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library(RQuantLib)
library(statmod)
root = "//WDSENTINEL/share/CorpCDOs/"
source(file.path(root, "R", "yieldCurve.R"))
source(file.path(root, "R", "cds_functions_generic.R"))
source(file.path(root, "R", "etdb.R"))
source(file.path(root, "R", "tranche_functions.R"))
load(file.path(root, "R", "bloomberg_data.RData"))
cdorating <- function(cusip){
return( sub("[0-9]","", dataMtge[dataMtge$CUSIP %in% cusip,]$RTG_MDY_INITIAL ))
}
getcollateral <- function(dealname, date=Sys.Date()){
sqlstring <- sprintf("select * from et_aggdealinfo_historical('%s', '%s')", dealname, date)
collatdata <- dbGetQuery(dbCon, sqlstring)
return(collatdata)
}
getdealdata <- function(dealnames){
sqlstring <- sprintf("select * from latest_clo_universe where dealname in ('%s')",
paste(dealnames, collapse="','"))
return( dbGetQuery(dbCon, sqlstring) )
}
recovery <- function(collateral) {
## return assumed recovery based on assumptions from recovery.assumptions
if(!is.na(collateral$secondlien) && collateral$secondlien){
collateral$assettype <- "SecondLien"
}
recovery <- with(global.params,
as.numeric(recovery.assumptions[collateral$assettype]))
if( !is.na(collateral$covlite) && collateral$covlite) {
recovery <- recovery - global.params$recovery.assumptions$Adj_Covlite
}
if( !is.na(collateral$iscdo) && collateral$iscdo ){
recovery <- 0
}
## price is too low need to lower the assumed recovery
if(!is.na(collateral$price) && recovery > collateral$price/100 - 0.1){
recovery <- max(collateral$price/100-0.2, 0)
}
return(recovery)
}
dealnamefromcusip <- function(cusips){
## wrapper around the sql procedure, not the fastest probably
r <- NULL
for(i in 1:length(cusips)){
sqlstr <- sprintf("select * from dealname_from_cusip('%s')", cusips[i])
r <- c(r, as.character(dbGetQuery(dbCon, sqlstr)))
}
return( r )
}
cusipsfromdealnames <- function(dealnames){
unlist(strsplit(getdealdata(dealnames)$"Deal Cusip List", ","))
}
fithazardrate.fast <- function(collateral, eps=1e-6){
lambda <- 0.05
cs <- couponSchedule(collateral$nextpaydate, collateral$maturity,
collateral$frequency, collateral$fixedorfloat,
collateral$grosscoupon * 0.01, collateral$spread*0.01)
R <- recovery(collateral)
while(abs(bondprice(lambda, cs, R) * 100 - collateral$price) > eps){
lambda <- lambda - (bondprice(lambda, cs, R) - 0.01*collateral$price)/dbondprice(lambda, cs, R)
}
return( lambda )
}
vanillabondprice <- function(h, collateral, prepay=TRUE) {
R <- recovery(collateral)
cs <- couponSchedule(collateral$nextpaydate, collateral$maturity,
collateral$frequency, collateral$fixedorfloat,
collateral$grosscoupon*0.01, collateral$spread*0.01)
if(prepay){
dpc <- new("defaultprepaycurve", dates=cs$dates, hazardrates=rep(h,length(cs$dates)),
prepayrates=rep(k(h), length(cs$dates)))
}else{
dpc <- new("defaultprepaycurve", dates=cs$dates, hazardrates=rep(h,length(cs$dates)),
prepayrates=numeric(0))
}
return( bondprice(cs, dpc, R) )
}
dvanillabondprice <- function(hazardrate, collateral) {
R <- recovery(collateral)
cs <- couponSchedule(collateral$nextpaydate, collateral$maturity,
collateral$frequency, collateral$fixedorfloat,
collateral$grosscoupon*0.01, collateral$spread*0.01)
return( bondprice(hazardrate, cs, R) )
}
fithazardrate <- function(collateral){
R <- recovery(collateral)
cs <- couponSchedule(collateral$nextpaydate, collateral$maturity,
collateral$frequency, collateral$fixedorfloat,
collateral$grosscoupon*0.01, collateral$spread*0.01)
f <- function(lambda){
u <- bondprice(lambda, cs, R )
return( (u * 100-collateral$price)^2 )
}
return( optimize(f, c(0,1), tol=1e-6)$minimum )
}
maturity <- function(creditcurve){
if(class(creditcurve)=="creditcurve"){
dates <- creditcurve@curve@dates
if(length(dates)){
return( dates[length(dates)] )
}else{
return( creditcurve@startdate )
}
}else{
stop("not of class creditcurve")
}
}
stackcurve <- function(SC, line.item, global.params, startdate){
newdates <- seq(startdate, line.item$maturity, by="3 months")
if(line.item$assettype=="Loan"){
hvec <- global.params$shape(yearFrac(today(), newdates[-1])) * global.params$defaultloanhazardrate
kvec <- global.params$alpha * exp(-global.params$beta * hvec)
}else if(line.item$assettype=="Bond" || line.item$iscdo){
hvec <- global.params$shape(yearFrac(today(), newdates[-1])) * global.params$defaultbondhazardrate
kvec <- rep(0, length(hvec))
}
SC@curve@hazardrates <- c(SC@curve@hazardrates, hvec)
SC@curve@prepayrates <- c(SC@curve@prepayrates, kvec)
SC@curve@dates <- c(SC@curve@dates, newdates[-1])
return(SC)
}
buildSC.matured <- function(SC, line.item, reinvdate, dealmaturity, global.params, startdate){
if(startdate<=reinvdate){ #reinvest
line.item$maturity <- min(dealmaturity, startdate + global.params$rollingmaturity)
SC <- stackcurve(SC, line.item, global.params, startdate)
}else{ #no reinvestment
SC@curve@dates <- startdate
SC@curve@hazardrates <- 0
SC@curveprepayrates <- 0
}
return( SC )
}
buildSC <- function(line.item, reinvdate, dealmaturity, global.params, startdate){
## cat(line.item$issuername, "\n")
if(!is.na(line.item$iscdo) && line.item$iscdo && is.na(line.item$price)){
##we have prices for some cdos e.g. 210795PS3
orig.moody <- cdorating(line.item$cusip)
if(length(orig.moody)==0){
orig.moody <- "NR"
}
line.item$price <- as.numeric(global.params$cdoprices[orig.moody])
}
##build survival curve
SC <- new("creditcurve", recovery=recovery(line.item), startdate=startdate,
issuer=line.item$issuername)
SC@curve <- new("defaultprepaycurve", dates=as.Date(character(0)))
## defaulted asset
if(!is.na(line.item$defaultedflag) && line.item$defaultedflag){
if(!is.na(line.item$price)){
line.item$currentbalance <- line.item$currentbalance * line.item$price/100
}else{
line.item$currentbalance <- line.item$currentbalance * recovery(line.item)
}
SC@startdate <- startdate + global.params$defaultedlag
line.item$maturity <- min(dealmaturity, SC@startdate + global.params$rollingmaturity)
## automatic reinvest
SC<- stackcurve(SC, line.item, global.params, SC@startdate)
}else if(line.item$maturity<=startdate){#matured asset
SC <- buildSC.matured(SC, line.item, reinvdate, dealmaturity, global.params, startdate)
}else if(is.na(line.item$price)){ #missing price
SC <- stackcurve(SC, line.item, global.params, SC@startdate)
}else{ #normal case
if(line.item$assettype=="Bond"){ #no prepay rate
alpha <- 0
}else{
alpha <- global.params$alpha
}
try <- bondhazardrate.shaped(line.item, global.params$shape,
recovery(line.item), alpha)
if(is.null(try)){
SC <- stackcurve(SC, line.item, global.params, SC@startdate)
}else{
SC@curve <- try
}
}
if(maturity(SC) <= reinvdate){ #we reinvest
newstartdate <- line.item$maturity
line.item$maturity <- min(dealmaturity, newstartdate + global.params$rollingmaturity)
SC <- stackcurve(SC, line.item, global.params, newstartdate)
}
return( list(SC=SC, notional=line.item$currentbalance) )
}
buildSC.portfolio <- function(dealname, global.params, startdate=today()) {
dealdata <- getdealdata(dealname)
collatdata <- getcollateral(dealname)
notionalvec <- c()
SCvec <- c()
betavec <- c()
for(i in 1:nrow(collatdata)){
line.item <- collatdata[i,]
if( is.na(line.item$maturity) ){
stop("empty maturity")
}
##most likely equity, doesn't impact the risk anyway
if(line.item$currentbalance < 1){
next
}
temp <- buildSC(line.item, dealdata$"Reinv End Date", dealdata$maturity, global.params, startdate)
notionalvec <- c(notionalvec, temp$notional)
SCvec <- c(SCvec, temp$SC)
betavec <- c(betavec, if(!is.na(line.item$iscdo) && line.item$iscdo) 1 else
global.params$defaultcorr)
}
return( list(notional=notionalvec, SC=SCvec, beta=betavec) )
}
cdrfromscenarios <- function(scenarios, dates){
## compute the forward cdr rates to pass to intex
## so that we match the default curves in scenarios
cdr <- matrix(0, nrow(scenarios), ncol(scenarios))
for(i in 1:nrow(scenarios)){
cdr[i,] <- 100*(1-exp(diff(c(0, log(1-scenarios[i,])))))/diff(c(0, yearFrac(today(), dates)))
}
return( cdr )
}
recoveryfromscenarios <- function(scenariosd, scenariosr){
## compute the forward recovery rate based on the term
## structure of recovery scenarios
## we run into trouble for very stressed scenarios
## this code should cap the scenarios at 0 it this happens
intexrecov <- matrix(0, n.scenarios, ncol(scenariosr))
for(i in 1:n.scenarios){
current <- 1
intexrecov[i,1] <- scenariosr[i,1]
for(t in 2:ncol(scenariosr)){
w <- scenariosd[i,current]/scenariosd[i,t]
if(scenariosr[i,t]-w*scenariosr[i,current]>=0){
intexrecov[i,t] <- (scenariosr[i,t]-w*scenariosr[i,current])/(1-w)
current <- current+1
}else{
intexrecov[i,t] <- 0
}
}
}
return(intexrecov)
}
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