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<span style="font-family: Calibri, Helvetica, sans-serif; font-size: 12pt;">Dear all,</span><br>
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<span style="font-size:12pt; font-family:Calibri,Helvetica,sans-serif">Hope everyone is having a lovely Monday!</span><br>
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<span style="font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; color: rgb(0, 0, 0);">At the S3RI seminar this Thursday (2 December) at 2 PM, we have Professor
</span><span style="font-family: Calibri, Helvetica, sans-serif;">Degui Li (University of York)</span><span style="font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; color: rgb(0, 0, 0);"> giving a talk on</span><br>
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<span style="margin: 0px; font-family: Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">"</span><span style="margin: 0px; font-family: Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">Detection of Multiple Structural
Breaks in Large Covariance Matrices.</span><span style="margin: 0px; font-family: Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">"</span></div>
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<span style="font-family: Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">P</span><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">lease see below for the details of the talk and how to
join the seminar online.</span></div>
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<span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; background-color: white; color: rgb(0, 0, 0);">Have a nice week, and hope to see you at the seminar on Thursday!</span><span style="margin: 0px; font-size: 12pt; font-family: "Times New Roman", serif; color: black;"></span></p>
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<span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; background-color: white; color: rgb(0, 0, 0);">Kind regards,</span><span style="margin: 0px; font-size: 12pt; font-family: "Times New Roman", serif; color: black;"></span></p>
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<span style="margin: 0px; font-size: 11.5pt; color: rgb(32, 31, 30); background-color: white;"><br>
</span><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; background-color: white; color: rgb(0, 0, 0);">Jessie</span><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; background-color: white; color: rgb(0, 0, 0);"><br>
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<span style="font-size:12pt; font-family:Calibri,Helvetica,sans-serif"><b><i></i></b></span>
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<div style="font-size:14px"><em><strong><span style="font-size:12pt; font-family:Calibri,Helvetica,sans-serif">Detection of Multiple Structural Breaks in Large Covariance Matrices</span></strong></em></div>
<div style=""><span style="font-size:12pt; font-family:Calibri,Helvetica,sans-serif">Degui Li (University of York)</span><span style="font-size:15px"><br>
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</span><span style="font-size:12pt; font-family:Calibri,Helvetica,sans-serif">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).</span></div>
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