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# Copyright (C) 2004, 2005, 2006 StatPro Italia srl
#
# This file is part of QuantLib, a free-software/open-source library
# for financial quantitative analysts and developers - http://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
# <http://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.
from QuantLib import *
# global data
todaysDate = Date(15,May,1998)
Settings.instance().evaluationDate = todaysDate
settlementDate = Date(17,May,1998)
riskFreeRate = FlatForward(settlementDate, 0.05, Actual365Fixed())
# option parameters
exercise = EuropeanExercise(Date(17,May,1999))
payoff = PlainVanillaPayoff(Option.Call, 8.0)
# market data
underlying1 = SimpleQuote(7.0)
volatility1 = BlackConstantVol(todaysDate, TARGET(), 0.10, Actual365Fixed())
dividendYield1 = FlatForward(settlementDate, 0.05, Actual365Fixed())
underlying2 = SimpleQuote(7.0)
volatility2 = BlackConstantVol(todaysDate, TARGET(), 0.10, Actual365Fixed())
dividendYield2 = FlatForward(settlementDate, 0.05, Actual365Fixed())
process1 = BlackScholesMertonProcess(QuoteHandle(underlying1),
YieldTermStructureHandle(dividendYield1),
YieldTermStructureHandle(riskFreeRate),
BlackVolTermStructureHandle(volatility1))
process2 = BlackScholesMertonProcess(QuoteHandle(underlying2),
YieldTermStructureHandle(dividendYield2),
YieldTermStructureHandle(riskFreeRate),
BlackVolTermStructureHandle(volatility2))
procs = StochasticProcessVector()
procs.push_back(process1)
procs.push_back(process2)
matrix = Matrix(2,2)
matrix[0][0] = 1.0
matrix[1][1] = 1.0
matrix[0][1] = 0.5
matrix[1][0] = 0.5
process = StochasticProcessArray(procs, matrix)
basketoption = BasketOption(MaxBasketPayoff(payoff), exercise)
basketoption.setPricingEngine(MCBasketEngine(process,
'pseudorandom',
timeStepsPerYear = 1,
requiredTolerance = 0.02,
seed = 42))
print basketoption.NPV()
basketoption = BasketOption(MinBasketPayoff(payoff), exercise)
basketoption.setPricingEngine(MCBasketEngine(process,
'pseudorandom',
timeStepsPerYear = 1,
requiredTolerance = 0.02,
seed = 42))
print basketoption.NPV()
basketoption = BasketOption(AverageBasketPayoff(payoff, 2), exercise)
basketoption.setPricingEngine(MCBasketEngine(process,
'pseudorandom',
timeStepsPerYear = 1,
requiredTolerance = 0.02,
seed = 42))
print basketoption.NPV()
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