Basic Information:
Xinbing Kong is a Professor and Doctoral Supervisor at the School of Statistics and Data Science, Southeast University. He graduated from Lanzhou University in 2007 with both Bachelor's and Master's degrees, and earned his Ph.D. from Hong Kong University of Science and Technology in 2011. He has published more than 20 papers in prestigious journals including Annals of Statistics, Journal of the American Statistical Association, Journal of Econometrics, Biometrika, and Journal of Business and Economic Statistics.
Contact Info:
Email：xinbingkong@126.com
Research Interests
Main research interests include high-frequency and high-dimensional data statistics, econometric and financial econometrics, and machine learning.
Representative Publications
1.Yong He, Xinbing Kong, Long Yu, Lorenzo Trapani. Online change-point detection for matrix-valued time series with latent two-way factor structure, Annals of Statistics, 2024, Published Online.
2.Yong He, Xinbing Kong, Long Yu, Xinsheng Zhang and Changwei Zhao. Matrix factor anlysis: from least squares to iterative projection, Journal of Business and Economic Statistics, 2024,42,322-334.
3.Xin-Bing Kong, Jin-Guan Lin, Cheng Liu, Guang-Ying Liu. Discrepancy between global and local principal component analysis on large-panel high-frequency data, Journal of the American Statistical Association, 2023, 118,1333-1344.
4.Yong He, Xinbing Kong, Lorenzo Trapani, Long Yu. One-way or two-way factor model for matrix sequences? Journal of Econometrics, 2023, 235,1981-2004.
5.Yong He, Xinbing Kong, Long Yu, Xinsheng Zhang. Large-dimensional factor analysis without moment constraints, Journal of Business and Economic Statistics, 2022, 40, 302-312.
6.Long Yu, Yong He, Xinbing Kong*, Xinsheng Zhang. Projected estimation for large-dimensional matrix factor model, Journal of Econometrics, 2022, 229, 201-217.
7.Xinbing Kong. A random-perturbation-based rank estimator of the number of factors, Biometrika, 2020, 107, 505-511.
8.Xinbing Kong, Jiangyan Wang, Jinbao Xing, Chao Xu and Chao Ying. Factor and idiosyncratic empirical processes,Journal of the American Statistical Association, 2019, 114, 1138-1146.
9.Xin-Bing Kong, Zhi Liu, Wang Zhou. A rank test of the number of factors with high-frequency data, Journal of Econometrics, 2019, 211, 439-460.
10.Xin-Bing Kong and Cheng Liu. Testing against constant factor loading matrix with large panel high-frequency data, Journal of Econometrics, 2018, 204, 301-319.
11.Xin-Bing Kong. On the systematic and idiosyncratic volatility with large panel high-frequency data, Annals of Statistics, 2018, 46, 1077-1108.
12.Zhi Liu, Xin-Bing Kong, Bing-Yi Jing. Estimating the integrated volatility using high-frequency data with zero durations，Journal of Econometrics, 2018，204, 18-32.
13.Donggyu Kim, Xinbing Kong, Cuixia Li, Yazhen Wang. Adaptive thresholding estimator for large volatility matrix estimation based on high-frequency financial data, Journal of Econometrics, 2018, 203, 69-79.
14.Xin-Bing Kong, Shao-Jun Xu, Wang Zhou. Bootstrapping volatility functionals: a local and nonparametric perspective, Biometrika, 2018, 105, 463-469.
15.Xin-Bing Kong. On the number of common factors with high-frequency data, Biometrika, 2017, 104, 397-410.
16.Xin-Bing Kong, Zhi Liu, Bing-Yi Jing. Testing for pure-jump processes for high-frequency data, Annals of Statistics, 2015, 43, 847-877.
17.Bing-Yi Jing, Zhi Liu, Xin-Bing Kong. On the estimation of integrated volatility with jumps and microstructure noise, Journal of Business and Ecoomic Statistics, 2014, 32, 457-467.
18.Bing-Yi Jing, Xin-Bing Kong, Zhi Liu. Modeling high frequency data by pure-jump processes? Annals of Statistics, 2012, 40, 759-784.
19.Bing-Yi Jing, Xin-Bing Kong, Zhi Liu, Per A Mykland. On the jump activity index for semimartingales, Journal of Econometrics, 2012, 166, 213-223.
20.Bing-Yi Jing, Xin-Bing Kong, Zhi Liu. Estimating the jump activity index under noisy observations using high frequency data, Journal of the American Statistical Association, 2011, 106, 558-568.
