| Horizon | High (P90) | Median | Low (P10) |
|---|---|---|---|
| 30 days | $1,928 increased by 46.00% from reference close | $1,327 increased by 0.50% from reference close | $944.34 decreased by 28.50% from reference close |
| 90 days | $2,853 increased by 116.10% from reference close | $1,322 increased by 0.10% from reference close | $703.20 decreased by 46.70% from reference close |
| 1 year | View 1-year illustrative outlook | ||
Rounded USD prices. Low and high are model P10 and P90; changes are from each snapshot’s reference close.
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The bullish scenario is the model P80 terminal estimate, with its P70-P90 corridor shown as a range.
The bearish scenario is the model P20 terminal estimate, with its P10-P30 corridor shown as a range.
This stressed downside marker extends the fitted loss tail beyond ordinary simulations and should not be read as a precise probability forecast.
Probabilistic scenario analysis, not a guarantee or investment recommendation.
| Year | Target date | Upside scenario | Central scenario | Downside scenario |
|---|---|---|---|---|
| 2026 | Year-end UTC close | $2,207 increased by 67.10% from reference close | $1,325 increased by 0.30% from reference close | $806.92 decreased by 38.90% from reference close |
| 2027 | Year-end UTC close | $6,677 increased by 405.80% from reference close | $1,588 increased by 20.30% from reference close | $332.77 decreased by 74.80% from reference close |
| 2028 | Year-end UTC close | $14,181 increased by 974.10% from reference close | $1,854 increased by 40.40% from reference close | $218.10 decreased by 83.50% from reference close |
| 2029 | Year-end UTC close | $24,636 increased by 1,766.10% from reference close | $2,007 increased by 52.10% from reference close | $156.23 decreased by 88.20% from reference close |
| 2030 | Year-end UTC close | $47,396 increased by 3,490.20% from reference close | $2,409 increased by 82.50% from reference close | $105.64 decreased by 92.00% from reference close |
Illustrative USD scenarios for December 31 through 2030. Percentage changes use the frozen reference close. A simulation uses up to three years of price history and drift shrinkage, adjusted by saved milestone assumptions. These bounds are not calibrated probability intervals; short-term backtests do not validate the annual model.
Reference close: Nov 6, 2026. Each event effect phases in once and is then held constant. Neutral central assumptions leave the central baseline unchanged. Overlapping events and effects already reflected in price can distort results. Review the assumptions with each event; price effects are illustrative editorial inputs.
We use historical Zcash prices to model possible outcomes. Backtested forecasts and fitting-only illustrative outlooks have separate evidence and limitations.
Past price data informs volatility and market patterns.
Models estimate possible future prices. Illustrative outlooks also apply saved milestone assumptions.
Held-out forecasts are scored against a simple baseline. This does not validate the separate illustrative outlooks.
CRPS improvement -0.38%; one-sided 95% lower bound -3.20%.
The forecast combines a horizon-aware, drift-shrunk Student-t GARCH model, filtered historical simulation, and regularized quantile regression. Nonnegative model weights are learned from earlier Monday-UTC forecast dates without using future data. An embargo of 12 weekly origins separates calibration from evaluation, followed by 20 held-out forecasts spaced 91 days apart. Performance is measured using CRPS and coverage diagnostics against the strongest predeclared simple baseline. An EVT model extends the downside tail for the black-swan stress scenario.
Price data CoinMarketCap, CryptoSlate
Momentum shifts the regularized quantile component while shrinkage limits extrapolation.
Higher conditional volatility widens both upside and downside predictive bands.
The first release retains a zero-weight boundary until historical overlay validation is available.
Past market behavior may not represent a new market structure or regulatory shock.
The forecast targets one UTC terminal price and does not predict the full intraday path.
Actual prices can finish outside every displayed interval.
Non-overlapping labels reduce mechanical overlap, but regime dependence can persist and manual review remains required.
An origin is a historical date when the model is tested as if it were issuing a live forecast.
Public raw backtest origins are fixed to Monday UTC to prevent schedule shopping, meaning results cannot be improved by choosing unusually favorable start dates. This horizon requires at least 2,714 continuous daily candles, including up to six days of weekday-alignment slack.
Calibration origins are earlier forecasts used to choose the ensemble weights. Held-out origins are kept separate and used only to evaluate the chosen model.
This run used 72 weekly calibration origins and compared weight selection with filtered historical simulation. An embargo then skipped 12 weekly dates, creating a deliberate gap before evaluation.
The final 20 held-out origins were issued 91 days apart across a 248-position validation span so their 90-day outcomes do not overlap.
CRPS stands for Continuous Ranked Probability Score. It measures the accuracy of the full probability forecast rather than a single price target; lower is better because it rewards both an accurate center and honest uncertainty bands.
The held-out point edge against the strongest simple baseline was -0.4%. The minimum one-sided 95% paired lower bound was -3.2%; this conservative check asks whether the measured advantage remains above zero after accounting for sample variation.
The monitoring target is a point edge of at least 2% and a positive lower bound against both GARCH and filtered historical simulation. These regime-dependent diagnostics remain advisory and do not interrupt publication.
A simulation path is one plausible 90-day price journey drawn by the model.
The published run used 2,000 paths for the displayed distribution and 2,000 paths at each backtest origin.
RNG seed 736,117,530,062,223,712 fixes the random-number stream so an identical run can be reproduced. It is deterministically derived from the frozen forecast.v1 production salt, horizon, asset ID, and modeled reference-close timestamp; candles, provenance, and market inputs do not select the random stream. The modeled reference close is timestamped 2026-11-06T23:59:59Z.
No quality-qualified Polymarket evidence contributed to this forecast, so prediction-market prices did not move the published distribution.
Showing retained 90-day forecasts. No targets have an exact stored close available for evaluation yet.
Scroll to view all columns
| Forecast date | Target date | Median | 80% range | Actual close | Result |
|---|---|---|---|---|---|
| $1,262.67 | $661.78 to $2,703.28 | — | Target pending | ||
| $826.41 | $424.03 to $1,909.01 | — | Target pending | ||
| $836.41 | $349.74 to $2,106.29 | — | Target pending |
One first-retained forecast per two-month calendar period, up to six rows. Completed checkpoints take priority. Values stay fixed; results use exact CoinMarketCap USD daily closes. The displayed sample’s hit rate excludes pending targets and missing closes. Past results do not guarantee future accuracy.
What public figures said, when they said it, and the targets they set. These statements are separate from the CryptoSlate forecast.
Mumtaz reaffirmed a long-term $10,000 ZEC target amid the January 2026 governance dispute. He argued that the former ECC team’s move to a new structure could help development and framed his view around encrypted money. CryptoSlate did not report a target date or a detailed valuation model.
Hayes projected ZEC could reach $10,000 as a privacy investment, as reported in November 2025. No deadline was stated. This is a historical forecast: CryptoSlate later reported on June 5, 2026 that he had sold his entire ZEC position after reassessing the privacy thesis following the Orchard vulnerability disclosure.
Historical statements retain their original context. Targets may change; inclusion is not endorsement or a claim of accuracy.
Backtested forecasts model prices at a stated target date using historical returns and volatility. Separately labeled illustrative outlooks fit scenarios to available price history with any saved milestone assumptions. The Method section identifies the inputs and validation evidence for the selected run.
In the backtested forecast table, low and high are the 10th and 90th model percentiles at the target date. Actual outcomes can fall outside them. Separately labeled illustrative outlooks use downside, central and upside scenarios with editorial milestone assumptions; their bounds are not calibrated probability intervals.
Enough daily price history is available to fit a one-year model, but held-out one-year validation is unavailable. Its upside, central and downside scenarios are illustrative, not calibrated probabilities. A shorter-horizon backtest does not validate this outlook.
The one-year target is 365 days after its reference close. By-year rows are December 31 UTC closes, so the target dates can differ. The illustrative one-year and year-end targets use the same frozen simulation and saved milestone assumptions.
Check the Method section and Track record where available. Backtests compare earlier forecasts with known outcomes and a simple baseline; a result for one horizon does not validate another. Historical performance does not establish future accuracy, and annual scenarios and illustrative outlooks have separate limitations.
No. A terminal distribution describes prices at a target date. It does not give the probability of touching a threshold at any point before that date.
Each forecast uses a frozen reference close. Current quotes can change after the run. Forecast percentage changes are measured from that reference close, not from the latest quote.
Price targets require enough usable historical data and an available published model output. A horizon can also be paused or withheld. Sections such as annual forecasts, milestones and prediction markets are omitted when they have no published content.
No. Model assumptions, limited historical samples and changing market conditions create substantial uncertainty. Older snapshots are labeled, and paused forecasts withhold current targets.
