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'*': Corresponding author; '^': Alphabetical order; '#': Student
'*': Corresponding author; '^': Alphabetical order; '#': Student
Working Papers
Working Papers
When asynchronicity meets price staleness: Robust estimation of high-frequency covariance, with Wenhao Cui. (Submitted to Journal of Econometrics)
Estimation of volatility functionals with time-varying price staleness, with Qiang Liu, Zhi Liu. (R&R at Econometric Theory)
Bias-corrected realized covariation in the presence of price staleness, with Zhi Liu.
Statistical inference of multivariate price staleness, with Wenjing Liu, Zhi Liu. (forthcoming at Statistics and Its Interface)
Library size-stabilized metacells construction enhances co-expression network analysis in single-cell data, with Tianjiao Zhang. (2nd R&R at Plos Computational Biology)
Penalized latent block model for functional data co-clustering, with #Shi Chen, Yiming Liu, Guangren Yang. (Submitted to Journal of Multivariate Analysis)
Intraday volume forecasting with periodicity, with #Dahao Tan, Chao Zhang.
A nonparametric test for time-varying systematic staleness with high-frequency data, with Wenjing Liu, #Yuexi Zhao.
The leverage effect puzzle revisited: Zeros, with Yu Jiang, Zhi Liu, Mathias Vetter.
Zhu, H., & Liu, Z. (2024). On bivariate time-varying price staleness. Journal of Business & Economic Statistics, 42(1), 229-242.
Abstract: Price staleness refers to the extent of zero returns in price dynamics. Bandi et al. (2020) introduce two types of staleness: systematic and idiosyncratic staleness. In this study, we allow price staleness to be time-varying and study the statistical inference for idiosyncratic and common price staleness between two assets. We propose consistent estimators for both time-varying idiosyncratic and systematic price staleness and derive their asymptotic theory. Moreover, we develop a feasible nonparametric test for the simultaneous constancy of idiosyncratic and common price staleness. Our inference is based on infill asymptotics. Finally, we conduct simulation studies under various scenarios to assess the finite sample performance of the proposed approaches and provide an empirical application of the proposed theory.
Zhu, H., Bai, L., He, L., & Liu, Z. (2023). Forecasting realized volatility with machine learning: Panel data perspective. Journal of Empirical Finance, 73, 251-271.
Abstract: Machine learning approaches have become very popular in many fields in this big data age. This paper considers the problem of forecasting realized volatility with machine learning using high-frequency data. Instead of treating the realized volatility as a univariate time series studied by many existing works in the literature, we employ panel data analysis to improve forecasting accuracy in the short term. We use six effective machine-learning methods for the realized volatility panel data. We compare our results with the traditional linear-type models under the same panel data framework and with the single time series forecasting via the same machine learning methods. The results show that the panel-data-based machine learning method (PDML) outperforms the other methods.
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