01 / THE PRACTICAL QUESTION
A decision, before a model.
Before trusting simulated losses, an analyst needs to establish that the simulation engine can reproduce a known option price.
Choose enough simulated paths to make sampling error visible and acceptable.
02 / DATA & COMPARISON
The idea in plain language.
Monte Carlo repeats a model of price changes many times. More paths reduce sampling noise, but cannot repair a wrong model of the market.
- Standard error
- Uncertainty caused by using a finite number of simulated paths.
Data. Seeded geometric Brownian motion and specified correlations.
Baseline. Analytical moments and option values.
03 / THE EXPERIMENT
What the saved experiment shows.
The saved 100,000-path call estimate is 9.388, versus an analytical price of 9.413. The analytical value lies inside the simulated 95% interval, 9.300–9.475, for this one-year option.
Inspect the supporting resultEvidence record: research-validation.json#numerical_checks/02
Explore the related lab04 / RESULTS & LIMITATIONS
Evidence with its boundaries attached.
The related lab is a cross-project demonstration. Read this investigation’s evidence and limits before transferring its conclusions.
Inspect numerical checks and validation records
Loading validation evidence…
GBM assumes continuous paths and a fixed diffusion law; jumps require a different model.
05 / REPRODUCE
Reproduce and challenge the result.
Code, configuration, and reproduction
The project contains its implementation, configuration, tests, and walkthrough. Download the lab configuration to record the exact parameters used in an interactive run.
Project code and walkthrough