Publication Type

PhD Dissertation

Version

publishedVersion

Publication Date

5-2026

Abstract

This dissertation studies econometric inference for high-frequency financial data, with a focus on detecting nonstandard drift and volatility dynamics in continuous-time models.

The first chapter proposes a new framework for uniform inference on explosive drift in high-frequency data, where conventional Gaussian approximations can fail due to the non-Gaussian behavior of short-window spot statistics. Under fixed-window asymptotics, these statistics are coupled with dependent t variables, and their maximum converges to a Fréchet distribution. We establish an anti-clustering condition for dependent t-statistics under overlapping windows and develop a feasible coupling-based test. Simulation results demonstrate better size control, and the empirical findings suggest that significant intraday price explosions are less frequent than implied by Gaussian-based methods.


The second chapter develops a local likelihood-based framework for detecting volatility changes. By exploiting a local Gaussian approximation within shrinking windows, the testing problem is asymptotically represented by a Gaussian limit experiment. This enables the construction of likelihood ratio tests with optimality properties. An omnibus test is also proposed to provide a robust alternative without specifying a variance structure. Theoretical results establish asymptotic size control, while simulations and empirical analysis demonstrate strong finite-sample performance.

Keywords

High-frequency econometrics, Explosive drift, Volatility change detection

Degree Awarded

PhD in Economics

Discipline

Econometrics

Supervisor(s)

LI, Jia

First Page

1

Last Page

125

Publisher

Singapore Management University

City or Country

Singapore

Copyright Owner and License

Author

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Included in

Econometrics Commons

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