Limit Theory for Online Expectile Estimation under Short and Long Range Dependence

arXiv:2610.16030v1 Announce Type: cross Abstract: Expectiles are tail-sensitive distributional summaries with important applications in financial risk measurement and forecasting, yet statistical theory for their online estimation from dependent data remains limited. We study a one-pass, stochastic-gradient algorithm with constant learning rate gamma for estimating the marginal expectile of a stationary causal process. The recursion admits a unique stationary causal solution and geometrically forgets its initialization. Under short-range dependence, its last iterate is asymptotically Gaussian at the sqrt{gamma} scale, with variance governed by the population curvature and the long-run variance of the expectile score. For long-memory linear processes the scale changes to gamma^{eta-1/2}ell(1/gamma), with a Gaussian limit arising from a fractional Brownian motion integral. We further derive the joint long-memory limit for multiple expectile levels and characterize its limiting covariance structure. Various numerical experiments corroborate the predicted Gaussian approximations and regime-specific fluctuation scales.

Source: arXiv cs.LG | 2026-10-14