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# ---
# jupyter:
# jupytext:
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# extension: .py
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# language: python
# name: python3
# ---
# # ISDA CDS engine
#
# 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()
trade_date = ql.Date(21,5,2009)
ql.Settings.instance().evaluationDate = trade_date
ql.IborCoupon.createAtParCoupons()
dep_tenors = [1,2,3,6,9,12]
dep_quotes = [0.003081,0.005525,0.007163,0.012413,0.014,0.015488]
isdaRateHelpers = [ql.DepositRateHelper(dep_quotes[i],
dep_tenors[i]*ql.Period(ql.Monthly),
2,ql.WeekendsOnly(),
ql.ModifiedFollowing,
False,ql.Actual360())
for i in range(len(dep_tenors))]
swap_tenors = [2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, 25, 30]
swap_quotes = [0.011907,
0.01699,
0.021198,
0.02444,
0.026937,
0.028967,
0.030504,
0.031719,
0.03279,
0.034535,
0.036217,
0.036981,
0.037246,
0.037605]
isda_ibor = ql.IborIndex('IsdaIbor',3*ql.Period(ql.Monthly),2,
ql.USDCurrency(),ql.WeekendsOnly(),
ql.ModifiedFollowing,False,ql.Actual360())
isdaRateHelpers = isdaRateHelpers + [
ql.SwapRateHelper(swap_quotes[i],swap_tenors[i]*ql.Period(ql.Annual),
ql.WeekendsOnly(),ql.Semiannual,ql.ModifiedFollowing,
ql.Thirty360(ql.Thirty360.BondBasis),isda_ibor)
for i in range(len(swap_tenors))]
spot_date = ql.WeekendsOnly().advance(trade_date, 2 * ql.Period(ql.Daily))
# Technically, the model requires the discount factor to be 1 at spot;
# but we can't do that and also have the discount curve extend back to
# the trade date. For the time being, we'll keep discount = 1 at trade.
# The results match anyway.
swap_curve = ql.PiecewiseFlatForward(trade_date, isdaRateHelpers, ql.Actual365Fixed())
discountCurve = ql.YieldTermStructureHandle(swap_curve)
probabilityCurve = ql.RelinkableDefaultProbabilityTermStructureHandle()
termDates = [ql.Date(20, 6, 2010),
ql.Date(20, 6, 2011),
ql.Date(20, 6, 2012),
ql.Date(20, 6, 2016),
ql.Date(20, 6, 2019)]
spreads = [0.001, 0.1]
recoveries = [0.2, 0.4]
markitValues = [97798.29358, #0.001
97776.11889, #0.001
-914971.5977, #0.1
-894985.6298, #0.1
186921.3594, #0.001
186839.8148, #0.001
-1646623.672, #0.1
-1579803.626, #0.1
274298.9203,
274122.4725,
-2279730.93,
-2147972.527,
592420.2297,
591571.2294,
-3993550.206,
-3545843.418,
797501.1422,
795915.9787,
-4702034.688,
-4042340.999]
tolerance = 1.0e-2
l = 0
distance = 0
data = []
upfront_date = ql.WeekendsOnly().advance(trade_date, 3 * ql.Period(ql.Daily))
for termDate in termDates:
for spread in spreads:
for recovery in recoveries:
cdsSchedule = ql.Schedule(trade_date, termDate,
3*ql.Period(ql.Monthly),
ql.WeekendsOnly(),
ql.Following, ql.Unadjusted,
ql.DateGeneration.CDS, False)
quotedTrade = ql.CreditDefaultSwap(
ql.Protection.Buyer,10000000,0,spread,cdsSchedule,
ql.Following,ql.Actual360(),True,True,trade_date,
upfront_date, ql.FaceValueClaim(), ql.Actual360(True))
h = quotedTrade.impliedHazardRate(0,discountCurve,ql.Actual365Fixed(),
recovery,1e-10,
ql.CreditDefaultSwap.ISDA)
probabilityCurve.linkTo(
ql.FlatHazardRate(0,ql.WeekendsOnly(),
ql.makeQuoteHandle(h),
ql.Actual365Fixed()))
engine = ql.IsdaCdsEngine(probabilityCurve,recovery,discountCurve)
conventionalTrade = ql.CreditDefaultSwap(
ql.Protection.Buyer,10000000,0,0.01,cdsSchedule,
ql.Following,ql.Actual360(),True,True,trade_date,
upfront_date, ql.FaceValueClaim(), ql.Actual360(True))
conventionalTrade.setPricingEngine(engine)
upfront = conventionalTrade.notional() * conventionalTrade.fairUpfront()
data.append(
(termDate,
spread,
recovery,
h,
upfront,
markitValues[l],
abs(upfront-markitValues[l]),
abs(upfront-markitValues[l])<tolerance)
)
distance = distance + abs(upfront-markitValues[l])
l = l + 1
df = pd.DataFrame(data, columns=["Term date", "Spread", "Recovery",
"Hazard rate", "Upfront", "Markit value", "Distance", "Within tolerance"])
if not interactive:
print(df)
df.style.format({'Spread': '{:.4%}', 'Hazard rate': '{:.2%}', 'Upfront': '{:.2f}',
'Markit value': '{:.2f}', 'Distance': '{:.6f}'})
print('total distance:',distance)
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