Statistics Seminar - Yifan Li (Manchester, Business School)
| Dates: | 2 December 2026 |
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| Times: | 14:00 - 15:00 |
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| What is it: | Seminar |
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| Organiser: | Department of Mathematics |
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| Who is it for: | University staff, External researchers, Current University students |
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Title: 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 Gaussian-based methods.
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