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.

Risk Management • Browser • 35-45 min

Risk Parity Portfolio

A portfolio construction approach that **equalizes risk contribution** from each asset rather than equal weight.

Open About Demo Read README View Course Path

Risk parity allocates capital so each asset contributes equally to portfolio risk. Unlike equal weighting, it accounts for volatility differences.

Asset Volatilities

Portfolio Allocation

Asset A

40.8%

Asset B

34.0%

Asset C

25.2%

Risk Contribution Analysis

Risk A

33.3%

Risk B

33.3%

Risk C

33.3%

Portfolio Vol

22.9%

Risk Parity Formula

Weight_i = (σ_i)^(-1) / Σ_j (σ_j)^(-1)

Each asset's risk contribution = Weight_i × σ_i / Σ_j (Weight_j × σ_j)

Key Insight: Lower volatility assets get higher weights!

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

A portfolio construction approach that **equalizes risk contribution** from each asset rather than equal weight.

Learning objectives

  • Explain the main risk decision that Risk Parity Portfolio 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 asset risk contribution, allocation weights, and portfolio volatility
  • Observe how risk parity differs from capital-weighted allocation

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.