rename file since there are no tranche functions left

1 files changed, 0 insertions, 550 deletions

diff --git a/R/tranche_functions.R b/R/tranche_functions.R
deleted file mode 100644
index 8b10394..0000000
--- a/R/tranche_functions.R
+++ /dev/null
@@ -1,550 +0,0 @@
-## todo:

-## -investigate other ways to interpolate the random severities on the grid

-## I'm thinking that at eah severity that we add to the distribution, round it down

-##  and keep track of the missing mass: namely if X_i=S_i w.p p_i, then add

-## X_i=lu*floor(S_i/lu) with probability p_i and propagate

-## X_{i+1}=S_{i+1}+(S_i-lu*floor(S_i/lu)) with the right probability

-## - investigate truncated distributions more (need to compute loss and recov distribution

-## separately, for the 0-10 equity tranche, we need the loss on the 0-0.1 support and

-## recovery with 0.1-1 support, so it's not clear that there is a big gain.

-## - do the correlation adjustments when computing the deltas since it seems to be

-## the market standard

-

-#' Gauss-Hermite quadrature weights

-#'

-#' \code{GHquad} computes the quadrature weights for integrating against a

-#' Gaussian distribution.

-#'

-#' if f is a function, then with(GHquad(100), crossprod(f(Z), w))

-#'

-#' @param n Integer, the number of nodes

-#' @return A list with two components:

-#'  \item{Z}{the list of nodes}

-#'  \item{w}{the corresponding weights}

-#'

-GHquad <- function(n){

-    n <- as.integer(n)

-    Z <- double(n)

-    w <- double(n)

-    result <- .C("GHquad", n, Z=Z, w=w)

-    result[[1]] <- NULL

-    return(result)

-}

-

-#' Loss distribution of a portfolio

-#'

-#' \code{lossdistrib} computes the probability distribution of a sum

-#' of independent Bernouilli variables with unequal probabilities.

-#'

-#' This uses the basic recursive algorithm of Andersen, Sidenius and Basu

-#' We compute the probability distribution of S = \sum_{i=1}^n X_i

-#' where X_i is Bernouilli(p_i)

-#' @param p Numeric vector, the vector of success probabilities

-#' @return A vector q such that q[k]=P(S=k)

-lossdistrib <- function(p){

-    ## basic recursive algorithm of Andersen, Sidenius and Basu

-    n <- length(p)

-    q <- rep(0, (n+1))

-    q[1] <- 1

-    for(i in 1:n){

-        q[-1] <- p[i]*q[-(n+1)]+(1-p[i])*q[-1]

-        q[1] <- (1-p[i])*q[1]

-    }

-    return(q)

-}

-

-#' Loss distribution of a portfolio

-#'

-#' \code{lossdistrib.fft} computes the probability distribution of a sum

-#' of independent Bernouilli variables with unequal probabilities.

-#'

-#' This uses the fft. Complexity is of order O(n m) + O(m\log{m})

-#' where m is the size of the grid and n, the number of probabilities.

-#' It is slower than the recursive algorithm in practice.

-#' We compute the probability distribution of S = \sum_{i=1}^n X_i

-#' where X_i is Bernouilli(p_i)

-#' @param p Numeric vector, the vector of success probabilities

-#' @return A vector such that q[k]=P(S=k)

-lossdistrib.fft <- function(p){

-    n <- length(p)

-    theta <- 2*pi*1i*(0:n)/(n+1)

-    Phi <- 1 - p + p%o%exp(theta)

-    v <- apply(Phi, 2, prod)

-    return(1/(n+1)*Re(fft(v)))

-}

-

-#' recursive algorithm with first order correction

-#'

-#' @param p Numeric, vector of default probabilities

-#' @param w Numeric, vector of weights

-#' @param S Numeric, vector of severities

-#' @param N Integer, number of ticks in the grid

-#' @param defaultflag Boolean, if True, we compute the default distribution

-#' (instead of the loss distribution).

-#' @return a Numeric vector of size \code{N} computing the loss (resp.

-#' default) distribution if \code{defaultflag} is FALSE (resp. TRUE).

-lossdistrib2 <- function(p, w, S, N, defaultflag=FALSE){

-    n <- length(p)

-    lu <- 1/(N-1)

-    q <- rep(0, N)

-    q[1] <- 1

-    for(i in 1:n){

-        if(defaultflag){

-            d <- w[i] /lu

-        }else{

-            d <- S[i] * w[i] / lu

-        }

-        d1 <- floor(d)

-        d2 <- ceiling(d)

-        p1 <- p[i]*(d2-d)

-        p2 <- p[i] - p1

-        q1 <- c(rep(0,d1), p1*q[1:(N-d1)])

-        q2 <- c(rep(0,d2), p2*q[1:(N-d2)])

-        q <- q1 + q2 + (1-p[i])*q

-    }

-    q[length(q)] <- q[length(q)]+1-sum(q)

-    return(q)

-}

-

-lossdistrib2.truncated <- function(p, w, S, N, cutoff=N){

-    ## recursive algorithm with first order correction

-    ## p vector of default probabilities

-    ## w vector of weigths

-    ## S vector of severities

-    ## N number of ticks in the grid (for best accuracy should

-    ## be a multiple of the number of issuers)

-    ## cutoff where to stop computing the exact probabilities

-    ## (useful for tranche computations)

-

-    ## this is actually slower than lossdistrib2. But in C this is

-    ## twice as fast.

-    ## for high severities, M can become bigger than N, and there is

-    ## some probability mass escaping.

-    n <- length(p)

-    lu <- 1/(N-1)

-    q <- rep(0, truncated)

-    q[1] <- 1

-    M <- 1

-    for(i in 1:n){

-        d <- S[i] * w[i] / lu

-        d1 <- floor(d)

-        d2 <- ceiling(d)

-        p1 <- p[i]*(d2-d)

-        p2 <- p[i] - p1

-        q1 <- p1*q[1:min(M, cutoff-d1)]

-        q2 <- p2*q[1:min(M, cutoff-d2)]

-        q[1:min(M, cutoff)] <-  (1-p[i])*q[1:min(M, cutoff)]

-        q[(d1+1):min(M+d1, cutoff)] <- q[(d1+1):min(M+d1, cutoff)]+q1

-        q[(d2+1):min(M+d2, cutoff)] <- q[(d2+1):min(M+d2, cutoff)]+q2

-        M <- M+d2

-    }

-    return(q)

-}

-

-recovdist <- function(dp, pp, w, S, N){

-    ## computes the recovery distribution for a sum of independent variables

-    ## R=\sum_{i=1}^n w[i] X_i

-    ## where X_i = 0             w.p 1 - dp[i] - pp[i]

-    ##           = 1 - S[i]      w.p dp[i]

-    ##           = 1             w.p pp[i]

-    ## each non zero value v is interpolated on the grid as

-    ## the pair of values floor(v/lu) and ceiling(v/lu) so that

-    ## X_i has four non zero values

-    n <- length(dp)

-    q <- rep(0, N)

-    q[1] <- 1

-    lu <- 1/(N-1)

-    for(i in 1:n){

-        d1 <- w[i]*(1-S[i])/lu

-        d1l <- floor(d1)

-        d1u <- ceiling(d1)

-        d2 <- w[i] / lu

-        d2l <- floor(d2)

-        d2u <- ceiling(d2)

-        dp1 <- dp[i] * (d1u-d1)

-        dp2 <- dp[i] - dp1

-        pp1 <- pp[i] * (d2u - d2)

-        pp2 <- pp[i] - pp1

-        q1 <- c(rep(0, d1l), dp1 * q[1:(N-d1l)])

-        q2 <- c(rep(0, d1u), dp2 * q[1:(N-d1u)])

-        q3 <- c(rep(0, d2l), pp1 * q[1:(N-d2l)])

-        q4 <- c(rep(0, d2u), pp2 *q[1:(N-d2u)])

-        q <- q1+q2+q3+q4+(1-dp[i]-pp[i])*q

-    }

-    return(q)

-}

-

-lossdist.joint <- function(p, w, S, N, defaultflag=FALSE){

-    ## recursive algorithm with first order correction

-    ## to compute the joint probability distribution of the loss and recovery

-    ## inputs:

-    ##   p: vector of default probabilities

-    ##   w: vector of issuer weights

-    ##   S: vector of severities

-    ##   N: number of tick sizes on the grid

-    ##   defaultflag: if true computes the default distribution

-    ## output:

-    ##   q: matrix of joint loss, recovery probability

-    ##      colSums(q) is the recovery distribution marginal

-    ##      rowSums(q) is the loss distribution marginal

-    n <- length(p)

-    lu <- 1/(N-1)

-    q <- matrix(0, N, N)

-    q[1,1] <- 1

-    for(k in 1:n){

-        if(defaultflag){

-            x <- w[k] / lu

-        }else{

-            x <- S[k] * w[k]/lu

-        }

-        y <- (1-S[k]) * w[k]/lu

-        i <- floor(x)

-        j <- floor(y)

-        weights <- c((i+1-x)*(j+1-y), (i+1-x)*(y-j), (x-i)*(y-j), (j+1-y)*(x-i))

-        psplit <- p[k] * weights

-        qtemp <- matrix(0, N, N)

-        qtemp[(i+1):N,(j+1):N] <- qtemp[(i+1):N,(j+1):N] + psplit[1] * q[1:(N-i),1:(N-j)]

-        qtemp[(i+1):N,(j+2):N] <- qtemp[(i+1):N,(j+2):N] + psplit[2] * q[1:(N-i), 1:(N-j-1)]

-        qtemp[(i+2):N,(j+2):N] <- qtemp[(i+2):N,(j+2):N] + psplit[3] * q[1:(N-i-1), 1:(N-j-1)]

-        qtemp[(i+2):N, (j+1):N] <- qtemp[(i+2):N, (j+1):N] + psplit[4] * q[1:(N-i-1), 1:(N-j)]

-        q <- qtemp + (1-p[k])*q

-    }

-    q[length(q)] <- q[length(q)]+1-sum(q)

-    return(q)

-}

-

-lossdist.prepay.joint <- function(dp, pp, w, S, N, defaultflag=FALSE){

-    ## recursive algorithm with first order correction

-    ## to compute the joint probability distribition of the loss and recovery

-    ## inputs:

-    ##   dp: vector of default probabilities

-    ##   pp: vector of prepay probabilities

-    ##   w:  vector of issuer weights

-    ##   S:   vector of severities

-    ##   N: number of tick sizes on the grid

-    ##   defaultflag: if true computes the default

-    ## outputs

-    ##   q: matrix of joint loss and recovery probability

-    ##      colSums(q) is the recovery distribution marginal

-    ##      rowSums(q) is the loss distribution marginal

-    n <- length(dp)

-    lu <- 1/(N-1)

-    q <- matrix(0, N, N)

-    q[1,1] <- 1

-    for(k in 1:n){

-        y1 <- (1-S[k]) * w[k]/lu

-        y2 <- w[k]/lu

-        j1 <- floor(y1)

-        j2 <- floor(y2)

-        if(defaultflag){

-            x <- y2

-            i <- j2

-        }else{

-            x <- y2-y1

-            i <- floor(x)

-        }

-

-        ## weights <- c((i+1-x)*(j1+1-y1), (i+1-x)*(y1-j1), (x-i)*(y1-j1), (j1+1-y1)*(x-i))

-        weights1 <- c((i+1-x)*(j1+1-y1), (i+1-x)*(y1-j1), (x-i)*(y1-j1), (j1+1-y1)*(x-i))

-        dpsplit <- dp[k] * weights1

-

-        if(defaultflag){

-            weights2 <- c((i+1-x)*(j2+1-y2), (i+1-x)*(y2-j2), (x-i)*(y2-j2), (j2+1-y2)*(x-i))

-            ppsplit <- pp[k] * weights2

-        }else{

-            ppsplit <- pp[k] * c(j2+1-y2, y2-j2)

-        }

-        qtemp <- matrix(0, N, N)

-        qtemp[(i+1):N,(j1+1):N] <- qtemp[(i+1):N,(j1+1):N] + dpsplit[1] * q[1:(N-i),1:(N-j1)]

-        qtemp[(i+1):N,(j1+2):N] <- qtemp[(i+1):N,(j1+2):N] + dpsplit[2] * q[1:(N-i), 1:(N-j1-1)]

-        qtemp[(i+2):N,(j1+2):N] <- qtemp[(i+2):N,(j1+2):N] + dpsplit[3] * q[1:(N-i-1), 1:(N-j1-1)]

-        qtemp[(i+2):N,(j1+1):N] <- qtemp[(i+2):N, (j1+1):N] + dpsplit[4] * q[1:(N-i-1), 1:(N-j1)]

-        if(defaultflag){

-            qtemp[(i+1):N,(j2+1):N] <- qtemp[(i+1):N,(j2+1):N] + ppsplit[1] * q[1:(N-i),1:(N-j2)]

-            qtemp[(i+1):N,(j2+2):N] <- qtemp[(i+1):N,(j2+2):N] + ppsplit[2] * q[1:(N-i), 1:(N-j2-1)]

-            qtemp[(i+2):N,(j2+2):N] <- qtemp[(i+2):N,(j2+2):N] + ppsplit[3] * q[1:(N-i-1), 1:(N-j2-1)]

-            qtemp[(i+2):N,(j2+1):N] <- qtemp[(i+2):N, (j2+1):N] + ppsplit[4] * q[1:(N-i-1), 1:(N-j2)]

-        }else{

-            qtemp[, (j2+1):N] <- qtemp[,(j2+1):N]+ppsplit[1]*q[,1:(N-j2)]

-            qtemp[, (j2+2):N] <- qtemp[,(j2+2):N]+ppsplit[2]*q[,1:(N-j2-1)]

-        }

-        q <- qtemp + (1-pp[k]-dp[k]) * q

-    }

-    q[length(q)] <- q[length(q)] + 1 - sum(q)

-    return(q)

-}

-

-lossdistC <- function(p, w, S, N, defaultflag=FALSE){

-    ## C version of lossdistrib2, roughly 50 times faster

-    .C("lossdistrib", as.double(p), as.integer(length(p)),

-       as.double(w), as.double(S), as.integer(N), as.logical(defaultflag), q = double(N))$q

-}

-

-lossdistCblas <- function(p, w, S, N, defaultflag=FALSE){

-    ## C version of lossdistrib2, roughly 50 times faster

-    .C("lossdistrib_blas", as.double(p), as.integer(length(p)),

-       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){

-    #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)),

-       q = matrix(0, N, length(Z)))$q

-}

-

-lossdistC.truncated <- function(p, w, S, N, T=N){

-    ## C version of lossdistrib2, roughly 50 times faster

-    .C("lossdistrib_truncated", as.double(p), as.integer(length(p)),

-       as.double(w), as.double(S), as.integer(N), as.integer(T), q = double(T))$q

-}

-

-recovdistC <- function(dp, pp, w, S, N){

-    ## C version of recovdist

-    .C("recovdist", as.double(dp), as.double(pp), as.integer(length(dp)),

-       as.double(w), as.double(S), as.integer(N), q = double(N))$q

-}

-

-lossdistC.joint <- function(p, w, S, N, defaultflag=FALSE){

-    ## C version of lossdistrib.joint, roughly 20 times faster

-    .C("lossdistrib_joint", as.double(p), as.integer(length(p)), as.double(w),

-       as.double(S), as.integer(N), as.logical(defaultflag), q = matrix(0, N, N))$q

-}

-

-lossdistC.jointblas <- function(p, w, S, N, defaultflag=FALSE){

-    ## C version of lossdistrib.joint, roughly 20 times faster

-    .C("lossdistrib_joint_blas", as.double(p), as.integer(length(p)), as.double(w),

-       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

-    ## w is of size n.credits

-    ## S is of size n.credits by nZ

-    ## rho is a double

-    ## Z is a vector of length nZ

-    ## w is a vector if length wZ

-    r <- .C("lossdistrib_joint_Z", as.double(dp), as.integer(length(dp)),

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

-            as.double(Z), as.double(wZ), as.integer(length(Z)), q = matrix(0, N, N))$q

-}

-

-lossdistC.prepay.jointblas <- function(dp, pp, w, S, N, defaultflag=FALSE){

-    ## C version of lossdist.prepay.joint

-    r <- .C("lossdistrib_prepay_joint_blas", as.double(dp), as.double(pp), as.integer(length(dp)),

-            as.double(w), as.double(S), as.integer(N), as.logical(defaultflag), q=matrix(0, N, N))$q

-    return(r)

-}

-

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

-    ## N is the size of the grid

-    ## dp is of size n.credits

-    ## pp is of size n.credits

-    ## w is of size n.credits

-    ## S is of size n.credits by nZ

-    ## rho is a vector of doubles of size n.credits

-    ## Z is a vector of length nZ

-    ## w is a vector if length wZ

-

-    r <- .C("lossdistrib_prepay_joint_Z", as.double(dp), as.double(pp), as.integer(length(dp)),

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

-            as.double(Z), as.double(wZ), as.integer(length(Z)), output = matrix(0,N,N))

-    return(r$output)

-}

-

-lossrecovdist <- function(defaultprob, prepayprob, w, S, N, defaultflag=FALSE, useC=TRUE){

-    lossdistrib2 <- if(useC) lossdistC

-    recovdist <- if(useC) recovdistC

-    if(missing(prepayprob)){

-        L <- lossdistrib2(defaultprob, w, S, N, defaultflag)

-        R <- lossdistrib2(defaultprob, w, 1-S, N)

-    }else{

-        L <- lossdistrib2(defaultprob+defaultflag*prepayprob, w, S, N, defaultflag)

-        R <- recovdist(defaultprob, prepayprob, w, S, N)

-    }

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

-}

-

-lossrecovdist.term <- function(defaultprob, prepayprob, w, S, N, defaultflag=FALSE, useC=TRUE){

-    ## computes the loss and recovery distribution over time

-    L <- array(0, dim=c(N, ncol(defaultprob)))

-    R <- array(0, dim=c(N, ncol(defaultprob)))

-    if(missing(prepayprob)){

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

-            temp <- lossrecovdist(defaultprob[,t], , w, S[,t], N, defaultflag, useC)

-            L[,t] <- temp$L

-            R[,t] <- temp$R

-        }

-    }else{

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

-            temp <- lossrecovdist(defaultprob[,t], prepayprob[,t], w, S[,t], N, defaultflag, useC)

-            L[,t] <- temp$L

-            R[,t] <- temp$R

-        }

-    }

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

-}

-

-lossrecovdist.joint.term <- function(defaultprob, prepayprob, w, S, N, defaultflag=FALSE, useC=TRUE){

-    ## computes the joint loss and recovery distribution over time

-    Q <- array(0, dim=c(ncol(defaultprob), N, N))

-    lossdist.joint <- if(useC) lossdistC.jointblas

-    lossdist.prepay.joint <- if(useC) lossdistC.prepay.jointblas

-    if(missing(prepayprob)){

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

-            Q[t,,] <- lossdist.joint(defaultprob[,t], w, S[,t], N, defaultflag)

-        }

-    }else{

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

-            Q[t,,] <- lossdist.prepay.jointblas(defaultprob[,t], prepayprob[,t], w, S[,t], N, defaultflag)

-        }

-    }

-    return(Q)

-}

-

-dist.transform <- function(dist.joint){

-    ## compute the joint (D, R) distribution

-    ## from the (L, R) distribution using D = L+R

-    distDR <- array(0, dim=dim(dist.joint))

-    Ngrid <- dim(dist.joint)[2]

-    for(t in 1:dim(dist.joint)[1]){

-        for(i in 1:Ngrid){

-            for(j in 1:Ngrid){

-                index <- i+j

-                if(index <= Ngrid){

-                    distDR[t,index,j] <- distDR[t,index,j] + dist.joint[t,i,j]

-                }else{

-                    distDR[t,Ngrid,j] <- distDR[t,Ngrid,j] +

-                        dist.joint[t,i,j]

-                }

-            }

-        }

-        distDR[t,,] <- distDR[t,,]/sum(distDR[t,,])

-    }

-    return( distDR )

-}

-

-shockprob <- function(p, rho, Z, log.p=F){

-  ## computes the shocked default probability as a function of the copula factor

-  ## function is vectorized provided the below caveats:

-  ## p and rho are vectors of same length n, Z is a scalar, returns vector of length n

-  ## p and rho are scalars, Z is a vector of length n, returns vector of length n

-  if(length(p)==1){

-    if(rho==1){

-      return(Z<=qnorm(p))

-    }else{

-      return(pnorm((qnorm(p)-sqrt(rho)*Z)/sqrt(1-rho), log.p=log.p))

-    }

-  }else{

-    result <- double(length(p))

-    result[rho==1] <- Z<=qnorm(p[rho==1])

-    result[rho<1] <- pnorm((qnorm(p[rho<1])-sqrt(rho[rho<1])*Z)/sqrt(1-rho[rho<1]), log.p=log.p)

-    return( result )

-  }

-}

-

-shockseverity <- function(S, Stilde=1, Z, rho, p){

-  ## computes the severity as a function of the copula factor Z

-  result <- double(length(S))

-  result[p==0] <- 0

-  result[p!=0] <- Stilde * exp( shockprob(S[p!=0]/Stilde*p[p!=0], rho[p!=0], Z, TRUE) -

-           shockprob(p[p!=0], rho[p!=0], Z, TRUE))

-  return(result)

-}

-

-dshockprob <- function(p,rho,Z){

-    dnorm((qnorm(p)-sqrt(rho)*Z)/sqrt(1-rho))*dqnorm(p)/sqrt(1-rho)

-}

-

-dqnorm <- function(x){

-    1/dnorm(qnorm(x))

-}

-

-fit.prob <- function(Z, w, rho, p0){

-    ## if the weights are not perfectly gaussian, find the probability p such

-    ## E_w(shockprob(p, rho, Z)) = p0

-    require(distr)

-    if(p0==0){

-        return(0)

-    }

-    if(rho == 1){

-        distw <- DiscreteDistribution(Z, w)

-        return(pnorm(q(distw)(p0)))

-    }

-    eps <- 1e-12

-    dp <- (crossprod(shockprob(p0,rho,Z),w)-p0)/crossprod(dshockprob(p0,rho,Z),w)

-    p <- p0

-    while(abs(dp) > eps){

-        dp <- (crossprod(shockprob(p,rho,Z),w)-p0)/crossprod(dshockprob(p,rho,Z),w)

-        phi <- 1

-        while ((p-phi*dp)<0 || (p-phi*dp)>1){

-            phi <- 0.8*phi

-        }

-        p <- p - phi*dp

-    }

-    return(p)

-}

-

-fit.probC <- function(Z, w, rho, p0){

-    r <- .C("fitprob", as.double(Z), as.double(w), as.integer(length(Z)),

-            as.double(rho), as.double(p0), q = double(1))

-    return(r$q)

-}

-

-stochasticrecov <- function(R, Rtilde, Z, w, rho, porig, pmod){

-    ## if porig == 0 (probably matured asset) then return orginal recovery

-    ## it shouldn't matter anyway since we never default that asset

-    if(porig == 0){

-        return(rep(R, length(Z)))

-    }else{

-        ptilde <- fit.prob(Z, w, rho, (1-R)/(1-Rtilde) * porig)

-        return(abs(1-(1-Rtilde) * exp(shockprob(ptilde, rho, Z, TRUE) - shockprob(pmod, rho, Z, TRUE))))

-    }

-}

-

-stochasticrecovC <- function(R, Rtilde, Z, w, rho, porig, pmod){

-    r <- .C("stochasticrecov", as.double(R), as.double(Rtilde), as.double(Z),

-            as.double(w), as.integer(length(Z)), as.double(rho), as.double(porig),

-            as.double(pmod), q = double(length(Z)))

-    return(r$q)

-}

-

-BClossdist <- function(defaultprob, issuerweights, recov, rho, Z, w,

-                       N=length(recov)+1, defaultflag=FALSE, n.int=500){

-  if(missing(Z)){

-    quadrature <- GHquad(n.int)

-    Z <- quadrature$Z

-    w <- quadrature$w

-  }

-  ## do not use if weights are not gaussian, results would be incorrect

-  ## since shockseverity is invalid in that case (need to use stochasticrecov)

-  LZ <- matrix(0, N, length(Z))

-  RZ <- matrix(0, N, length(Z))

-  L <- matrix(0, N, ncol(defaultprob))

-  R <- matrix(0, N, ncol(defaultprob))

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

-    for(i in 1:length(Z)){

-      g.shocked <- shockprob(defaultprob[,t], rho, Z[i])

-      S.shocked <- shockseverity(1-recov, 1, Z[i], rho, defaultprob[,t])

-      temp <- lossrecovdist(g.shocked, , issuerweights, S.shocked, N)

-      LZ[,i] <- temp$L

-      RZ[,i] <- temp$R

-    }

-    L[,t] <- LZ%*%w

-    R[,t] <- RZ%*%w

-  }

-  list(L=L, R=R)

-}

-

-BClossdistC <- function(defaultprob, issuerweights, recov, rho, Z, w,

-                        N=length(issuerweights)+1, defaultflag=FALSE){

-    L <- matrix(0, N, dim(defaultprob)[2])

-    R <- matrix(0, N, dim(defaultprob)[2])

-    rho <- rep(rho, length(issuerweights))

-    r <- .C("BCloss_recov_dist", defaultprob, dim(defaultprob)[1], dim(defaultprob)[2],

-            as.double(issuerweights), as.double(recov), as.double(Z), as.double(w),

-            as.integer(length(Z)), as.double(rho), as.integer(N), as.logical(defaultflag), L=L, R=R)

-    return(list(L=r$L,R=r$R))

-}