About vs. Demo: This demo folder now uses a standardized launch page. Start with About Demo, then choose the run mode that fits your class.

Quant Finance • Browser • 35-45 min

Monte Carlo Options

Based on the **Law of Large Numbers** - as sample size increases, the sample mean converges to the expected value.

Open About Demo Read README View Course Path

Monte Carlo simulation generates thousands of possible stock price paths to estimate option prices. More simulations = more accurate results.

Market Parameters

Simulation Settings

Simulation Results

Call Option Price

$10.45

Put Option Price

$5.57

Standard Error

$0.12

Convergence

95%

Monte Carlo Method

  • Step 1: Generate random normal variables
  • Step 2: Simulate stock price paths using GBM
  • Step 3: Calculate payoff for each path
  • Step 4: Average payoffs and discount

Key Insight: More paths reduce standard error and improve accuracy.

Standard demo guide

Use this demo in a logical learning sequence

Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

What this demo is about

Based on the **Law of Large Numbers** - as sample size increases, the sample mean converges to the expected value.

Learning objectives

  • Explain the main quant decision that Monte Carlo Options is designed to support.
  • Change input assumptions and predict how the output should respond before running the demo.
  • Interpret the result in plain language, not just as a number, chart, or AI recommendation.

Run mode and expectations

  • Supported modes: Browser
  • Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

Step 1: Inputs

  • Start with the default assumptions, then change one variable at a time so students can isolate cause and effect.
  • Treat each input as a lever that changes the scenario, baseline, or business context behind the result.

Step 2: Decision buttons

  • Use the main run or simulate action to compute the scenario after inputs are set.
  • Use export or reset actions, when present, to compare runs or return to a classroom-safe baseline.

Step 3: Outputs and what to notice

  • Read the top-line result first, then look for supporting metrics, tables, or narratives that explain why it changed.
  • Students should explain whether the output is descriptive, predictive, simulated, or recommended.
  • Look for simulated paths, payoff calculation, and convergence behavior
  • Observe how more simulations stabilize the estimated option value

Available run modes

  • Browser: available for this demo.

How to proceed

  1. Choose the run mode that fits the class: Browser.
  2. Review the default assumptions before changing anything.
  3. Change one or two inputs, then use `Run the main action`.
  4. Read the output first, then compare any supporting metrics, charts, or AI text.
  5. Capture one insight, one limitation, and one action recommendation.