We generate 10,000+ stochastic paths for each simulation run, sampling from multivariate normal distributions calibrated to historical asset class characteristics. Each path represents a plausible future scenario for portfolio evolution.

Annual returns are simulated using mean-variance parameters and correlation matrices, with proper treatment of compounding effects. This captures both expected returns and realistic volatility across multiple asset classes.

To preserve correlations between asset classes, we apply Cholesky decomposition to the correlation matrix. This mathematical technique ensures that simulated returns maintain historically observed co-movement patterns.

The decomposition transforms independent random variables into correlated returns, accurately capturing diversification benefits and portfolio risk. This is critical for multi-asset portfolios where correlations drive risk-adjusted performance.

Supports multiple rebalancing strategies: annual, quarterly, or threshold-based. Each simulation tracks asset-level drift and executes rebalancing trades when specified conditions are met.

Counter-factual analysis runs parallel simulations with identical random seeds—one with rebalancing and one without—enabling direct measurement of rebalancing impact on risk and return. This isolates the true value-add of systematic rebalancing.

Risk attribution decomposes total portfolio volatility into marginal contributions from each asset class. This accounts for both individual asset volatility and correlation effects.

Calculated using the Euler allocation principle: each asset's risk contribution equals its weight multiplied by its marginal contribution to total risk. Enables identification of concentrated risk exposures that may not be obvious from allocation weights alone.

Asset classes are classified as liquid or illiquid based on redemption characteristics. The model tracks liquid asset percentages over time, ensuring portfolios maintain adequate liquidity buffers for spending and operational needs.

Automatically classifies private equity, private credit, real estate, infrastructure, and similar strategies as illiquid. Public equity, fixed income, and cash equivalents are treated as liquid. Critical for institutions with governance-imposed liquidity requirements.

Flexible spending policy implementation with support for percentage-of-market-value rules, smoothed spending with rolling averages, inflation adjustments, operating expenses, and grant distributions. IRS 5% minimum distribution option available.

Spending is calculated each period based on current endowment value, policy rate, and smoothing parameters. Adjusts for inflation to maintain real purchasing power over multi-decade horizons. Tracks spending sustainability and volatility metrics.

Scenario analysis capabilities allow users to model asset class shocks and inflation regime shifts. Apply discrete percentage shocks to specific asset classes in designated years (e.g., -30% equity shock in year 3) to test portfolio resilience.

Inflation stress testing models sustained shifts in CPI growth across specified periods. Critical for evaluating spending sustainability during high-inflation regimes. Scenarios can combine multiple shocks to model complex crisis environments.

Users can specify expected returns, volatilities, and correlation matrices for each asset class based on their own capital market assumptions or empirical research. Supports calibration to historical data, forward-looking views, or vendor forecasts.

Default parameters draw from long-term academic research and institutional best practices, but all assumptions are fully transparent and adjustable. This flexibility enables sensitivity analysis across different market regime expectations.