Granger Causality: Lead-Lag Across Major Stablecoin Pairs
· Updated daily at 15:20 UTC
Granger causality tests whether one stablecoin's market cap changes predict another's, beyond the second coin's own history (Granger, 1969). It is not classical causation, it is predictive precedence in the time-series sense. This page tracks lead-lag relationships across four pairs spanning the major stablecoin design types: fiat-backed incumbents (USDT/USDC), incumbent vs algorithmic (USDT/DAI, USDC/DAI), and incumbent vs yield-bearing synthetic (USDT/USDE). Select a pair below to inspect its dynamics over the rolling window of your choice. The "All Pairs at a Glance" panel shows where significant lead-lag relationships currently exist across the market.
Rolling Granger F-Statistic
Rolling Granger F-statistic for both directions of the selected pair over the selected window. Amber dashes mark F = 3.84 (p < 0.05); coral dashes mark F = 6.63 (p < 0.01). Values above a threshold reject the null that the leading series adds no predictive value. Sustained crossing of the 3.84 line signals an emerging structural lead-lag relationship.
Current F-Statistics. Both Directions
F-statistics for both directions of the selected pair at the selected window. Bars are coloured when above F = 3.84 (p < 0.05). A higher bar in one direction indicates that direction as the primary predictor. Amber dashes mark the significance threshold.
Window Sensitivity, 30D / 60D / 90D
F-statistics for the selected pair across all three rolling windows. Consistency across windows signals a robust structural relationship. Divergence suggests the lead-lag may be regime-specific rather than persistent.
All Pairs at a Glance
Current F-statistics across all four pairs and both directions at the selected window. Bars above the amber line (F = 3.84) are statistically significant lead-lag relationships. This panel reveals where in the stablecoin market predictive precedence currently exists, independent of which pair you have selected above.
The F-statistic exceeds the 99% significance threshold. Within the tested window, lagged changes in the leading series contain statistically significant predictive information about the following series. Granger results indicate predictive content within the sample, not economic causality, and the relationship can be unstable across regimes.
For enterprise: Lagged changes in the leading issuer's series contain predictive information for the follower within this sample; treat as one input, not a confirmed leading indicator. For policymakers: The leading issuer's flows are worth monitoring alongside other liquidity and concentration metrics.
F-statistic is above 95% threshold but below 99%. The relationship is statistically significant but may be driven by a specific regime or short period. If this coincides with a market expansion or stress episode, it may reflect a temporary information spillover rather than a structural lead.
For enterprise: Treat as confirming, not standalone, signal. Monitor across subsequent windows. For policymakers: Worth tracking; may solidify into a robust lead over time.
Neither series provides statistically significant predictive information about the other. The two coins are likely responding to the same contemporaneous signals, macro liquidity conditions, crypto market flows, ETF or treasury flows, rather than to each other's lagged signals. This is the expected baseline for mature, liquid markets where information propagates quickly across issuers.
For enterprise: No exploitable lead-lag at this window, both issuers react simultaneously to demand. For policymakers: Market is informationally efficient across the two issuers; neither sets a leading signal.
Test: Granger causality F-test (Granger, 1969) with 1 lag. Tests whether lagged values of X significantly improve predictions of Y beyond Y's own history. Implemented by comparing a restricted model (AR(1) in Y) to an unrestricted model (AR(1) in Y + lagged X). F-statistic: F = ((RSS_r − RSS_u) / 1) / (RSS_u / (n − 3)), where q = 1 restriction, k = 3 parameters (intercept + Y_lag + X_lag). The Frisch–Waugh–Lovell theorem is used for numerical stability: the X coefficient is estimated from residuals after partialling out Y_lag from both Y and X.
Data: Daily market cap % changes (ΔMcap/Mcap) for USDT, USDC, DAI, and USDE from CoinGecko daily snapshots. The test is computed independently for each pair across both directions. Market cap % changes are equivalent to net minting/redemption flow signals for dollar-pegged stablecoins.
Pairs covered: USDT↔USDC (the two incumbent fiat-backed dollars), USDT↔DAI and USDC↔DAI (incumbent vs algorithmic / crypto-collateralized), USDT↔USDE (incumbent vs yield-bearing synthetic). Selected to span the major stablecoin design types.
Thresholds: F = 3.84 (p < 0.05 for F(1, ∞)) and F = 6.63 (p < 0.01 for F(1, ∞)). Exact p-values require numerical CDF evaluation; these thresholds are used as practical decision boundaries. For short windows (n < 50), treat results with caution, finite-sample critical values are slightly higher.
Important caveat: Granger causality is not classical causation. "USDT Granger-causes USDC" means USDT's past values help predict USDC's future values, not that Tether causes Circle to issue stablecoins. It reveals information spillovers and issuance timing dynamics.
Data source: CoinGecko API (daily snapshots). Update frequency: Daily at ~15:30 UTC. Coverage: Apr 2025 – present (449 snapshots). Computation runs entirely in the browser.