Professor Wang Guoqiang from the School of Mathematics, Physics and Statistics has guided his postgraduate students to achieve the latest research results in the field of statistical inference for high-dimensional data
School of Mathematics, Physics and Statistics; Jing Zhou
Recently, Prof. Wang Guoqiang from the School of Mathematics, Physics and Statistics guided Sun Zhangshuang, a graduate student of the class of 2022,to publishin Finance Research Letters, a Top journal in the field of finance (SCI journal Zone 2 of the Chinese Academy of Scienceswith an impact factor of 7.4) entitled "Enhancing High-Dimensional Dynamic Conditional Angular Correlation Model Based on GARCH Family," were published in the journal Models: Comparative Performance Analysis for Portfolio Optimization”.
The paper focuses on the precise estimation of high-dimensional covariance matrices, combining the nonlinear information capture ability of GARCH family models with the excellent characteristics of DCAC models in peak cases, and proposes an enhanced dynamic conditional angular correlation model based on GARCH family models. By capturing nonlinear information such as long-term memory features, the model has achieved a breakthrough in portfolio optimization, not only providing a more accurate and information-rich method for estimating high-dimensional covariance matrices, but also significantly enhancing the returns of portfolio strategies while reducing portfolio risks. This research provides a fresh perspective on risk measurement for portfolio optimization and has significant theoretical value and practical guiding significance.
