <- rep(0, 8)
quotes$recovery[i,] <- rep(0.4, 8)
}
quotes$upfront_curve[i, tenor] <- pmax(-yearFrac(tradedate+2, cds.cs$cdsdates)*
quotes$spread_curve[i, tenor] * 1e-2,
quotes$upfront_curve[i, tenor])
quote <- list(ticker = quotes$ticker[i],
running = quotes$spread_curve[i, tenor] * 1e-4,
upfront = quotes$upfront_curve[i, tenor] * 0.01,
recovery = if(is.null(recov)) as.double(quotes$recovery[i,tenor][1])
else recov)
if(all(is.na(quote$upfront))){
loginfo(paste("no quotes available for", quote$ticker, "on day",
as.character(tradedate)))
## probably defaulted
if(!is.na(quote$recovery)) {
quote$upfront = (1-quote$recovery) * 0.9
} else {
index$portfolio <- NULL
return( index )
}
}
index$portfolio <- c(index$portfolio, buildSC(quote, cds.cs$cs, cds.cs$cdsdates))
}
index$issuerweights <- rep(1/length(index$portfolio), length(index$portfolio))
## group common curves
index$issuerweights <- aggregate(index$issuerweights,
by = list(unlist(lapply(index$portfolio, function(x)x@issuer))),
sum)[,2]
index$portfolio <- unique(index$portfolio)
index$recov <- sapply(index$portfolio, attr, "recovery")
return( index )
}
set.tranchedata <- function(index, tradedate){
if(is.null(index)){
stop("index is NULL")
}
temp <- get.tranchequotes(index$name, index$tenor, tradedate)
if(nrow(temp) == 0) {
stop(paste(index$name, "no quote for day", as.character(tradedate)))
}
index$quotes <- data.frame(maturity=index$maturity,
refspread=temp$indexrefspread[1],
refprice=temp$indexrefprice[1])
index$quotes$spread <- couponfromindex(index$name, index$tenor) * 1e-4
index$cs <- couponSchedule(IMMDate(tradedate, noadj=TRUE), index$maturity, "Q", "FIXED", 1,
0, tradedate, IMMDate(tradedate, "prev"))
if(!is.na(index$quotes$refprice) && index$quotes$refprice != 0) {
index$quotes$price <- index$quotes$refprice/100
} else {
index$quotes$price <- snacpv(index$cs, index$quotes$refspread[1]*1e-4, index$quotes$spread,
if (index$type %in% c("IG", "EU")) 0.4 else 0.3, tradedate)
}
if(length(indexpv(index, tradedate=tradedate)) == 1) {
stop("why am I stopping?")
}
tweak <- tweakcurves(index, tradedate)
index$portfolio <- NULL
index <- c(index, tweak)
index$defaultprob <- 1 - SPmatrix(index$portfolio, index$cs$dates)
negprob <- which(index$defaultprob < 0, arr.ind=T)
if(nrow(negprob) > 0) {
stop(paste(index$portfolio[[negprob[1,1]]]@issuer, "has negative probability, check single names data"))
}
K <- c(0, temp$detach/100)
index$K.orig <- K
index$K <- adjust.attachments(K, index$loss, index$factor)
## convert snac prices to proper upfront
if(index$type == "XO") {
coupon <- 500
temp$trancheupfrontmid[4] <- 100 * ( 1 - snacpv(index$cs, temp$trancherunningmid[4]*1e-4,
coupon*1e-4, 0.4, tradedate))
temp$trancherunningmid[4] <- coupon
}
if(index$type =="EU") {
if(index$series>=21){
coupon <- 100
temp$trancheupfrontmid[3] <- 100 * ( 1 - snacpv(index$cs, temp$trancherunningmid[3]*1e-4,
coupon*1e-4, 0, tradedate))
temp$trancheupfrontmid[4] <- 100 * ( 1 - snacpv(index$cs, temp$trancherunningmid[4]*1e-4,
coupon*1e-4, 0.4, tradedate))
temp$trancherunningmid[3:4] <- coupon
}else if(index$series == 9) {
for(i in 4:5){
coupon <- 100
temp$trancheupfrontmid[i] <- 100 * ( 1 - snacpv(index$cs, temp$trancherunningmid[i]*1e-4,
coupon*1e-4, 0, tradedate))
temp$trancherunningmid[i] <- coupon
}
coupon <- 25
temp$trancheupfrontmid[6] <- 100 * ( 1 - snacpv(index$cs, temp$trancherunningmid[6]*1e-4,
coupon*1e-4, 0.4, tradedate))
temp$trancherunningmid[6] <- coupon
}
}
## compute dirty protection price
accrued <- cdsAccrued(tradedate, temp$trancherunningmid*1e-4)
if(index$type == "HY"){
dirtyquotes <- 1 - temp$trancheupfrontmid/100 - accrued
}else{
dirtyquotes <- temp$trancheupfrontmid/100 - accrued
}
index$tranches <- data.frame(id = temp$id,
upfront=temp$trancheupfrontmid,
running=temp$trancherunningmid * 1e-4,
quotes=dirtyquotes,
mkt.delta=temp$tranchedelta,
row.names=paste(index$K.orig[-length(index$K.orig)]*100,
index$K.orig[-1]*100, sep="-"))
return( index )
}
build.skew <- function(index, type="bottomup"){
require(lossdistrib)
aux <- function(rho, index, K, quote, spread, complement){
temp <- BCtranche.legs(index, K, rho, complement)
return(abs(temp$pl+temp$cl*spread + quote))
}
rhovec <- rep(NA, length(index$K))
dK <- diff(index$K)
if(type=="bottomup"){
for(j in 1:(length(dK)-1)){
##use the current tranche coupon
## we compute the 0-index$K[j+1] equivalent quote using the coupon of the jth quote
tranchepv <- BCtranche.legs(index, index$K[j], rhovec[j])
q <- index$tranches$quotes[j] * dK[j] -
tranchepv$pl - tranchepv$cl*index$tranches$running[j]
rho <- optimize(aux, interval=c(0,1), index=index, K=index$K[j+1], quote=q,
spread=index$tranches$running[j], complement=FALSE)$minimum
rhovec[j+1] <- rho
}
}else if(type=="topdown"){
for(j in length(dK):2){
tranchepv <- BCtranche.legs(index, index$K[j+1], rhovec[j+1], complement=TRUE)
q <- index$tranche.quotes[j] * dK[j] -
tranchepv$pl - tranchepv$cl * index$tranches$running[j]
rho <- optimize(aux, interval=c(0,1), index=index, K=index$K[j], quote=q,
spread=index$tranches$running[j], complement=TRUE)$minimum
rhovec[j] <- rho
}
}
return( rhovec )
}
build.MFdist <- function(index, type="bottomup", tol=1e-2){
index$w.mod <- index$w
p <- index$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,], index$w, index$tranches$quotes[select])
err <- 0
for(i in 1:n.credit){
for(j in 1:ncol(p)){
err <- err + abs(crossprod(shockprob(p[i,j], rho[i],
index$Z), program$weight) - index$defaultprob[i,j])
}
}
## update the new probabilities
p <- MFupdate.prob(index$Z, program$weight, rho, index$defaultprob)
index$w.mod <- program$weight
cat("=")
}
cat("\n")
return(index$w.mod)
}
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