China Credit-to-GDP Gap Forecast 2026
Quantitative analysis from 98 observations of China Credit-to-GDP Gap history, joined to four universal macro regime classifications. Numbers are computed, not narrated.
Performance by Window[02]
| WINDOW | N | ANN RET | ANN VOL | RET/VOL | HIT % | TOTAL |
|---|---|---|---|---|---|---|
| 1Y | 5 | -6.67% | 64.98% | -0.10 | 25.0% | -6.67% |
| 3Y | 12 | 15.20% | 56.52% | 0.27 | 27.3% | 47.54% |
| 5Y | 21 | -28.88% | 128.86% | -0.22 | 25.0% | -455.56% |
| 10Y | 40 | 12.64% | 239.16% | 0.05 | 23.1% | -126.78% |
| All | 98 | -4.50% | 289.79% | -0.02 | 41.2% | -390.91% |
Annualized total return = (1 + total)^(1/years) - 1. Ret/Vol is the annualized return divided by annualized volatility (Sharpe-equivalent without risk-free subtraction). Hit % = pct of single periods that were positive.
Where We Are Now[03]
Historical Analogs[06]
Periods where China Credit-to-GDP Gap sat at a similar percentile rank to today, with what happened over the next 30 / 90 / 252 trading days. Analogs are clustered to avoid double-counting nearby dates.
| DATE | VALUE | +30D | +90D | +1Y |
|---|---|---|---|---|
| Jun 30, 2024 | -5.7000 | 0.00% | -5.26% | 7.02% |
| Jun 30, 2023 | -7.5000 | 0.00% | -2.67% | 24.00% |
| Mar 31, 2023 | -7.0000 | 0.00% | -7.14% | 28.57% |
| Mar 31, 2021 | -5.0000 | 0.00% | -72.00% | -144.00% |
| Jun 30, 2018 | -2.0000 | 0.00% | -145.00% | -240.00% |
Worst Historical Drawdown[07]
Largest Single-Period Moves[09]
- Jun 30, 20091000.00%
- Mar 31, 2002600.00%
- Jun 30, 2020580.00%
- Mar 31, 2009107.48%
- Mar 31, 202094.25%
- Jun 30, 2018-281.82%
- Jun 30, 2005-240.00%
- Dec 31, 2020-194.44%
- Mar 31, 2021-194.12%
- Sep 30, 2018-145.00%
Calendar-Month Seasonality[10]
Average single-period return aggregated by the calendar month in which the period ended.
| MONTH | AVG RETURN | HIT % | N |
|---|---|---|---|
| March | 35.71% | 79.2% | 24 |
| June | 38.22% | 50.0% | 24 |
| September | -20.45% | 24.0% | 25 |
| December | -26.98% | 12.5% | 24 |
N = 98 OBS · GENERATED 2026-05-03 08:00Z
Forecast Approach
regime implied: The current macro regime classification (Goldilocks, Reflation, Stagflation, or Deflation) dictates the expected direction and magnitude of movement, calibrated against historical regime performance.
Key Drivers & Risks
- •Macro regime
- •Monetary policy
- •Risk appetite
Historical Volatility
Moderate
Frequently Asked Questions
What factors could push China Credit-to-GDP Gap higher?▾
The primary drivers that tend to lift China Credit-to-GDP Gap depend on the current macro regime. BIS credit-to-GDP gap for China; the benchmark BIS gauge flagged this as the largest positive gap in the world during 2013-2017 before policy-driven deleveraging brought it back to trend. Convex tracks these drivers live across the Global Credit Gap category and flags when multiple forces align in the same direction. See the "Key Drivers & Risks" section on this page for the current list, and check the regime dashboard for how the macro backdrop is currently tilted.
What factors could push China Credit-to-GDP Gap lower?▾
The same transmission channels that drive China Credit-to-GDP Gap higher operate in reverse when conditions flip. The risk drivers listed above map directly to scenarios that, if triggered, would pull this metric in the opposite direction. Convex aggregates these into a scenario-weighted probability distribution rather than a point forecast, so the magnitude depends on which scenarios activate.
Where does consensus see China Credit-to-GDP Gap heading?▾
Rather than publish a point target that goes stale the day after release, Convex assembles consensus from the macro regime classification, active scenario probabilities, and historical base rates. Point forecasts from banks and strategists are worth reading for context, but they typically cluster around the consensus and miss the tail events that actually move markets. The scenario-weighted approach here captures that tail risk explicitly.
What is the historical range for China Credit-to-GDP Gap?▾
Historical ranges for China Credit-to-GDP Gap vary dramatically by regime. A level that is extreme in Goldilocks can be routine in Stagflation, and vice versa. The Historical Volatility section on this page describes the typical range and regime-specific behavior. For the full multi-decade history, visit the China Credit-to-GDP Gap chart page, which includes selectable time ranges up to five years and downloadable data.
How often is the China Credit-to-GDP Gap forecast updated?▾
This forecast page recalculates whenever the underlying data or regime classification changes, typically within hours of new data releases. The scenario probabilities refresh daily as the macro state is regenerated. Specific drivers listed on this page reflect the current state of the Convex regime engine, not static historical assumptions.
Is this forecast actionable for trading?▾
Convex forecasts are informational and educational. They describe probability distributions and regime-conditional paths rather than specific entry and exit levels. Traders and portfolio managers use them alongside other inputs including position sizing rules, risk management, and their own conviction calibration. They are not investment advice.
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Forecasts are model-based projections derived from current regime classification, scenario probabilities, and historical patterns. They are not investment advice. All investments involve risk.