Ziyang Xiong, Zhao Chen, Christina Dan Wang
arXiv 13 May 2025 · Statistics — Methodology
arXiv:2505.08654 · PDF · DOI · OpenAlex · Extracted main text
Estimating the leverage effect from high-frequency data is vital but challenged by complex, dependent microstructure noise, often exhibiting non-Gaussian higher-order moments. This paper introduces a novel multi-scale framework for efficient and robust leverage effect estimation under such flexible noise structures. We develop two new estimators, the Subsampling-and-Averaging Leverage Effect (SALE) and the Multi-Scale Leverage Effect (MSLE), which adapt subsampling and multi-scale approaches holistically using a unique shifted window technique. This design simplifies the multi-scale estimation procedure and enhances noise robustness without requiring the pre-averaging approach. We establish central limit theorems and stable convergence, with MSLE achieving convergence rates of an optimal $n^{-1/4}$ and a near-optimal $n^{-1/9}$ for the noise-free and noisy settings, respectively. A cornerstone of our framework's efficiency is a specifically designed MSLE weighting strategy that leverages covariance structures across scales. This significantly reduces asymptotic variance and, critically, yields substantially smaller finite-sample errors than existing methods under both noise-free and realistic noisy settings. Extensive simulations and empirical analyses confirm the superior efficiency, robustness, and practical advantages of our approach.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Aït-Sahalia, Yacine and Fan, Jianqing and Laeven, Roger J. A. and Wa… (2017) Estimation of the Continuous and Discontinuous Leverage Effects self | 0.888 | 10 | 7 | 70% |
| 2 | Kalnina, Ilze and Xiu, Dacheng (2017) Nonparametric Estimation of the Leverage Effect: A Trade-Off Between Robustness and Efficiency | 0.843 | 3 | 3 | 100% |
| 3 | Wang, Christina D. and Mykland, Per A (2014) The Estimation of Leverage Effect With High-Frequency Data self | 0.843 | 3 | 3 | 100% |
| 4 | Li, Z. Merrick and Linton, Oliver (2022) A ReMeDI for Microstructure Noise | 0.794 | 6 | 5 | 50% |
| 5 | Jacod, Jean and Li, Yingying and Zheng, Xinghua (2017) Statistical Properties of Microstructure Noise | 0.737 | 4 | 4 | 50% |
| 6 | Aït-Sahalia, Yacine and Mykland, Per A. and Zhang, Lan (2011) Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise | 0.644 | 2 | 2 | 100% |
| 7 | Yang, Xiye (2023) Estimation of Leverage Effect: Kernel Function and Efficiency | 0.644 | 2 | 2 | 100% |
| 8 | Aït-Sahalia, Yacine and Xiu, Dacheng (2019) A Hausman Test for the Presence of Market Microstructure Noise in High Frequency Data | 0.585 | 3 | 1 | 100% |
| 9 | Aït-Sahalia, Yacine and Jacod, Jean (2014) High-Frequency Financial Econometrics | 0.511 | 2 | 2 | 50% |
| 10 | Da, Rui and Xiu, Dacheng (2021) When Moving-Average Models Meet High-Frequency Data: Uniform Inference on Volatility | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 43 scored citations.