Li Qiyuan

WORKING PAPERS

"Uniform Inference for High-Frequency Data",  (Job Market Paper)

We address the uniform inference problem for high-frequency data that includes prices, volumes, and trading ows. Such data is modeled with a general state-space framework, where latent state process is the corresponding risk indicators, e.g., volatility, price jump, average order size, and arrival of events. The functional estimators are formed as the collection of localized estimates across different time points. Although the proposed estimators do not admit a functional central limit theorem, a Gaussian strong approximation, or coupling, is established under in-fill asymptotics to facilitate feasible inference. We apply the proposed methodology to distinguish the informative part from the Federal Open Market Committee speeches, and to analyze the impact of social media activities on cryptocurrency markets.

RESEARCH PAPERS

“Optimal Nonparametric Range-Based Volatility Estimation”, with Tim Bollerslev and Jia Li, accepted in Journal of Econometrics.

We present a general framework for optimal nonparametric spot volatility estimation based on intraday range data, comprised of the first, highest, lowest, and last price over a given time interval. We rely on a decision-theoretic approach together with a coupling-type argument to directly tailor the form of the nonparametric estimator to the specific volatility measure of interest and relevant loss function. The resulting new optimal estimators offer substantial efficiency gains compared to existing commonly used range-based procedures.

PUBLICATIONS

“Permutation-based Tests for Discontinuities in Event Studies”, with Federico Bugni and Jia Li, Quantitative Economics, 14(1), 2023, 37-70.

“Seemingly Unrelated Regression Estimation for VAR Models with Explosive Roots”, with Ye Chen and Jian Li, Oxford Bulletin of Economics and Statistics, 85(1), 2023, 910-937.