Tensor Dynamic Conditional Correlation Model with Applications to Portfolio
Prof. ZHU Ke
Associate Professor, School of Computing and Data Science (Statistics and Actuarial Science), Faculty of Science, HKU
Date: 17 December 2025 (Wednesday)
Time: 15:30-17:00
Venue: E22-G015
Host: Prof. Lianjie SHU, Professor in Business Intelligence and Analytics
Abstract
Style investing creates asset classes (or the so-called “styles”) with low correlations, aligning well with the principle of “Holy Grail of investing” in terms of portfolio selection. The returns of styles naturally form a tensor-valued time series, which requires new tools for studying the dynamics of the conditional correlation matrix to facilitate the aforementioned principle. Towards this goal, we introduce a new tensor dynamic conditional correlation (TDCC) model, which is based on two novel treatments: trace-normalization and dimension-normalization. These two normalizations adapt to the tensor nature of the data, and they are necessary except when the tensor data reduce to vector data. Moreover, we provide an easy-to-implement estimation procedure for the TDCC model, and examine its finite sample performance by simulations. Finally, we assess the usefulness of the TDCC model in international portfolio selection across ten global markets and in large portfolio selection for 1800 stocks from the Chinese stock market.
Speaker
Prof. ZHU Ke is an Associate Professor in the School of Computing and Data Science (Statistics and Actuarial Science), Faculty of Science, HKU. His teaching areas include Probability and Statistics, Stochastic Process, and Risk Management; and his research interests focuses on Time Series Analysis; Econometrics; Causal Inference and etc. He was awarded as Fellow of International Statistical Institute and Fellow for Journal of Econometrics since 2023, and Associate Editor for Journal of Business & Economic Statistics since 2025. Prof. Zhu’s papers are published in prestigious journal including “Journal of Empirical Finance”, “Journal of Business & Economic Statistics”, “Journal of Agricultural, Biological, and Environmental Statistics” etc.
All are welcome!
