Educational brief
ETF Portfolio Simulation Methodology
Framework design choices for realistic policy testing across diversified ETF allocations.
Updated
In short
Methodology quality drives simulation credibility: assumptions, constraints, and risk metrics must be explicit and reproducible.
Key takeaways
- Transparent assumptions are required for meaningful comparisons.
- Use risk-aware metrics, not terminal value alone.
- Sensitivity analysis helps prevent overfitting to one regime.
Full analysis
What a credible simulation requires
A credible ETF simulation framework requires transparent assumptions, reproducible calculations, and consistent metric definitions. Without this, scenario comparisons become unreliable.
Inputs should include contribution cadence, allocation policy, rebalancing rules, and test-window design.
Risk-first evaluation
Terminal value is important but incomplete. Include drawdown depth, recovery time, downside dispersion, and policy stability under regime shifts.
A policy that protects against severe path risk can be preferable to one that optimizes only median outcomes.
Sensitivity and robustness
Stress assumptions systematically: cadence, allocation weights, rebalance thresholds, and window ranges. Robust policies should remain acceptable under multiple plausible settings.
Robustness testing helps avoid false confidence from overfitting to one period.
How to apply this
Use this topic as one module inside a broader simulation process: define contribution rules, test across rolling windows, and compare drawdown and recovery behavior across regimes before selecting a policy.