(1) Chang Jinyuan*, Hu Qiao, Liu Cheng, and Tang Cheng Yong (2024). Optimal covariance matrix estimation for high-dimensional noise in high-frequency data. Journal of Econometrics，239(2): 105239, 1-39. DOI: https://doi.org/10.1016/j.jeconom.2022.06.010.
(2) Jiang Binyan, Liu Cheng*, and Tang Cheng Yong (2023). Dynamic covariance matrix estimation and portfolio analysis with high-frequency data. Journal of Financial Econometrics, Online Published; DOI: https://doi.org/10.1093/jjfinec/nbad003.
(3) Kong Xin-Bing, Lin Jin-Guan, Liu Cheng* and Liu Guang-Ying (2023). Discrepancy between global and local principal component analysis on large-panel high-frequency data. Journal of the American Statistical Association, 118(542): 1333-1344.
DOI: https://doi.org/10.1080/01621459.2021.1996376.
(4) Liu Cheng, Wang Moming, and Xia Ningning (2022). Design-free estimation of integrated covariance matrices for high-frequency data. Journal of Multivariate Analysis, 189: 1-14.
DOI: https://doi.org/10.1016/j.jmva.2021.104910.
(5) Liu Cheng and Sun, Yixiao (2019). A Simple and Trustworthy Asymptotic t Test in Difference-in-Differences Regressions. Journal of Econometrics, 210: 327-362.
(6) Kong Xin-Bing and Liu Cheng* (2018). Testing against constant factor loading matrix with large panel high-frequency data, Journal of Econometrics, 2018, 204: 301-319.
(7) Liu Cheng and Tang Cheng Yong (2014). A quasi-maximum likelihood approach for integrated covariance matrix estimation with high frequency data, Journal of Econometrics, 180: 217-232.
(8) Liu Cheng and Tang Cheng Yong (2013). A state space model approach to integrated covariance matrix estimation with high frequency data, Statistics and Its Interface (SCI), 6: 463-475.
(9) 刘成，罗金斗，罗知 （2022）. 高频数据下积分波动率矩阵的伪似然估计、预测及应用，《数量经济技术经济研究》，第3期。