Prof LI, Wai Keung    李偉強 教授
Research Chair Professor
Department of Mathematics and Information Technology
Dean
Faculty of Liberal Arts and Social Sciences
Contact
ORCiD
0000-0001-9149-9289
Phone
(852) 2948 7093
Fax
(852) 2948 7127
Email
waikeungli@eduhk.hk
Address
10 Lo Ping Road, Tai Po, New Territories, Hong Kong
Website
https://pappl.eduhk.hk/rich/web/person.xhtml?pid=245973&name=LI, Wai Keung
Scopus ID
14015971200
Research Interests

Time Series Analysis;

Financial Econometrics;

Financial Risk Management; 

Big Data Analytics; 

Environmetrics;

Stochastic Processes with Applications to Hydrology and Climatology;

Extreme Value Theory;

Spatial Statistics;

Sampling Methods

Teaching Interests

Time Series Analysis;

Financial Econometrics;

Financial Risk Management;

Statistical Inference;

Statisitcal Learning;

Elementary Statistics;

General Education on Statistical Thinking. 

Personal Profile

B.Sc. (First class with Distinction) in Mathematics,

York University, Canada (1975)

M.A. in Mathematics, York University, Canada (1976)

Ph.D. in Statistics, University of Western Ontario (1981)

Elected Member, International Statistical Institute (1991)

Elected Fellow, American Statistical Association (2003)

Elected Fellow, Institute of Mathematical Statistics (2006)

Honorary Member, Hong Kong Statistical Society, conferred March, 2009

Outstanding Service Award, International Chinese Statistical Association,  conferred August, 2009

Emeritus Professor, Univeristy of Hong Kong, conferred May, 2020.

Distinguished Author, Journal of Time Series Analysis, conferred June, 2020.

Research Interests

Time Series Analysis;

Financial Econometrics;

Financial Risk Management; 

Big Data Analytics; 

Environmetrics;

Stochastic Processes with Applications to Hydrology and Climatology;

Extreme Value Theory;

Spatial Statistics;

Sampling Methods

Teaching Interests

Time Series Analysis;

Financial Econometrics;

Financial Risk Management;

Statistical Inference;

Statisitcal Learning;

Elementary Statistics;

General Education on Statistical Thinking. 

Research Outputs
Journal Publications
Publication in refereed journal
F. JIANG, D. LI, W.K. LI and K. ZHU (2023). Testing and Modelling for the Structural Change in Covariance Matrix Time Series with Multiplicative Form. Statistica Sinica, 33, 787 - 818. https://doi.org/10.5705/ss.202021.0029
Y. ZHENG, J. WU, W. K. LI and G. LI (2023). Least Absolute Deviations Estimation for Nonstationary Vector Autoregressive Time Series Models with Pure Unit Root.. Statistics and Its Interface, 16, 199 - 216. https://dx.doi.org/10.4310/21-SII721
ZHOU, J., JIANG, F., ZHU, K., and LI, W. K. (2022). Time Series Models for Realized Covariance Matrices Based on the Matrix-F Distribution. Statistica Sinica, 32, 755 - 768. https://doi.org/10.5705/ss.202019.0424
WANG, G., ZHU, K. LI, G. and LI, Wai Keung (2022). Hybrid Quantile Estimation for Asymmetric Power GARCH Models. Journal of Econometrics, 227, 264-284.
WONG, T.S.T. and LI, Wai Keung (2021). A new test for tail index with application to Danish fire loss data. Journal of Statistical Computation and Simulation, 91, 3880 - 3893. https://doi.org/10.1080/00949655.2021.1954647
K.K.F. LAW, W.K. LI and Philip L.H. YU (2021). An Alternative Nonparametric Tail Risk Measure. Quantitative Finance, 21(4), 685-696.
G. WANG, W.K. LI and K. ZHU (2021). New HSIC-Based Tests for Independence between Two Stationary Multivariate Time Series. Statistica Sinica, 31, 269-300.
J. XU, W.K. LI and Z. YING (2020). Variable Screening for Survival Data in the Presence of Heterogeneous Censoring. Scandinavian Journal of Statistics, 47(4), 1171-1191.
K. SHEN, J.F. YAO, W.K. LI (2020). Forecasting High-Dimensional Realized Volatility Matrices Using a Factor Model. Quantitative Finance, 20, 1879-1887.
Seto, W. K. W., Chiu, W. H. K., Yu, P. L. H., Cao, W., Cheng, H. M., Wong, E. M. F., Wu, J., Lui, G. C. S., Shen, X., Mak, L. Y., Li, W. K. and Yuen, R. M. F. (2020). An end-to-end artificial intelligence model accurately diagnosing hepatocellular carcinoma on computed tomography. United European Gastroenterology Journal, 8(8 suppl), 48-49.
Seto, W., Chiu, K., Yu, P. L. H., Cao, W., Cheng, H. M., Lui, G., Wong, E. M. F., Wu, J., Mak, L. Y., Shen, X. P., Li, W. K. and Yuen, M. F. (2020). High diagnostic performance of a deep learning artificial intelligence model in accurately diagnosing hepatocellular carcinoma on computed tomography. Hepatology, 72 (1 Suppl), 84-85.
K.K.F. LAW, W.K. LI and Philip L.H. YU (2020). Evaluation Methods for Portfolio Management. Applied Stochastic Models in Business and Industry, 36(5), 857-876.
XIA, Qiang, ZHANG, Zhiqiang & LI, Wai Keung (2020). A Portmanteau Test for Smooth Transition Autoregressive Models. Journal of Time Series Analysis, 41, 722-730.
Sini GUO, Wai-Ki CHING, Wai-Keung LI, Tak-Kuen SIU, Zhiwen ZHANG (2020). Fuzzy Hidden Markov-Switching Portfolio Selection with Capital Gain Tax. Expert Systems With Applications, 149, 113304.
K. LAW, W.K. LI and P. YU (2020). An Empirical Evaluation of Large Dynamic Covariance Models in Portfolio Value-at-Risk Estimation. Journal of Risk Model Validation, 14(2), 21-39.
Li, D., Zeng, M.R., Li, W.K., & Li G. (2020). Conditional Quantile Estimation for Hysteretic Autoregressive Models. Statistica Sinica, 30, 809-827.
WANG, D., & LI, W.K. (2020). Unit Root Testing on Buffered Autoregressive Model. Statistica Sinica, 30, 977-1003.
CUI, Y., ZHU, F.K., & LI, W.K, (2020). Modeling of RCOV matrices with a generalized threshold conditional Wishart autoregressive model. Statistics and Its Interface, 13, 77-89.
ZHANG, Z.Y., & LI, W.K. (2019). An experiment on autoregressive and threshold autoregressive models with non-Gaussian error with application to realized volatility. Economies, 7(2), 1-11.
Publication in policy or professional journal
Philip L. H. YU and Wai Keung LI (2021). Project-based Learning via Competition for Data Science Students. Harvard Data Science Review, 3(1), 1-4.

Conference Papers
Refereed conference paper
Seto, W.K.W., Chiu, K.H.K., Cao, W., Lui, G., Zhou, J. Cheng, H.M., Wu, J., Shen, X., Mak, L.Y., Huang, J., Li, W.K. and Yuen, R.M.F. & Yu, P.L.H. (2022, June). Training, validation and testing of a multiscale three-dimensional deep learning algorithm in accurately diagnosing hepatocellular carcinoma on computed tomography. Journal of Hepatology, UK.

Projects
Moving Average for Buffered Time Series Modelling
The buffered time series model is a new type of nonlinear time series models that have attracted some attention in the literature. However, nearly all buffered time series models are of the autoregressive type. The objective of this project is to extend the buffered time series to include the moving average specification.
One paper has been submitted to a peer reviewed international journal for consideration for possible publication.
Project Start Year: 2021, Principal Investigator(s): LI, Wai Keung

 
Early Childhood Education STEM and Maker Education
This pilot project aims at promoting age-appropriate and authentic STEM learning experiences in early childhood settings through co-developing 24 sets of STEM teaching packages with early childhood teachers. This project aim at building up children’s STEM awareness, knowledge and skills through STEM-rich maker learning experience. 12 pilot project schools will be invited to join this project, including kindergartens and child care centres (both half-day & whole-day service). Children in the project schools are able to learn science and mathematics through the application of technology and engineering in a realistic and integrated learning experience. This project will align with the core education concepts of ECE including observation, awareness and motivation in designing STEM activities. Additionally, fundamental concepts of physical science, life science and earth science will be included in the content of teaching packages.Early childhood professionals have an essential role in supporting young children’s motivation and engagement in STEM education. The co-development process with early childhood teachers provides practical professional development opportunities to enhance knowledge and skills of designing quality STEM learning activities. Additionally, teachers’ needs, difficulties and challenges in applying STEM teaching packages will be examined. Teachers will participate in pre and post-project interviews before and after the training and implementation stage to reflect on their pedagogical needs, school-level conditions, challenges in implementing STEM activities at schools.
Project Start Year: 2020, Principal Investigator(s): LEE, Chi Kin, John (LI, Wai Keung as Collaborator)

 
Promoting AI Literacy and Effective Use of AI in Education
The objectives of this project are twofold: firstly to prepare students with AI literacy and secondly to explore and develop AI tools for teaching and learning in a range of academic disciplines. The project will offer a good opportunity not only to develop the AI literacy of students, but also to promote effective use of AI in education as well as to prepare students to adopt AI-powered technology in their future teaching careers.
Project Start Year: 2020, Principal Investigator(s): LI, Wai Keung 李偉強

 
New Tools and Models for Time Series Analysis with Potential Applications to Financial and Environmental Data
This project will explore and construct new time series models/tools for the analysis of financial/environmental time series. As is well documented, such time series are often nonlinear, heteroscedastic and perhaps only locally stationary. This means that their analysis are mathematically challenging. An objective is to provide some further investigations on matrix time series which the PI has developed some years ago. A second objective is to explore new time series models or new methodologies for statistical inference for various types of time series in finance and the environment.
Project Start Year: 2020, Principal Investigator(s): LI, Wai Keung

 
Statistical Inferences for Possibly Nonstationary and Nonlinear Time Series Models with a Hysteretic (Buffered) Structure
This project aims to develop statistical inferences for nonlinear time series data with a hysteretic (buffered) non-linearlity. A major objective is to derive unit root non-stationarity tests for such time series. Conversely tests for buffered non-linearity are also developed under the assumption of the presence of unit root. Extension of classical time series inferential procedures such as quantile estimation, smooth transitions in and out of the buffered region for this type of time series will also be considered.
This project was originally under HKU but was transferred to this university last September.
Project Start Year: 2017, Principal Investigator(s): LI, Wai Keung 李偉強