Inverse-Volatility Allocation vs Inverse-Volatility (GPW)
Two published track records side by side — same data, same methodology, zero verdicts.
Inverse-Volatility Allocation
coreSpreads capital across the universe, giving more weight to lower-volatility names. Recomputed monthly.
Full track record →
Inverse-Volatility (GPW)
coreWarsaw blue chips (WIG20), each weighted inversely to its recent volatility and recomputed monthly — calmer names get more, jumpier less. Low volatility on a narrow 20-name index: no edge over the equal-weight WIG20 benchmark beside it is claimed. Simulated with real Warsaw costs (20 bps) on point-in-time WIG20 membership (archived to 2025-09, held flat after). Depending on the feed, up to 3 of 28 historical members are unpriced early on (2020-2022), so the earliest drawdowns read a touch optimistic.
Full track record →
The numbers, side by side
| Inverse-Volatility Allocation | Inverse-Volatility (GPW) | |
|---|---|---|
| CAGR | 11.2% | 18.3% |
| Volatility (ann.) | 15.6% | 20.8% |
| Sharpe | 0.55 | 0.78 |
| Sortino | 0.79 | 1.15 |
| Max drawdown | -27.2% | -40.5% |
| Calmar | 0.41 | 0.45 |
| Simulated since | 2020-12 | 2020-12 |
Simulated backtests (up to 6 years of point-in-time data, costs included) — not live results. Methodology: here. Past performance does not predict future results.
What the data says
- Inverse-Volatility (GPW) posted the higher CAGR (18.3% vs 11.2%).
- Inverse-Volatility Allocation fell less at its worst (-27.2% vs -40.5%).
- Inverse-Volatility Allocation had the calmer ride (volatility 15.6% vs 20.8%).
These are facts from the published snapshots, not a recommendation — the rules differ in their risk profile. Overlay both equity curves in the interactive comparison tool.