On uniform confidence intervals for the tail index and the extreme quantile

Yuya Sasaki and Yulong Wang

Journal of Econometrics, 2024, vol. 244, issue 1

Abstract: This paper presents two results concerning uniform confidence intervals for the tail index and the extreme quantile. First, we show that there exists a lower bound of the length for confidence intervals that satisfy the correct uniform coverage over a nonparametric family of tail distributions. Second, in light of the impossibility result, we construct honest confidence intervals that are uniformly valid by incorporating the worst-case bias in the nonparametric family. The proposed method is applied to simulated data and real data of financial time series.

Keywords: Honest confidence interval; Extreme quantile; Impossibility; Tail index; Uniform inference (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:244:y:2024:i:1:s0304407624002100

DOI: 10.1016/j.jeconom.2024.105865

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