equity power option

financepy.products.equity.equity_power_option

Class

EquityPowerOption

EquityPowerOption(
    expiry_dt: Date | List,
    strike_price: float | numpy.ndarray,
    opt_type: OptionTypes,
    power: float = 1.0,
    num_options: float = 1.0,
) -> None

Inherits from: EquityOption

Class for managing European power option calls and puts on equities.

Methods

intrinsic


intrinsic(
    self,
    value_dt: Date,
    stock_price: numpy.ndarray | float,
    discount_curve: DiscountCurve,
    dividend_curve: DiscountCurve,
)

Calculate the discounted intrinsic value of the option.

value


value(
    self,
    value_dt: Date,
    stock_price: float | numpy.ndarray,
    discount_curve: DiscountCurve,
    dividend_curve: DiscountCurve,
    model: Model,
)

Equity Vanilla Option valuation using Black-Scholes model.

delta


delta(
    self,
    value_dt: Date,
    stock_price: float,
    discount_curve: DiscountCurve,
    dividend_curve: DiscountCurve,
    model: Model,
)

Analytical spot delta for a European power call or put.

value_mc


value_mc(
    self,
    value_dt: Date,
    stock_price: float,
    discount_curve: DiscountCurve,
    dividend_curve: DiscountCurve,
    model: Model,
    num_paths: int = 10000,
    seed: int = 4242,
) -> MCResult

Value the power option using Monte Carlo simulation. The terminal stock price is simulated under the risk-neutral measure: S(T) = S(0) * exp( (r - q - 0.5 * sigma**2) * T + sigma * sqrt(T) * Z ) and the payoff is: Call: max(S(T)**n - K, 0) Put: max(K - S(T)**n, 0) Parameters ---------- value_dt : Date Valuation date. stock_price : float Current stock price. discount_curve : DiscountCurve Risk-free discount curve. dividend_curve : DiscountCurve Dividend discount curve. model : Model Black-Scholes model. num_paths : int Number of Monte Carlo paths. seed : int Random number seed. Returns ------- MCResult Monte Carlo value and standard error.

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