2

我的理解是，为了提前一天，你做这样的事情：

ql.Settings.instance().evaluation_date = calculation_date + 1

但是，当我执行以下代码时，我得到的选项值相同：

import QuantLib as ql     

# option data
maturity_date = ql.Date(15, 1, 2016)
spot_price = 127.62
strike_price = 130
volatility = 0.20 # the historical vols for a year
dividend_rate =  0.0163
option_type = ql.Option.Call

risk_free_rate = 0.001
day_count = ql.Actual365Fixed()
#calendar = ql.UnitedStates()
calendar = ql.TARGET()

calculation_date = ql.Date(8, 5, 2015)
ql.Settings.instance().evaluationDate = calculation_date

# construct the European Option
payoff = ql.PlainVanillaPayoff(option_type, strike_price)
exercise = ql.EuropeanExercise(maturity_date)
european_option = ql.VanillaOption(payoff, exercise)

spot_handle = ql.QuoteHandle(
    ql.SimpleQuote(spot_price)
)

flat_ts = ql.YieldTermStructureHandle(
    ql.FlatForward(calculation_date, risk_free_rate, day_count)
)

dividend_yield = ql.YieldTermStructureHandle(
    ql.FlatForward(calculation_date, dividend_rate, day_count)
)

flat_vol_ts = ql.BlackVolTermStructureHandle(
    ql.BlackConstantVol(calculation_date, calendar, volatility, day_count)
)

bsm_process = ql.BlackScholesMertonProcess(spot_handle, 
                                           dividend_yield, 
                                           flat_ts, 
                                           flat_vol_ts)

european_option.setPricingEngine(ql.AnalyticEuropeanEngine(bsm_process))
bs_price = european_option.NPV()
print "The theoretical European price is ", bs_price   

payoff = ql.PlainVanillaPayoff(option_type, strike_price)
settlement = calculation_date
am_exercise = ql.AmericanExercise(settlement, maturity_date)
american_option = ql.VanillaOption(payoff, am_exercise)

#Once you have the american option object you can value them using the binomial tree method:

binomial_engine = ql.BinomialVanillaEngine(bsm_process, "crr", 100)
american_option.setPricingEngine(binomial_engine)
print "The theoretical American price is ", american_option.NPV()                                  

ql.Settings.instance().evaluation_date = calculation_date + 1

print "The theoretical European price is ", european_option.NPV() 
print "The theoretical American price is ", american_option.NPV()

[idf@node3 python]$ python european_option.py 
The theoretical European price is  6.74927181246
The theoretical American price is  6.85858045945
The theoretical European price is  6.74927181246
The theoretical American price is  6.85858045945
[idf@node3 python]$

编辑

按照下面的建议将代码更改为，但日期更改对计算没有影响。

[idf@node3 python]$ python advance_day.py 
The theoretical European price is  6.74927181246
The theoretical American price is  6.85858045945
The theoretical European price is  6.74927181246
The theoretical American price is  6.85858045945
[idf@node3 python]$

以下是根据建议的代码更改。

import QuantLib as ql     

# option data
maturity_date = ql.Date(15, 1, 2016)
spot_price = 127.62
strike_price = 130
volatility = 0.20 # the historical vols for a year
dividend_rate =  0.0163
option_type = ql.Option.Call

risk_free_rate = 0.001
day_count = ql.Actual365Fixed()
#calendar = ql.UnitedStates()
calendar = ql.TARGET()

calculation_date = ql.Date(8, 5, 2015)
ql.Settings.instance().evaluationDate = calculation_date

# construct the European Option
payoff = ql.PlainVanillaPayoff(option_type, strike_price)
exercise = ql.EuropeanExercise(maturity_date)
european_option = ql.VanillaOption(payoff, exercise)

spot_handle = ql.QuoteHandle(
    ql.SimpleQuote(spot_price)
)

flat_ts = ql.YieldTermStructureHandle(
    ql.FlatForward(0, calendar, risk_free_rate, day_count)
)

dividend_yield = ql.YieldTermStructureHandle(
    ql.FlatForward(0, calendar, dividend_rate, day_count)
)

flat_vol_ts = ql.BlackVolTermStructureHandle(
    ql.BlackConstantVol(0, calendar, volatility, day_count)
)

bsm_process = ql.BlackScholesMertonProcess(spot_handle, 
                                           dividend_yield, 
                                           flat_ts, 
                                           flat_vol_ts)

european_option.setPricingEngine(ql.AnalyticEuropeanEngine(bsm_process))
bs_price = european_option.NPV()
print "The theoretical European price is ", bs_price   

payoff = ql.PlainVanillaPayoff(option_type, strike_price)
settlement = calculation_date
am_exercise = ql.AmericanExercise(settlement, maturity_date)
american_option = ql.VanillaOption(payoff, am_exercise)

#Once you have the american option object you can value them using the binomial tree method:

binomial_engine = ql.BinomialVanillaEngine(bsm_process, "crr", 100)
american_option.setPricingEngine(binomial_engine)
print "The theoretical American price is ", american_option.NPV()                                  

ql.Settings.instance().evaluation_date = calculation_date + 1
# Also tried calendar.advance(calculation_date,1,ql.Days)

print "The theoretical European price is ", european_option.NPV() 
print "The theoretical American price is ", american_option.NPV()
4

1 回答 1

2

计算日期并不是全部。您正在设置曲线，以便它们的参考日期是固定的（也就是说，您正在调用获取日期的构造函数；有关详细信息，请参阅此帖子，或此视频作为示例）。

如果您指定参考日期，则该参考日期独立于计算日期使用；这是因为它们不一定相同（例如，您可能希望利率曲线基于即期日期而不是今天的日期）。因此，即使您更改了计算日期，从曲线返回的波动率和利率仍将与其参考日期相关，而参考日期并未发生变化。

为了获得您想要的效果，您可以创建曲线，以便它们随着评估日期移动；例如，而不是

ql.FlatForward(calculation_date, risk_free_rate, day_count)

您可以使用

ql.FlatForward(0, calendar, risk_free_rate, day_count)

这意味着将参考日期指定为“计算日期后的0个工作日”，即计算日期。波动率曲线具有类似的构造函数。

于 2017-01-04T21:42:45.550 回答