Workflow:Avhz RustQuant Monte Carlo Option Pricing
| Knowledge Sources | |
|---|---|
| Domains | Quantitative_Finance, Option_Pricing, Monte_Carlo, Derivatives |
| Last Updated | 2026-02-07 19:00 GMT |
Overview
End-to-end process for pricing exotic and path-dependent options using Monte Carlo simulation with configurable stochastic processes.
Description
This workflow covers Monte Carlo pricing of options whose payoffs depend on the entire price path, not just the terminal value. RustQuant supports pricing vanilla, Asian (arithmetic discrete averaging), power, barrier, and other exotic options by simulating underlying asset paths using stochastic processes (primarily Geometric Brownian Motion) and computing discounted expected payoffs. The simulation engine supports configurable step counts, number of paths, and optional parallelism.
Usage
Execute this workflow when you need to price options that lack closed-form solutions, such as Asian options, barrier options, lookback options, or options with custom payoff functions. This is also appropriate when you want to validate analytic prices via simulation or when working with complex payoff structures.
Execution Steps
Step 1: Define Market Parameters
Specify the underlying asset parameters: spot price, strike price, risk-free rate, time to maturity, and volatility. These parameters define the market environment for the simulation.
Key considerations:
- Spot price is the current price of the underlying asset
- Strike price defines the option exercise level
- Risk-free rate is the continuously compounded interest rate
- Volatility is the annualized standard deviation of log-returns
Step 2: Configure the Stochastic Process
Instantiate a stochastic process model (typically Geometric Brownian Motion for equity options) and configure the simulation parameters: initial value, time range, number of time steps, discretization scheme, number of simulation paths, and whether to use parallel execution.
Process configuration includes:
- Initial asset value (spot price)
- Start and end time for the simulation period
- Number of discrete time steps (e.g., 365 for daily steps)
- Stochastic discretization scheme (Euler-Maruyama)
- Number of Monte Carlo paths (e.g., 100,000 for convergence)
- Parallel execution toggle for multi-core acceleration
Step 3: Define the Option Contract
Construct the specific option type to price. For exotic options, this includes specifying the exercise style (European, American), the strike type (fixed, floating), and any path-dependent features such as averaging method (Asian) or barrier levels.
Supported option types:
- EuropeanVanillaOption - Standard call/put with terminal payoff
- AsianOption - Payoff depends on average price over the path (arithmetic or geometric)
- PowerOption - Payoff raised to a specified power
- BarrierOption - Payoff contingent on the path touching (or not touching) a barrier level
- Custom payoffs - User-defined payoff functions applied to generated paths
Step 4: Run Monte Carlo Simulation
Execute the pricing by generating stochastic paths and computing the discounted expected payoff across all simulated paths. The pricer generates the paths internally, evaluates the payoff function for each path, discounts back to present value, and averages the results.
What happens:
- The stochastic process generates the specified number of sample paths
- Each path is evaluated against the option payoff function
- Results are discounted by the risk-free rate and averaged
- The output is the estimated fair value of the option
Step 5: Evaluate Results
Collect and analyze the Monte Carlo price estimates. For custom payoffs (e.g., barrier options), the user applies their payoff logic directly to the generated path data. Results converge to the true price as the number of simulations increases.
Key considerations:
- Increase path count for better convergence (standard error scales as 1/sqrt(N))
- Compare Monte Carlo results against analytic solutions when available
- Barrier option payoffs require checking if any path value breaches the barrier level