High-Frequency Estimation of the Leverage Effect under Rough Volatility with Microstructure Noise
Abstract
This paper develops feasible high-frequency inference for the leverage effect when the spot variance follows rough Volterra dynamics, leverage loadings depend on endogenous rough states, and observed prices may be contaminated by microstructure noise. We define an integrated leverage parameter through a scaled covariation between log prices and spot variance that accounts for their different local fluctuation scales. The scaled realized covariation admits a stable functional central limit theorem with an explicit pathwise centering correction arising from within-interval variation in the leverage loading. A loading regularity condition renders this correction asymptotically negligible, and we verify the condition for direct Volterra loadings and for smooth functions of closed rough states for every Hurst index below one half. We construct a block-based estimator with an exact finite-window correction for the leading proportional bias, together with a nonnegative single-scale estimator of its asymptotic variance. Pre-averaging extends feasible inference to observations with independent and identically distributed microstructure noise. Simulations under the rough Heston model demonstrate accurate finite-sample calibration with and without noise. An empirical application to fifteen years of one-second data on nine U.S. equities and ETFs finds negative median leverage estimates for every asset at weekly and monthly horizons.
Related
- Uncertainty and Explainability in Deep Rough Volatility: A Neural Information-Theoretic Posterior Approach
- From rough to multifractal multidimensional volatility: A multidimensional Log S-fBM model
- Model-based clustering of diffusion processes with constant volatility coefficients using high-frequency data
- A frequency localized maximum principle and its applications to time fractional equations with rough coefficients
- Nearly-minimax variance estimation under rough random design
BibTeX
@misc{chen2026highfrequency,
title = {{High-Frequency Estimation of the Leverage Effect under Rough Volatility with Microstructure Noise}},
author = {Zhao Chen and Ming Cheng and Christina Dan Wang and Ziyang Xiong},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.00965},
eprint = {2611.00965},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.00965},
}
RIS
TY - UNPB
TI - High-Frequency Estimation of the Leverage Effect under Rough Volatility with Microstructure Noise
AU - Zhao Chen
AU - Ming Cheng
AU - Christina Dan Wang
AU - Ziyang Xiong
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.00965
UR - https://arxiv.org/abs/2611.00965
AB - This paper develops feasible high-frequency inference for the leverage effect when the spot variance follows rough Volterra dynamics, leverage loadings depend on endogenous rough states, and observed prices may be contaminated by microstructure noise. We define an integrated leverage parameter through a scaled covariation between log prices and spot variance that accounts for their different local fluctuation scales. The scaled realized covariation admits a stable functional central limit theorem with an explicit pathwise centering correction arising from within-interval variation in the leverage loading. A loading regularity condition renders this correction asymptotically negligible, and we verify the condition for direct Volterra loadings and for smooth functions of closed rough states for every Hurst index below one half. We construct a block-based estimator with an exact finite-window correction for the leading proportional bias, together with a nonnegative single-scale estimator of its asymptotic variance. Pre-averaging extends feasible inference to observations with independent and identically distributed microstructure noise. Simulations under the rough Heston model demonstrate accurate finite-sample calibration with and without noise. An empirical application to fifteen years of one-second data on nine U.S. equities and ETFs finds negative median leverage estimates for every asset at weekly and monthly horizons.
ER -