[Turing-Southampton] S3RI seminar: Degui Li (University of York)

[Chieh-Hsi Wu]

Dear all,

Hope everyone is having a good week!

At today's S3RI seminar at 2 PM, Professor Degui Li (University of York) will present his work on

"Detection of Multiple Structural Breaks in Large Covariance Matrices."

Please see below for the details of the talk and how to join the seminar online.



Hope to see you at the seminar later today!



Kind regards,

Jessie



Detection of Multiple Structural Breaks in Large Covariance Matrices
Degui Li (University of York)

This paper studies multiple structural breaks in large contemporaneous covariance matrices of high-dimensional time series satisfying an approximate factor model. The breaks in the second-order moment structure of the common components are due to sudden changes in either factor loadings or covariance of latent factors, requiring appropriate transformation of the factor models to facilitate estimation of the (transformed) common factors and factor loadings via the classical principal component analysis. With the estimated factors and idiosyncratic errors, an easy-to-implement CUSUM-based detection technique is introduced to consistently estimate the location and number of breaks and correctly identify whether they originate in the common or idiosyncratic error components. The algorithms of Wild Binary Segmentation for Covariance (WBS-Cov) and Wild Sparsified Binary Segmentation for Covariance (WSBS-Cov) are used to estimate breaks in the common and idiosyncratic error components, respectively. The asymptotic properties of the proposed methodology are derived with near-optimal rates (up to a logarithmic factor) achieved for the estimated breaks. Monte-Carlo simulation studies are conducted to examine the finite-sample performance of the developed method. We finally apply our method to detect breaks in the contemporaneous covariance structure of daily returns of S&P 500 constituents. An R package is provided to implement the proposed algorithms. This is a joint work with Yuning Li (York) and Piotr Fryzlewicz (LSE).



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