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import numpy as np
from tranche_functions import *
from yieldcurve import YC
import yaml
import datetime
import os
import pandas as pd
import pdb
n_int = 500
n_credit = 100
Z, w = GHquad(n_int)
with open("../R/index_definitions.yml") as fh:
indices = yaml.load(fh, Loader=yaml.FullLoader)
indices["hy21"]["maturity"] = datetime.date(1970, 1, 1) + datetime.timedelta(
indices["hy21"]["maturity"]
)
hy21 = indices["hy21"]
hy21["startdate"] = datetime.date(2013, 9, 20)
dates = [
f[9:19]
for f in os.listdir(os.path.join(os.environ["DATA_DIR"], "Backtest"))
if "survprob" in f
]
Rho = np.zeros((len(dates), 3))
for i, d in enumerate(dates):
startdate = datetime.datetime.strptime(d, "%Y-%m-%d")
ts = YC(startdate)
with open(
os.path.join(os.environ["DATA_DIR"], "Backtest", "recov_{0}.csv".format(d))
) as fh:
recov = np.array([float(e) for e in fh], dtype="double", order="F")
with open(
os.path.join(os.environ["DATA_DIR"], "Backtest", "survprob_{0}.csv".format(d))
) as fh:
fh.readline() ##skip header
SurvProb = np.array(
[[float(e) for e in line.split(",")] for line in fh],
dtype="double",
order="F",
)
defaultprob = 1 - SurvProb
issuerweights = np.ones(100) / 100
rho = 0.4
Ngrid = 101
K = np.array([0, 0.15, 0.25, 0.35, 1])
Kmod = adjust_attachments(K, hy21["loss"], hy21["factor"])
quotes = pd.read_csv(
os.path.join(
os.environ["BASE_DIR"],
"Scenarios",
"Calibration",
"hy21_tranches_{0}.csv".format(d),
)
)
quotes = quotes["Mid"] / 100
dK = np.diff(Kmod)
quotes = np.cumsum(dK * (1 - quotes))
sched = creditSchedule(startdate, "5Yr", 0.05, ts, enddate=hy21["maturity"])
acc = cdsAccrued(startdate, 0.05)
for j, q in enumerate(quotes[:-1]):
def aux(rho):
L, R = BClossdist(defaultprob, issuerweights, recov, rho, Z, w, 101)
cl = tranche_cl(L, R, sched, 0, Kmod[j + 1])
pl = tranche_pl(L, sched, 0, Kmod[j + 1])
return cl + pl + q - acc
l, u = (0, 1)
for _ in range(10):
rho = (l + u) / 2.0
if aux(rho) > 0:
u = rho
else:
l = rho
Rho[i, j] = (l + u) / 2.0
print(Rho[i, :])
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