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library(ggplot2)
date <- Sys.Date()
root.dir <- Sys.getenv("BASE_DIR")
dists <- list()
for(deal in c('ares29', 'bally131')) {
load(file.path(root.dir, "Scenarios", sprintf("Intex Curves_%s", date), "csv", paste(deal, "RData", sep=".")))
dists[[deal]] <- dist.joint
}
support <- seq(0, 1, length = 201)
##5 year
ggplot() +
geom_line(aes(x=support, y=rowSums(dists$ares29[17,,]), colour='Ares XXIX CLO')) +
geom_line(aes(x=support, y=rowSums(dists$bally131[17,,]), colour='Ballyrock CLO 2013-1')) +
ylab("loss density")+xlab("percentage of loss") +
geom_vline(aes(colour='Market assumption', xintercept=0.03)) +
annotate("text", x=0.5, y=0.025, label="T=5 year", size=4) +
annotate("text", x=0.5, y=0, label="2 CDR, 70% recovery", colour="#F8766D", size=4) +
labs(colour="") +
theme(legend.position="bottom")
ggsave("Loss distribution.png", width=4.35, height=4)
##ggsave("Loss distribution.png")
tradedate <- as.Date("2016-10-19")
calibration.date <- as.Date("2016-10-18")
load(file.path(root.dir, "Scenarios", "Calibration", sprintf("marketdata-%s.RData", calibration.date)))
load(file.path(root.dir, "Scenarios", paste0("Prices_", tradedate), "cashflows.RData"))
getcusip.pv <- function(cusip, dealname, cusipdata, date) {
indic.data <- getcusip_indicdata(cusip, dealname, date)
cusip.pv <- cusipdata[[cusip]]$fields[,"Cashflow"]/indic.data$curr_balance
return( cusip.pv )
}
index.pv <- function(indexdist, dealdata) {
dealweight <- dealdata$weight
dealprice <- dealdata$price
nT <- dim(indexdist$L)[2]
Ngrid <- dim(indexdist$L)[1]
scenariosl <- matrix(0, length(dealweight), nT)
scenariosr <- matrix(0, length(dealweight), nT)
for(t in 1:nT){
scenariosl[,t] <- interpvalues(indexdist$L[,t], seq(0, 1, length=Ngrid), dealweight)
## numerical artefact, but we want scenariosr[i,] + scenariosl[i,] <= 1 at all times
scenariosr[,t] <- pmin(interpvalues(indexdist$R[,t], seq(0, 1, length=Ngrid), dealweight),
1-scenariosl[,t])
}
## we assume the index is fully funded - need to be changed depending
## on how we fund the swaps (hence floating coupon instead of fixed)
indexpv <- c()
for(i in 1:length(dealweight)){
indexpv <- c(indexpv, funded.tranche.pv(scenariosl[i,], scenariosr[i,], cs, 0, 1, TRUE))
}
return(indexpv)
}
getcusip_indicdata <- function(Cusip, dealname, date){
sqlstr <- "SELECT DISTINCT isin FROM cusip_universe WHERE cusip=$1"
r <- tryCatch(dbGetQuery(etdb, sqlstr, params=list(Cusip)),
error = function(w) logerror(w$message))
if(length(r$isin)>1){
stop("We have a problem")
}
isinval <- r$isin[1]
sqlstr <- "SELECT * FROM historical_dealname_universe($1, $2)"
r <- tryCatch(dbGetQuery(etdb, sqlstr, params = list(dealname, date)),
error = function(w) logerror(w$message))
if(!is.na(isinval)){
return(r %>% group_by(isin) %>% slice(1) %>%
summarize(cusip, curr_balance, orig_balance, spread, curr_attach) %>%
arrange(desc(curr_attach)) %>%
mutate(cum_bal = cumsum(curr_balance)) %>% filter(isin==isinval) )
}else{
return(r %>% select(cusip, curr_balance, orig_balance, spread, curr_attach) %>%
arrange(desc(curr_attach)) %>%
mutate(cum_bal = cumsum(curr_balance)) %>% filter(cusip==Cusip) )
}
}
tradedate <- as.Date("2016-10-19")
dealname <- "bally131"
hy.pv <- index.pv(dist, cfdata[[dealname]])
bally131 <- data.frame(cusips = c("05874PAA4", "05874PAC0", "05874PAE6", "05874PAG1", "05874QAC8", "05874QAA2"),
name = c("A", "B", "C", "D", "E", "SUB"),
rating = c("AAA", "AA", "A", "BBB", "BB", "NA"))
support <- cumsum(cfdata[[dealname]]$weight) * 100
df <- data.frame(x =support, pv = hy.pv * 100, name = "HY27 5Y")
for(i in 1:nrow(bally131)) {
df <- rbind(df,
data.frame(x =support,
pv = getcusip.pv(bally131$cusips[i], dealname, cusipdata, date)*100,
name = bally131$rating[i]))
}
ggplot(df) +
geom_line(aes(x=x, y =pv, group = name, colour = factor(name))) +
ylab("Expected price")+xlab("scenario percentile") +
theme(legend.position="bottom")
for(cusip in cusips ) {
cusip.pv <- getcusip.pv(cusip, dealname, cusipdata, date)
plot <- plot + geom_line(aes(x=support, y =cusip.pv * 100,
colour=sprintf('Ballyrock CLO 2013-1 %s', cusip)))
}
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