Next seminar: 

4 February 2026 - Yi He (Eastern Institute of Technology, Ningbo)

(09:00 UTC) = (04:00 New York) = (10:00 Paris) = (17:00 Shanghai) = (20:00 Sydney)

Fixed-k Inference for Explosive Drift

Abstract: We propose a new framework for uniform inference on explosive drifts in high-frequency data. Standard large-bandwidth asymptotics often fail in this context because the spot test statistics computed over short windows are far from Gaussian. By treating the window size k as fixed, we show that the spot statistics are coupled with a sequence of dependent Student-t variables, and their maximum converges to a Frechet distribution rather than the conventional Gumbel limit. We establish a novel anti-clustering condition for dependent Student-t processes to justify this limit theory under overlapping estimation windows. A local power analysis reveals that explosive drifts induce a multiplicative power transformation of the limiting distribution, contrasting with the additive location shift characteristic of Gaussian theory. Empirically, we show that the proposed coupling-based test offers superior size control and reveals that statistically significant intraday price explosions are far rarer than suggested by conventional Gaussian-based methods.

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Log in link:  https://umich.zoom.us/j/92851213572

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