Publication Type
PhD Dissertation
Version
publishedVersion
Publication Date
5-2025
Abstract
This dissertation consists of two papers that contribute to the theory of estimation, inference, and prediction using high-frequency financial data. In the first chapter, time-varying betas from return regressions, commonly used to measure systematic financial market risk, are studied using high-frequency data. A novel econometric framework is introduced for the nonparametric estimation of these betas. The optimal finite-sample inference, in a well-defined sense, is enabled by the local Gaussian property of a generic continuous-time benchmark model under the fixed-k framework. Furthermore, a uniform functional inference theory for spot regressions is developed. The proposed methodology is broadly applicable in empirical settings, such as evaluating the performance of leveraged ETFs and analyzing intraday event studies. The second chapter focuses on the predictive theory with high-frequency data. It introduces a conformal predictive framework to assess the impact of intraday events, offering new insights into text-based analysis within the finance literature. Under a general continuous-time spot regression model within the fixed-k framework, the inference problem for cumulative abnormal returns (CAR) is recast as a counterfactual prediction problem for cumulative returns by constructing asymptotically valid conformal prediction intervals. Extending the theory to incorporate a counterfactual model with many control units, the proposed prediction intervals remain valid when using the synthetic control estimator. An intraday event study of AMD’s conference session illustrates the empirical application of the proposed methodology.
Degree Awarded
PhD in Economics
Discipline
Econometrics | Finance
Supervisor(s)
LI, Jia
First Page
1
Last Page
133
Publisher
Singapore Management University
City or Country
Singapore
Citation
REN, Yuexuan.
Essays on financial econometrics. (2025). 1-133.
Available at: https://ink.library.smu.edu.sg/etd_coll/769
Copyright Owner and License
Author
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.