Dear all, I have used the following R function in order to estimate the upper bound PD given the number of obligors and defaults, considering the Pluto & Tasche approach. I'd be so grateful if you tell me how can I achieve the same results, however, just using PROC IML and SAS native functions, instead of having the R running in the background? Thanks in advance. Rgs, Raphael proc iml; submit / R; # Estimates the Upper Pound PD given the number of obligors and defaults plutotashe <- function(n, i, theta, rho, T, Conf, N) { hh = uniroot(error, interval=c(0,1), lower=0.000000001, upper=0.999999999, tol = 1e-8, maxiter=500, n=n, i=i, theta=theta, rho=rho, T=T, Conf=Conf, N=N) names(hh) = c("UpperBoundPD", "Function", "iter", "estim.prec") return(hh) } # Error Function error <- function(pd, n, i, theta, rho, T, Conf, N) { AverageSim = 1-mean(replicate(N, sim(pd,n,i,theta,rho, T))) error = AverageSim-Conf return(error) } # Simulation Run sim = function(pd, n, i, theta, rho, T) { # Generate a state of the economy Z=array(0,c(T,1)) Z[1] = rnorm(1) if (T > 1) { for (j in 2:T) { Z[j] = theta*Z[j-1]+sqrt(1-theta^2)*rnorm(1) } rm(j) } p = 1-prod(1 - pnorm((qnorm(pd)-Z*sqrt(rho))/sqrt(1-rho))) # Prob of Defaulting (tt=binom_cum(n,p,i)) return(tt) } # Cumulative Binomial binom_cum = function (n,p,i) { bc = 0 for (j in 0:i) { bc = bc + choose(n,j)*(p^j)*((1-p)^(n-j)) } return(bc) } n <- 800 #number of obligors i <- 0 #number of defaults theta <- 0.30 #year-to-year correlation rho <- 0.12 #asset correlation T <- 5 #number of years Conf <- 0.99 #confidence level N <- 1000 #number of simulations plutotashe(n, i, theta,rho,T,Conf, N) endsubmit; quit;
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