[Python] Hull White calibration runtime error using Quantlib in python

I am refreshing my previous QuantLib code to calibrate HW parameters using Cap. I encountered some error never experienced before. Since Quantlib version 1.28 can work but 1.29 cannot, guess reason might be some function depreciated.


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)




The run time error occurs at model.calibrate(), with error message:

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

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