Ошибка выполнения калибровки корпуса белого калибровки с использованием Quantlib в Python ⇐ Python

[Anonymous]

Я обновляю свой предыдущий код Quantlib, чтобы калибровать параметры HW с помощью Cap. Я столкнулся с какой -то ошибкой, никогда не испытываемой раньше. Поскольку Quantlib версия 1.28 может работать, но 1.29 не может, угадайте разум может быть некоторой амортизированной функцией. < /P>

import QuantLib as ql
from collections import namedtuple
import math
import pandas as pd
import numpy as np
from dateutil.relativedelta import relativedelta
from datetime import datetime

analysis_date = "2025-03-31"
freq = 2
df = pd.read_excel("MarketData_IR - LIBOR.xlsx", index_col=0)
df = df.loc[[analysis_date], :] / 100
today = df.index[0].date()
today = ql.Date(today.day, today.month, today.year)

df = np.log(df / freq + 1) * freq

ql.Settings.instance().evaluationDate = today;
dates = [today + ql.Period(x, ql.Days) for x in [0, 7, 14]] + [today + ql.Period(x, ql.Months) for x in
[1, 2, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 48,
60, 72, 84, 96, 108, 120, 144, 180, 240, 300, 360, 363]]
yields = df.values[0].tolist()
yields = yields + [yields[-1]]
day_count = ql.Actual360()
calendar = ql.UnitedStates(ql.UnitedStates.GovernmentBond)
interpolation = ql.Linear()
compounding = ql.Compounded
compounding_frequency = ql.Continuous

ts_handle = ql.YieldTermStructureHandle(
ql.ZeroCurve(dates, yields, day_count, calendar,
interpolation, compounding, compounding_frequency))

df_vol = pd.read_excel("MarketData_CapNormalVol.xlsx", index_col=0)
df_vol = df_vol.loc[[analysis_date], :]
vols = df_vol.values[0]

ibor_index = ql.USDLibor(ql.Period(3, ql.Months), ts_handle)

expiries = [ql.Period(i, ql.Years) for i in range(1, 11)] + [ql.Period(15, ql.Years)] + [ql.Period(20, ql.Years)] + [
ql.Period(30, ql.Years)]

def create_cap_helpers(expiries, vols, index, term_structure, engine):
caps = []
fixed_leg_tenor = ql.Quarterly
fixed_leg_daycounter = ql.Actual365Fixed()
# fixed_leg_daycounter = ql.Actual360()
for d in range(len(expiries)):
vol_handle = ql.QuoteHandle(ql.SimpleQuote(vols[d]))
helper = ql.CapHelper(expiries[d], # Period
vol_handle,
index,
fixed_leg_tenor,
fixed_leg_daycounter,
False,
term_structure,
# ql.BlackCalibrationHelper.RelativePriceError,
ql.BlackCalibrationHelper.PriceError,
# ql.BlackCalibrationHelper.ImpliedVolError,
ql.Normal,
# ql.ShiftedLognormal
)
helper.setPricingEngine(engine)
caps.append(helper)

return caps

def calibration_report(swaptions, vol, expirie_date):
print("-" * 60)
print("%15s %15s %15s %15s" % \
("Maturity date", "Model Price", "Market Price", "Rel Error"))
print("-" * 60)
cum_err = 0.0
for i, s in enumerate(swaptions):
date = expirie_date
model_price = s.modelValue()
market_vol = vol
black_price = s.blackPrice(market_vol)
rel_error = model_price / black_price - 1.0
print("%15s %15.5f %15.5f %15.5f" % \
(date, model_price, black_price, rel_error))

print("-" * 60)

model = ql.HullWhite(ts_handle, a=0.001)
engine = ql.AnalyticCapFloorEngine(model)

optimization_method = ql.LevenbergMarquardt(1.0e-14, 1.0e-8, 1.0e-8)

end_criteria = ql.EndCriteria(10000000000, 100000, 1e-8, 1e-8, 1e-8)
caps = create_cap_helpers(expiries, vols, ibor_index, ts_handle, engine)

model.calibrate(caps,
optimization_method,
end_criteria,
ql.CompositeConstraint(ql.BoundaryConstraint(0.001, 1.0),
ql.BoundaryConstraint(0.00001, 1.0)),
[],
[False, False]) # First is if fixed alpha, second is sigma

a, sigma = model.params()
print("a = %.10f, sigma = %6.9f" % (a, sigma))

calibration_report(caps, vols, expiries)

< /code>
Ошибка времени выполнения возникает на model.calibrate () с сообщением об ошибке: < /p>
RuntimeError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_19944\955511495.py in ?()
1 period = pd.date_range(start = '4-1-2024' , end = '3-31-2025', freq="D")
----> 2 results = pd.DataFrame([calibration(str(i.date())) for i in period]).dropna()

~\AppData\Local\Temp\ipykernel_19944\3509880946.py in ?(analysis_date, a, sigma)
41 # optimization_method = ql.NonLinearLeastSquare()
42 end_criteria = ql.EndCriteria(1000000000, 1000000, 1e-8, 1e-8, 1e-8)
43 caps = create_cap_helpers(expiries, vols, ibor_index, ts_handle, engine)
44
---> 45 model.calibrate(caps, optimization_method, end_criteria, ql.CompositeConstraint(ql.BoundaryConstraint(0.001, 1.0),
46 ql.BoundaryConstraint(0.00001, 1.0)), [], [False, False]) # First is alpha, second sigma
47
48 a, sigma = model.params()

~\.conda\envs\Quantlib\Lib\site-packages\QuantLib\QuantLib.py in ?(self, *args)
17197 def calibrate(self, *args):
17198 r"""calibrate(CalibratedModel self, CalibrationHelperVector arg2, OptimizationMethod arg3, EndCriteria arg4, Constraint constraint=Constraint(), DoubleVector weights=std::vector< Real >(), BoolVector fixParameters=std::vector< bool >())"""
> 17199 return _QuantLib.CalibratedModel_calibrate(self, *args)

RuntimeError: null number of evaluations



Подробнее здесь: https://stackoverflow.com/questions/796 ... -in-python