add two new MF functions

1 files changed, 30 insertions, 19 deletions

diff --git a/R/tranche_functions.R b/R/tranche_functions.R
index 39888d9..32a4859 100644
--- a/R/tranche_functions.R
+++ b/R/tranche_functions.R
@@ -251,7 +251,8 @@ lossdistCblas <- function(p, w, S, N, defaultflag=FALSE){
        as.double(w), as.double(S), as.integer(N), as.logical(defaultflag), q = double(N))$q

 }

 

-lossdistCZ <- function(p, w, S, N, defaultflag=FALSE, rho, Z, wZ){

+lossdistCZ <- function(p, w, S, N, defaultflag=FALSE, rho, Z){

+    #S is of size (length(p), length(Z))

     .C("lossdistrib_Z", as.double(p), as.integer(length(p)),

        as.double(w), as.double(S), as.integer(N), as.logical(defaultflag),

        as.double(rho), as.double(Z), as.integer(length(Z)),

@@ -282,7 +283,6 @@ lossdistC.jointblas <- function(p, w, S, N, defaultflag=FALSE){
        as.double(S), as.integer(N), as.logical(defaultflag), q = matrix(0, N, N))$q

 }

 

-

 lossdistC.jointZ <- function(dp, w, S, N, defaultflag = FALSE, rho, Z, wZ){

     ## N is the size of the grid

     ## dp is of size n.credits

@@ -604,7 +604,6 @@ tranche.pv.scenarios <- function(l, r, cs, K1, K2){
            tranche.cl.scenarios(l, r, cs, K1, K2, TRUE))

 }

 

-

 adjust.attachments <- function(K, losstodate, factor){

     ## computes the attachments adjusted for losses

     ## on current notional

@@ -997,24 +996,36 @@ MFlossdist.prepay.joint <- function(w, Z, rho, defaultprob, defaultprobmod,
   return( Q )

 }

 

-MFtranche.pv <- function(cl, cs, w, rho, defaultprob, defaultprobmod, issuerweights, recov,

-                         Kmodified, Ngrid=length(issuerweights)+1){

-    ## computes the tranches pv using the modified factor distribution

-    ## p is the modified probability so that

-    n.credit <- length(issuerweights)

-    Rstoch <- array(0, dim=c(n.int, n.credit, ncol(defaultprob)))

-    for(t in 1:ncol(defaultprob)){

+MFrecovery <- function(index, defaultprobmod){

+    n.credit <- length(index$issuerweights)

+    n.int <- length(index$Z)

+    Rstoch <- array(0, dim=c(n.credit, n.int, ncol(index$defaultprob)))

+    rho <- rep(0.45, n.credit)

+    for(t in 1:ncol(index$defaultprob)){

         for(i in 1:n.credit){

-            Rstoch[,i,t] <- stochasticrecov(recov[i], 0, Z, w, rho, defaultprob[i,t], defaultprobmod[i,t])

+            Rstoch[i,,t] <- stochasticrecovC(index$recov[i], 0, index$Z, index$w.mod,

+                                             rho, index$defaultprob[i,t], defaultprobmod[i,t])

         }

     }

-    parf <- function(i){

-        pshocked <- apply(defaultprobmod, 2, shockprob, rho=rho, Z=Z[i])

-        S <- 1 - Rstoch[i,,]

-        dist <- lossrecovdist.term(pshocked, 0, issuerweights, S, Ngrid)

-        return( tranche.pvvec(Kmodified, dist$L, dist$R, cs))

+    return( Rstoch )

+}

+

+MFdist <- function(index){

+    rho <- rep(0.45, n.credit)

+    defaultprobmod <- MFupdate.probC(index$Z, index$w.mod, rho, index$defaultprob)

+    n.credit <- length(index$issuerweights)

+    n.int <- length(index$Z)

+    Rstoch <- MFrecovery(index, defaultprobmod)

+    Lw <- matrix(0, index$N, n.int)

+    Rw <- matrix(0, index$N, n.int)

+    L <- matrix(0, index$N, ncol(index$defaultprob))

+    R <- matrix(0, index$N, ncol(index$defaultprob))

+    for(t in 1:ncol(index$defaultprob)){

+        S <- 1 - Rstoch[,,t]

+        Lw <- lossdistCZ(defaultprobmod[,t], index$issuerweights, S, index$N, 0, rho, index$Z)

+        Rw <- lossdistCZ(defaultprobmod[,t], index$issuerweights, 1-S, Ngrid, 0, rho, index$Z)

+        L[,t] <- Lw%*%index$w.mod

+        R[,t] <- Rw%*%index$w.mod

     }

-    clusterExport(cl, list("Rstoch", "p"), envir=environment())

-    result <- parSapply(cl, 1:length(w), parf)

-    return( list(pv=100*(1+result%*%w), pv.w=result))

+    return(list(L=L, R=R))

 }