import sys, os
sys.path.append(os.path.join(os.path.dirname(__file__),'QuantLibWrapper'))
import pandas
import numpy as np
from scipy.optimize import least_squares
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # for 3d plotting
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
from QuantLibWrapper import YieldCurve, HullWhiteModel, createSwaption
flatCurve = YieldCurve(['70y'],[0.03])
discCurve = flatCurve
projCurve = flatCurve
atmVols = pandas.read_csv('swaptionATMVols.csv', sep=';', index_col=0 )
meanReversion = 0.1
volatilityTimes = np.array([ 2.0, 5.0, 10.0, 20.0 ])
volatilityValues = np.array([ 0.005, 0.005, 0.005, 0.005 ])
hwModel = HullWhiteModel(discCurve,meanReversion,volatilityTimes,volatilityValues)
# calibration targets
expiries = np.array([ 2, 5, 10, 20 ])
swapterms = np.array([ 2, 5, 10, 20 ])
marketVols = np.zeros([len(expiries),len(swapterms)])
swaptions = []
import sys, os
sys.path.append(os.path.join(os.path.dirname(__file__), 'QuantLibWrapper'))
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import pandas
import QuantLib as ql
import QuantLibWrapper.YieldCurve as yc
from QuantLibWrapper import MCSimulation, Payoffs, HullWhiteModel, HullWhiteModelWithDiscreteNumeraire
# yield curves
flatCurve = yc.YieldCurve(['30y'], [0.03])
terms = [
'1y', '2y', '3y', '4y', '5y', '6y', '7y', '8y', '9y', '10y', '12y', '15y',
'20y', '25y', '30y'
]
rates = [
2.70e-2, 2.75e-2, 2.80e-2, 3.00e-2, 3.36e-2, 3.68e-2, 3.97e-2, 4.24e-2,
4.50e-2, 4.75e-2, 4.75e-2, 4.70e-2, 4.50e-2, 4.30e-2, 4.30e-2
]
fwdRateYC = yc.YieldCurve(terms, rates)
# Hull-White model
meanReversion = 0.05
volatilityTimes = np.array([1.0, 2.0, 5.0, 10.0])
3.36e-2,
3.68e-2,
3.97e-2,
4.24e-2,
4.50e-2,
4.75e-2,
4.75e-2,
4.70e-2,
4.50e-2,
4.30e-2,
4.30e-2,
4.30e-2,
]
rates = [0.025 for r in rates]
rates2 = [r + 0.005 for r in rates]
discCurve = YieldCurve(terms, rates)
projCurve = YieldCurve(terms, rates2)
meanReversion = 0.05
normalVol = 0.01
# swaption(s)
maturity = 20 # in years
swaptions = []
terms = []
inputVols = []
for k in range(1, maturity):
expTerm = str(k) + "y"
swpTerm = str(maturity - k) + "y"
sigma = 0.01 # atmVols.loc[expTerm,swpTerm]
terms.append(expTerm + "-" + swpTerm)
2.75e-2,\
2.80e-2,\
3.00e-2,\
3.36e-2,\
3.68e-2,\
3.97e-2,\
4.24e-2,\
4.50e-2,\
4.75e-2,\
4.75e-2,\
4.70e-2,\
4.50e-2,\
4.30e-2,\
4.30e-2 ]
fwdRateYC = yc.YieldCurve(terms, rates)
print(fwdRateYC.table())
fwdRateYC.plot(1.0 / 365)
today = ql.Settings.getEvaluationDate(ql.Settings.instance())
print('Today: ' + str(today))
calendar = ql.TARGET()
period = ql.Period('500d')
bdc = ql.Following
print(str(calendar) + ', ' + str(period) + ', ' + str(bdc))
maturity = calendar.advance(today, period, bdc)
print('Maturity: ' + str(maturity))
discountFactor = fwdRateYC.discount(maturity)
print('Discount: ' + str(discountFactor))
import matplotlib.pyplot as plt
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import pandas
import QuantLib as ql
import QuantLibWrapper.YieldCurve as yc
from QuantLibWrapper.HullWhiteModel import HullWhiteModel
from QuantLibWrapper.MCSimulation import MCSimulation
from QuantLibWrapper import Payoffs
# yield curves
flatCurve = yc.YieldCurve(['30y'], [0.03])
# We calibrate a Hull-White model to 100bp 'volatility' of x at 10y and 11y
meanReversion = 0.2
def obj1(sigma1):
model = HullWhiteModel(flatCurve, meanReversion, np.array([5.0, 10.0]),
np.array([sigma1, sigma1]))
return model.varianceX(0, 5.0) - (1.0e-2)**2 * 5
sigma1 = brentq(obj1, 1.0e-4, 1.0e-1)
from scipy.optimize import brentq
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # for 3d plotting
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import pandas
import QuantLib as ql
from QuantLibWrapper import YieldCurve, HullWhiteModel, Swap, Swaption, createSwaption
# yield curves
flatCurve = YieldCurve(["30y"], [0.03])
terms = [
"1y",
"2y",
"3y",
"4y",
"5y",
"6y",
"7y",
"8y",
"9y",
"10y",
"12y",
"15y",
"20y",
from QuantLibWrapper import YieldCurve, Swap, Swaption
# market data
terms = [
'1y', '2y', '3y', '4y', '5y', '6y', '7y', '8y', '9y', '10y', '12y', '15y',
'20y', '25y', '30y'
]
rates = [
2.70e-2, 2.75e-2, 2.80e-2, 3.00e-2, 3.36e-2, 3.68e-2, 3.97e-2, 4.24e-2,
4.50e-2, 4.75e-2, 4.75e-2, 4.70e-2, 4.50e-2, 4.30e-2, 4.30e-2
]
rates2 = [r + 0.005 for r in rates]
discCurve = YieldCurve(terms, rates)
projCurve = YieldCurve(terms, rates2)
normalVol = 0.01
# underlying swap
startDate = ql.Date(30, 10, 2028)
endDate = ql.Date(30, 10, 2038)
swap = Swap(startDate, endDate, 0.052513, discCurve, projCurve)
# swaption
expiryDate = ql.TARGET().advance(startDate, ql.Period('-2d'), ql.Preceding)
swaption = Swaption(swap, expiryDate, normalVol)
