• 演讲人： 何勇（山东大学金融研究院研究员）
• 时间：2022年09月30日 周五上午10:00
• 地点：腾讯会议：797-591-599

摘要：In this article, we first propose generalized row/column matrix Kendall's tau  for matrix-variate observations that are ubiquitous in areas such as finance and medical imaging. For a random matrix following a matrix-variate elliptically contoured distribution,  we show that the eigenspaces of the proposed row/column matrix Kendall's tau coincide with those of the row/column scatter matrix respectively, with the same descending order of the eigenvalues. We perform eigenvalue decomposition to the generalized row/column matrix Kendall's tau for recovering the loading spaces of the matrix factor model. We also propose to estimate the pair of the factor numbers by exploiting the eigenvalue-ratios of the  row/column matrix Kendall's tau. Theoretically, we derive the convergence rates of the estimators for loading spaces, factor scores and common components, and prove the consistency of the estimators for the factor numbers without any moment constraints on the idiosyncratic errors. Thorough simulation studies are conducted to show  the higher degree of robustness of the proposed estimators over the existing ones. Analysis of a financial dataset of asset returns and a medical imaging dataset associated with COVID-19 illustrate the empirical usefulness of the proposed method. This is a joint work with Yalin Wang, Long Yu, Wang Zhou and Wen-Xin Zhou.

联系人：高照省（zhaoxing_gao@zju.edu.cn）