Friendly Reminder: FSS Computational Social Sciences Workshop: “Testing and Support Recovery of Correlation Structures for Matrix-Valued Observations with an Application to Stock Market Data” by Prof. Haipeng SHEN (28 March, 12:30, E21B-G016)

提提您：計算社會科學工作坊 - 講者：沈海鵬教授 (3月28日, 12:30, E21B-G016)

The Faculty of Social Sciences (FSS) is holding a series of Computational Social Sciences Workshop. We have the pleasure to invite Prof. Haipeng SHEN to deliver the fourth seminar on “Testing and Support Recovery of Correlation Structures for Matrix-Valued Observations with an Application to Stock Market Data”. Details of the Workshop are as follows:

Date: 28 Mar 2023 (Tues)
Time: 12:30 – 14:00
Venue: E21B-G016
Language: English

Please register by 27/03/2023 (Monday) 1pm:

https://forms.gle/HdGUmzfpniqdQTor6

Speaker: Prof. Haipeng SHEN obtained his PhD in Statistics from University of Pennsylvania in 2003. His research focuses on Data-driven decision making in the face of uncertainty: big data, business analytics, healthcare analytics and service engineering. He joined HKU in 2015 as a Professor of Innovation and Information Management. Before joining HKU, he was a Professor of Statistics and Operations Research, University of North Carolina at Chapel Hill, USA.

Abstract: Estimation of the covariance matrix of asset returns is crucial to portfolio construction. As suggested by economic theories, the correlation structure among assets differs between emerging markets and developed countries. It is therefore imperative to make rigorous statistical inference on correlation matrix equality between the two groups of countries. However, if the traditional vector-valued approach is undertaken, such inference is either infeasible due to limited number of countries comparing to the relatively abundant assets, or invalid due to the violations of temporal independence assumption. This highlights the necessity of treating the observations as matrix-valued rather than vector-valued. With matrix-valued observations, our problem of interest can be formulated as statistical inference on covariance structures under sub-Gaussian distributions, i.e., testing non-correlation and correlation equality, as well as the corresponding support estimations. We develop procedures that are asymptotically optimal under some regularity conditions. Simulation results demonstrate the computational and statistical advantages of our procedures over certain existing state-of-the-art methods for both normal and non-normal distributions. Application of our procedures to stock market data reveals interesting patterns and validates several economic propositions via rigorous statistical testing.