Geopolitics risk-off · 1–3 years
A what‑if from the future

What if China dumped its US Treasury holdings as a weapon?

If China weaponizes its Treasury stock, the long end sells hardest (30y > 10y) on fading reserve demand, the dollar softens, and gold/Bitcoin bid as non-sovereign reserve hedges — the cleanest single chain is bear-steepener plus weaker DXY. The template is the Feb-2022 reserve freeze and the 1971 Nixon shock, both reserve-confidence regime breaks. Skeptic's note: China selling tanks the value of its own remaining holdings and pushes CNY up against its export interest, so a full dump is self-limiting — fade the most extreme yield overshoot.

8%
our model probability
over 1–3 years
prediction markets — the market's odds
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Anchored to measured history 8% · 90% range 0–20% · 35 dated precedents behind it — a wider range means thinner evidence

Every number ships with its receipt — the odds, the range, the precedents, and a public grade at Reality Check. The statistical machinery that produces it is proprietary.

The butterfly cascade

How this trigger trickles across markets, left → right — the root shock, its first‑order moves, then the ripple effects. Drag any node; tap a market for its real price history.

Resolution timeline — how this probability is moving

Our model's odds (electric blue) over time vs the market's (Polymarket, amber), from the past toward the 1–3 years horizon. Each dot is a real macro event that nudged the probability — green pushed it up, red pushed it down. Tap a dot for the source. Loading the probability audit trail…

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What it would mean

If this plays out, it is a risk-off shock. China weaponizes its Treasury holdings, selling aggressively into the market. The trigger decomposes into signed root‑shocks — Dollar/reserve confidence ▼ — which propagate through our causal graph to the markets below.

Methodology. Probability and impact are anchored to history and scored against what actually happens — wins and losses, in public, at Reality Check. Market odds live from Polymarket & Kalshi. By Vikas Singh, Quantitative Strategist. Updated 2026-08-13.