Funding Rate Mean Reversion: Testing Ornstein-Uhlenbeck Dynamics in Perpetual Futures Markets

Abstract Carry strategies in perpetual futures depend critically on how fast funding rates revert to equilibrium — not whether they revert (the mechanism guarantees this), but the timescale and reliability of reversion. We fit Ornstein-Uhlenbeck (OU) models to 46,256 high-frequency funding rate observations from seven Hyperliquid perpetual futures (BTC, ETH, SOL, GRIFFAIN, BLUR, FARTCOIN, PURR) over a 7-day window (March 28 – April 4, 2026). These assets were selected to span the liquidity spectrum from blue chips to meme coins, enabling cross-asset comparison of mean reversion dynamics. What we can say with confidence: Augmented Dickey-Fuller tests reject the unit root null for all seven assets (six at Bonferroni-corrected α = 0.007), confirming that funding rates are stationary. OU half-life estimates range from 27 periods (GRIFFAIN) to 345 periods (BLUR), with meme coins reverting 3–10× faster than blue chips — consistent with the hypothesis that speculative demand is more volatile and faster-correcting than structural positioning. What we cannot say yet: The 7-day sample is too short for reliable OU parameter estimation on slow-reverting assets (BTC/ETH half-lives exceed the data window). The low R² values (0.2%–3.9%) confirm that the OU drift is a weak signal buried in noise. R/S Hurst estimates (H 0.84 for all assets) are likely inflated by well-documented short-sample bias in the rescaled range method and should not be interpreted as evidence of genuine dual-regime dynamics without confirmation from DFA or GPH estimators on longer datasets. We do not report Sharpe ratios because funding-income-only Sharpe figures are misleading for strategy evaluation — they exclude execution costs, price risk, liquidity constraints, and counterparty risk, and would overstate real-world performance by an order of magnitude. The value of this pilot: It establishes a methodological framework, provides preliminary parameter ranges, identifies the data requirements for robust estimation (minimum 3–6 months), and generates testable hypotheses for future work. It should be read as a foundation, not a conclusion. --- 1. Introduction Perpetual futures — the dominant derivative instrument in cryptocurrency markets — maintain price parity with spot through a funding rate mechanism. Every 8 hours (on most exchanges), long and short positions exchange payments based on the spread between perpetual and spot prices. When the perpetual trades at a premium (contango), longs pay shorts; when at a discount (backwardation), shorts pay longs. This mechanism creates a natural mean-reverting force: extreme funding rates attract arbitrage capital, which in turn compresses the rate back toward equilibrium. The question is not whether funding rates revert — the mechanism guarantees it — but how fast they revert, what drives the speed, and whether the dynamics are well-captured by a simple stochastic model like the Ornstein-Uhlenbeck (OU) process. 1.1 Research Hypotheses We test three hypotheses, ordered by the confidence our data can support: H1 (Strong support expected): Funding rates are stationary. Justification: The funding rate mechanism creates a mechanical restoring force. ADF rejection of the unit root is expected. H2 (Moderate support for fast-reverting assets, unreliable for slow): Mean reversion speed varies systematically across assets as a function of speculative vs. structural positioning. Specifically, we expect meme coins (driven by volatile retail positioning) to exhibit faster reversion than blue chips (driven by institutional hedging with structural demand). Justification: The OU half-life should reflect the characteristic timescale of positioning flows. H3 (Exploratory — data insufficient for reliable conclusions): Funding rates exhibit multi-timescale dynamics, with short-term trending (H 0.5) coexisting with long-term stationarity. Justification: If confirmed, this would have direct implications for carry strategy entry timing. However, R/S Hurst estimates are known to be upward-biased in short samples, and our 7-day window provides insufficient data to distinguish genuine multi-timescale behavior from estimation artifacts. 1.2 Why a 7-Day Pilot? Before investing in the infrastructure for a 6–12 month study, we run a 7-day pilot to: (a) validate the data pipeline, (b) establish preliminary parameter ranges, (c) identify methodological pitfalls, and (d) generate hypotheses for the full study. We are explicit throughout about which findings are robust and which require confirmation with extended data. 1.3 Asset Selection Rationale We select assets spanning the liquidity and market-cap spectrum: | Tier | Assets | Rationale | |------|--------|-----------| | Blue chips | BTC, ETH | Deep liquidity, institutional positioning, structural demand for long exposure | | Mid-cap | SOL, BLUR | Moderate liquidity, mixed retail/institutional positioning; BLUR included specifically because its token unlock dynamics create sustained sell