障碍期权定价python

障碍期权的定价可以使用Black-Scholes模型或者Binomial Tree模型。Python中可以使用Quantlib库来进行障碍期权的定价。

以下是一个使用Quantlib库来进行障碍期权定价的Python代码示例：

import QuantLib as ql

# 定义期权参数
barrier_type = ql.Barrier.UpOut # 障碍类型
barrier = 100 # 障碍价
exercise_date = ql.Date(31, 12, 2022) # 行权日期
option_type = ql.Option.Call # 期权类型
strike_price = 100 # 行权价
underlying_price = 95 # 标的资产价格
risk_free_rate = 0.05 # 无风险利率
dividend_rate = 0.02 # 分红率
volatility = 0.2 # 波动率

# 构造欧式期权和障碍期权
payoff = ql.PlainVanillaPayoff(option_type, strike_price)
european_option = ql.EuropeanOption(payoff, ql.EuropeanExercise(exercise_date))
barrier_option = ql.BarrierOption(barrier_type, barrier, 0, payoff, ql.EuropeanExercise(exercise_date))

# 构造Black-Scholes过程
spot_handle = ql.QuoteHandle(ql.SimpleQuote(underlying_price))
flat_ts = ql.YieldTermStructureHandle(ql.FlatForward(0, ql.TARGET(), risk_free_rate, ql.Actual365Fixed()))
dividend_yield = ql.YieldTermStructureHandle(ql.FlatForward(0, ql.TARGET(), dividend_rate, ql.Actual365Fixed()))
flat_vol_ts = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(0, ql.TARGET(), volatility, ql.Actual365Fixed()))
bs_process = ql.BlackScholesProcess(spot_handle, dividend_yield, flat_ts, flat_vol_ts)

# 计算欧式期权和障碍期权的价值
european_option.setPricingEngine(ql.AnalyticEuropeanEngine(bs_process))
barrier_option.setPricingEngine(ql.AnalyticBarrierEngine(bs_process))
european_option_value = european_option.NPV()
barrier_option_value = barrier_option.NPV()

print("European option value: ", european_option_value)
print("Barrier option value: ", barrier_option_value)

这个例子中使用了Black-Scholes模型进行期权定价，如果要使用Binomial Tree模型，则需要使用`ql.BinomialTree`类来构造期权定价模型。