Monte Carlo Option Price
Calculate European option prices through Monte Carlo simulation of Geometric Brownian Motion (GBM) stock price paths.
The Monte Carlo method prices an option by simulating a large number of possible future paths for the underlying stock price under the risk-neutral measure, computing the option’s payoff at expiration for each simulated path, and then discounting the average payoff back to the present:
- S(t + Δt) = S(t) * e^((r - q - σ²/2) * Δt + σ * √Δt * Z)
- Call Price = e^(-r * T) * mean(max(S(T) - K, 0))
- Put Price = e^(-r * T) * mean(max(K - S(T), 0))
Where S(t) is the stock price at time t, r is the risk-free rate, q is the dividend yield, σ is the volatility, Δt is the length of a single time step and Z is a standard normal random variable.
As it is a simulation, the result comes with sampling error. Set show_standard_error=True to additionally return the standard error of each estimate - as a rule of thumb, the true price lies within plus or minus 2 times the standard error roughly 95% of the time. A fixed seed is used by default for reproducibility of documentation examples; set it explicitly (or leave it as None) to control this behavior.
Also known as: Monte Carlo option pricing, simulation-based option pricing.
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Calculate the Monte Carlo Option Price in Python
The Monte Carlo Option Price is available in the Options module of the open-source Finance Toolkit. Install it with:
pip install financetoolkit -U
Then call get_monte_carlo_option_price as shown below.
from financetoolkit import Toolkit
toolkit = Toolkit(["AMZN", "AAPL"], api_key="FINANCIAL_MODELING_PREP_KEY")
monte_carlo = toolkit.options.get_monte_carlo_option_price(seed=42)
monte_carlo.loc['AMZN']
Parameters
get_monte_carlo_option_price accepts the following parameters:
- start_date (str | None, optional): The start date which determines the stock price. Defaults to None which means it will use the most recent date.
- put_option (bool, optional): Whether to calculate the put option price. Defaults to False which means it will calculate the call option price.
- strike_price_range (float): The percentage range to use for the strike prices. Defaults to 0.25 which equals 25% and thus results in strike prices from 75 to 125 if the current stock price is 100.
- strike_step_size (int): The step size to use for the strike prices. Defaults to 5 which means that the strike prices will be 75, 80, 85, 90, 95, 100, 105, 110, 115 and 120 if the current stock price is 100.
- expiration_time_range (int): The number of days to use for the time to expiration. Defaults to 30 which equals 30 days.
- risk_free_rate (float, optional): The risk free rate to use for the calculation. Defaults to None which means it will use the current risk free rate.
- dividend_yield (float, optional): The dividend yield to use for the calculation. Defaults to None which means it will use the dividend yield as obtained through annual historical data.
- simulations (int, optional): The number of simulated stock price paths. Defaults to 10,000.
- time_steps (int, optional): The number of time steps used to build each simulated path. Defaults to 100.
- seed (int | None, optional): The seed used to initialize the random number generator, ensuring reproducible results. Defaults to None, which means the results will not be reproducible.
- show_standard_error (bool, optional): Whether to also return the standard error of each Monte Carlo estimate as a second DataFrame. Defaults to False.
- show_input_info (bool, optional): Whether to show the input information. Defaults to False.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to 4.
- standardize (bool, optional): Whether to standardize (Z-Score) the result across the time to expiration columns for each ticker and strike price. Defaults to False.
Related Option Pricing
The Options module page introduces the module, and the sidebar lists all of its functions.