BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Jia Chen (University of York) DTSTART:20240514T130000Z DTEND:20240514T135000Z DTSTAMP:20260423T021828Z UID:UEA_mth/8 DESCRIPTION:Title: Estimation of Large Dynamic Precision Matrices with a Latent Semiparametri c Structure\nby Jia Chen (University of York) as part of Fluids and St ructures Seminar @ UEA\n\nLecture held in SCI 1.50.\n\nAbstract\nThis pape r studies the estimation of dynamic precision matrices of high-dimensional time series satisfying an approximate factor model with multiple conditio ning variables. We introduce an easy-to-implement semiparametric method to estimate each entry of the conditional covariance matrices of the common factors and the idiosyncratic components via Model Averaging MArginal Regr ession (MAMAR). We apply the CLIME method to obtain the estimate of the dy namic precision matrix for the idiosyncratic components and then we utilis e the Sherman-Morrison-Woodbury formula to obtain the dynamic precision ma trix for the time series. Under some regularity conditions\, we derive the uniform consistency for the proposed estimators. We provide a simulation study that illustrates the finite-sample performance of the developed meth odology and then apply the proposed method in construction of the minimum variance portfolio from daily stock returns of S\\&P 500 index constituent s in 2022.\n LOCATION:https://researchseminars.org/talk/UEA_mth/8/ END:VEVENT END:VCALENDAR