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import numpy as np
from tranche_functions import GHquad, BClossdist
n_int = 500
n_credit = 100
Z, w = GHquad(n_int)
with open("recov.csv") as fh:
recov = np.array([float(e) for e in fh], dtype='double', order='F')
with open("SurvProb.csv") as fh:
SurvProb = np.array([[float(e) for e in line.split(",")] for line in fh], dtype='double', order='F')
defaultprob = 1 - SurvProb
p = defaultprob
rho = 0.45 * np.ones(n_credit)
for l in range(150):
Rstoch = np.zeros((n_credit, n_int, SurvProb.shape[1]))
for t in range(SurvProb.shape[1]):
for i in range(n_credit):
Rstoch[i,:,t] = stochasticrecov(recov[i], 0, Z, w_mod, rho[i], defaultprob[i,t], p[i,t])
L = np.zeros((n_int, Ngrid, SurvProb.shape[1]))
R = np.zeros((n_int, Ngrid, SurvProb.shape[1]))
for t in range(SurvProba.shape[1]):
S = 1 - Rstoch[:,:,t]
L[:,:,t] = lossdistZ(p[:,t], issuerweights, S, Ngrid, 0, rho, Z)
R[:,:,t] = lossdistZ(p[:,t], issuerweights, S, Ngrid, 0, rho, Z)
for i in range(n_int):
result[:,i] = tranche.pvvec(Kmodified, L[i,,], R[i,,], cs)
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