def evaluate_american_option(input_dict):
tparams = input_dict["tradeParameters"]
# Option Construction
todaysDate = construct_date(tparams["evaluationDate"])
ql.Settings.instance().evaluationDate = todaysDate
exercise = ql.AmericanExercise(todaysDate, construct_date(tparams["exerciseDate"]))
payoff = ql.PlainVanillaPayoff(ql.Option.Put, tparams["payoff"])
option = ql.VanillaOption(payoff, exercise)
# Market Data
underlying = ql.SimpleQuote(tparams["underlying"])
dividendYield = ql.FlatForward(todaysDate, tparams["dividendYield"], ql.Actual365Fixed())
volatility = ql.BlackConstantVol(todaysDate, ql.TARGET(), tparams["volatility"], ql.Actual365Fixed())
riskFreeRate = ql.FlatForward(todaysDate, tparams["riskFreeRate"], ql.Actual365Fixed())
process = ql.BlackScholesMertonProcess(
ql.QuoteHandle(underlying),
ql.YieldTermStructureHandle(dividendYield),
ql.YieldTermStructureHandle(riskFreeRate),
ql.BlackVolTermStructureHandle(volatility),
)
if input_dict["engineName"] == "BaroneAdesiWhaleyApproximationEngine":
option.setPricingEngine(ql.BaroneAdesiWhaleyApproximationEngine(process))
elif input_dict["engineName"] == "BjerksundStenslandApproximationEngine":
option.setPricingEngine(ql.BjerksundStenslandApproximationEngine(process))
elif input_dict["engineName"] == "FdBlackScholesVanillaEngine":
timeSteps = input_dict["engineParameters"]["timeSteps"]
gridPoints = input_dict["engineParameters"]["gridPoints"]
option.setPricingEngine(ql.FdBlackScholesVanillaEngine(process, timeSteps, gridPoints))
elif input_dict["engineName"] == "BinomialVanillaEngine":
timeSteps = input_dict["engineParameters"]["timeSteps"]
# possible tree settings: ["JR", "CRR", "EQP", "Trigeorgis", "Tian", "LR", "Joshi4"]
tree = input_dict["engineParameters"]["tree"]
option.setPricingEngine(ql.BinomialVanillaEngine(process, tree, timeSteps))
else:
raise Exception("Unimplemented engineName [{}]".format(input_dict["engineName"]))
value = option.NPV()
return value
# semi-analytic Bates for European
method = 'Bates semi-analytic'
batesProcess = ql.BatesProcess(
flatTermStructure, flatDividendTS, underlyingH,
volatility * volatility, 1.0, volatility * volatility,
0.001, 0.0, 1e-14, 1e-14, 1e-14)
batesModel = ql.BatesModel(batesProcess)
europeanOption.setPricingEngine(
ql.BatesEngine(batesModel))
tab.add_row([method, europeanOption.NPV(), 'N/A', 'N/A'])
# Barone-Adesi and Whaley approximation for American
method = 'Barone-Adesi/Whaley'
americanOption.setPricingEngine(
ql.BaroneAdesiWhaleyApproximationEngine(bsmProcess))
tab.add_row([method, 'N/A', 'N/A', americanOption.NPV()])
# Bjerksund and Stensland approximation for American
method = 'Bjerksund/Stensland'
americanOption.setPricingEngine(
ql.BjerksundStenslandApproximationEngine(bsmProcess))
tab.add_row([method, 'N/A', 'N/A', americanOption.NPV()])
# Integral
method = 'Integral'
europeanOption.setPricingEngine(
ql.IntegralEngine(bsmProcess))
tab.add_row([method, europeanOption.NPV(), 'N/A', 'N/A'])
# Finite differences
ql.BlackVolTermStructureHandle(volatility),
)
# %% [markdown]
# ### Pricing
#
# We'll collect tuples of method name, option value, and estimated error from the analytic formula.
# %%
results = []
# %% [markdown]
# #### Analytic approximations
# %%
option.setPricingEngine(ql.BaroneAdesiWhaleyApproximationEngine(process))
results.append(("Barone-Adesi-Whaley", option.NPV()))
# %%
option.setPricingEngine(ql.BjerksundStenslandApproximationEngine(process))
results.append(("Bjerksund-Stensland", option.NPV()))
# %% [markdown]
# #### Finite-difference method
# %%
timeSteps = 801
gridPoints = 800
# %%
option.setPricingEngine(
