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# ---
# jupyter:
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# language: python
# name: python3
# ---
# %% [markdown]
# # Global curve bootstrap
#
# Copyright (©) 2020 StatPro Italia srl
#
# This file is part of QuantLib, a free-software/open-source library
# for financial quantitative analysts and developers - https://www.quantlib.org/
#
# QuantLib is free software: you can redistribute it and/or modify it under the
# terms of the QuantLib license. You should have received a copy of the
# license along with this program; if not, please email
# <quantlib-dev@lists.sf.net>. The license is also available online at
# <https://www.quantlib.org/license.shtml>.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the license for more details.
# %%
import QuantLib as ql
import pandas as pd
# %%
interactive = "get_ipython" in globals()
# %% [markdown]
# ### Setup
# %%
today = ql.Date(26, 9, 2019)
spot = ql.TARGET().advance(today, 2, ql.Days)
# %%
ql.Settings.instance().evaluationDate = today
# %% [markdown]
# ### Data
#
# We'll use the following data as input:
# %%
refMktRates = [
-0.373,
-0.388,
-0.402,
-0.418,
-0.431,
-0.441,
-0.45,
-0.457,
-0.463,
-0.469,
-0.461,
-0.463,
-0.479,
-0.4511,
-0.45418,
-0.439,
-0.4124,
-0.37703,
-0.3335,
-0.28168,
-0.22725,
-0.1745,
-0.12425,
-0.07746,
0.0385,
0.1435,
0.17525,
0.17275,
0.1515,
0.1225,
0.095,
0.0644,
]
# %% [markdown]
# ### Market instruments
# %%
index = ql.Euribor6M()
# %% [markdown]
# The first market rate is for the 6-months deposit...
# %%
helpers = [
ql.DepositRateHelper(
refMktRates[0] / 100.0, ql.Period(6, ql.Months), 2, ql.TARGET(), ql.ModifiedFollowing, True, ql.Actual360()
)
]
# %% [markdown]
# ...the next 12 are for FRAs...
# %%
helpers += [ql.FraRateHelper(r / 100.0, i + 1, index) for i, r in enumerate(refMktRates[1:13])]
# %% [markdown]
# ...and the others are swap rates.
# %%
swapTenors = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 25, 30, 35, 40, 45, 50]
helpers += [
ql.SwapRateHelper(
r / 100.0, ql.Period(T, ql.Years), ql.TARGET(), ql.Annual, ql.ModifiedFollowing, ql.Thirty360(ql.Thirty360.BondBasis), index
)
for r, T in zip(refMktRates[13:32], swapTenors)
]
# %% [markdown]
# We'll also add a few synthetic helpers:
# %%
additional_helpers = [ql.FraRateHelper(-0.004, 12 + i, index) for i in range(7)]
additional_dates = [ql.TARGET().advance(spot, 1 + i, ql.Months) for i in range(5)]
# %% [markdown]
# ### Global bootstrap
#
# This curve takes into account the market instruments, as well as the passed additional ones.
# %%
curve = ql.GlobalLinearSimpleZeroCurve(
spot, helpers, ql.Actual365Fixed(), ql.GlobalBootstrap(additional_helpers, additional_dates, 1.0e-12)
)
curve.enableExtrapolation()
# %% [markdown]
# ### Report
# %%
data = []
for i, h in enumerate(helpers):
pillar = h.pillarDate()
if i < 13:
day_counter = ql.Actual360()
compounding = ql.Simple
else:
day_counter = ql.Thirty360(ql.Thirty360.BondBasis)
compounding = ql.SimpleThenCompounded
r = curve.zeroRate(pillar, day_counter, compounding, ql.Annual).rate()
data.append((pillar.to_date(), r * 100))
# %%
df = pd.DataFrame(data, columns=["pillar", "zero rate"])
if not interactive:
print(df)
df
|
