Memory, persistence, and the price process in interest rate futures contracts: A statistical investigation at the mesoscale using kernel two-sample testing

We test whether long memory in interest rate futures reflects intrinsic dynamics or calendar-time artifact. Using tick-level data from 11 futures contracts, we compare multifractal scaling properties under calendar-time and information clocks. Kernel two-sample tests on 37-dimensional MFDFA feature vectors assess whether these distributions differ across clock types. Information clocks reduce volatility clustering by 90.5% on average, independent of liquidity ( R 2 = 0.02). The shift is structural: marginal returns are indistinguishable across clocks, but joint distributions with lagged volatility differ significantly (96% vs. 2.3% detection rate). Forecast gains concentrate in illiquid, irregularly traded contracts; liquid benchmarks show no improvement. Most long memory documented in interest rate futures is a sampling artifact, not an intrinsic property. Calendar-time volatility models require re-specification, not recalibration: the dependence structure changes, not only parameter scale.