QuantLib-Python: Hull-White one-factor model calibration

This Python program is presenting the process of calibrating Hull-White One-factor interest rate model to a given set of Swaption volatilities. In the example program, I have used exactly the same data as used in the book QuantLib Python Cookbook by G. Balaraman and L. Ballabio in the Chapter 14 (Short Interest Rate Model Calibration) and corresponding results are perfectly replicated here.

Thanks for reading my blog.
-Mike

from QuantLib import *
import numpy as Numpy

# hard-coded data
def CreateSwaptionVolatilityList():
    vol = []
    vol.append(0.1148)
    vol.append(0.1108)
    vol.append(0.1070)
    vol.append(0.1021)
    vol.append(0.1000)
    return vol

class ModelCalibrator:
    def __init__(self, endCriteria):        
        self.endCriteria = endCriteria
        self.helpers = []

    def AddCalibrationHelper(self, helper):
        self.helpers.append(helper)

    def Calibrate(self, model, engine, curve, fixedParameters):
        # assign pricing engine to all calibration helpers
        for i in range(len(self.helpers)):
            self.helpers[i].setPricingEngine(engine)
        method = LevenbergMarquardt()
        if(len(fixedParameters) == 0):
            model.calibrate(self.helpers, method, self.endCriteria)
        else:
            model.calibrate(self.helpers, method, self.endCriteria,
                NoConstraint(), [], fixedParameters)

# general parameters
tradeDate = Date(15, February, 2002)
Settings.instance().evaluationDate = tradeDate
settlementDate = Date(19, February, 2002)
calendar = TARGET()
dayCounter = Actual360()

# create market data: term structure and diagonal volatilities
curveHandle = YieldTermStructureHandle(FlatForward(settlementDate, 0.04875825, Actual365Fixed()))
vol = CreateSwaptionVolatilityList()

# create calibrator object
endCriteria = EndCriteria(10000, 100, 0.000001, 0.00000001, 0.00000001)
calibrator = ModelCalibrator(endCriteria)

# add swaption helpers to calibrator
for i in range(len(vol)):
    t = i + 1; tenor = len(vol) - i    
    helper = SwaptionHelper(
        Period(t, Years), 
        Period(tenor, Years), 
        QuoteHandle(SimpleQuote(vol[i])), 
        USDLibor(Period(3, Months), curveHandle), 
        Period(1, Years), 
        dayCounter, 
        dayCounter, 
        curveHandle)    
    calibrator.AddCalibrationHelper(helper)

# create model and pricing engine, calibrate model and print calibrated parameters
print('case 1 : calibrate all involved parameters (HW1F : reversion, sigma)')
model = HullWhite(curveHandle)
engine = JamshidianSwaptionEngine(model)
fixedParameters = []
calibrator.Calibrate(model, engine, curveHandle, fixedParameters)
print('calibrated reversion: ' + str(round(model.params()[0], 5)))
print('calibrated sigma: ' + str(round(model.params()[1], 5)))
print()

print('case 2 : calibrate sigma and fix reversion to 0.05')
model = HullWhite(curveHandle, 0.05, 0.0001)
engine = JamshidianSwaptionEngine(model)
fixedParameters = [True, False]
calibrator.Calibrate(model, engine, curveHandle, fixedParameters)
print('fixed reversion: ' + str(round(model.params()[0], 5)))
print('calibrated sigma: ' + str(round(model.params()[1], 5)))
print()

print('case 3 : calibrate reversion and fix sigma to 0.01')
model = HullWhite(curveHandle, 0.05, 0.01)
engine = JamshidianSwaptionEngine(model)
fixedParameters = [False, True]
calibrator.Calibrate(model, engine, curveHandle, fixedParameters)
print('calibrated reversion: ' + str(round(model.params()[0], 5)))
print('fixed sigma: ' + str(round(model.params()[1], 5)))
print()