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X-WR-CALNAME:Mathematical Finance
X-ORIGINAL-URL:https://www.math.ttu.edu/mathematicalfinance
X-WR-CALDESC:Events for Mathematical Finance
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TZOFFSETFROM:-0600
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DTSTART:20230312T080000
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DTSTART:20231105T070000
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DTSTART;TZID=America/Chicago:20231006T140000
DTEND;TZID=America/Chicago:20231006T150000
DTSTAMP:20260406T205424
CREATED:20230724T173619Z
LAST-MODIFIED:20230725T205237Z
UID:1085-1696600800-1696604400@www.math.ttu.edu
SUMMARY:Identification and Estimation of Parameter Instability in High Dimensional Approximate Factor Models
DESCRIPTION:Speaker: Prof. Ruiqi Liu\, Department of Mathematics & Statistics\, Texas Tech University \nAbstract: This paper introduces a novel approach for estimating structural break ratios in the factor loadings of high-dimensional approximate factor models\, where the breaks occur at unknown common dates and the number of factors is unknown. Our method is based on the observation that the sum of the numbers of pseudo factors in the pre- and post-split subsamples is minimized when the sample is split at the structural break. By appropriately transforming these criteria using the eigenvalue ratios of the covariance matrices of the pre-  and post-split subsamples\, we derive consistent estimators for the structural break ratios. Notably\, our framework exhibits remarkable flexibility in accommodating weak factors and can be easily extended to handle multiple breaks. We also introduce a data-driven process to determine the number of breaks. Monte Carlo simulations demonstrate good performance of the proposed estimators. Furthermore\, in an empirical analysis of the FRED-MD dataset\, we identify two structural breaks around January 1983 and March 2009.
URL:https://www.math.ttu.edu/mathematicalfinance/event/identification-and-estimation-of-parameter-instability-in-high-dimensional-approximate-factor-models/
LOCATION:via Zoom
CATEGORIES:Fall 2023,Seminars
ATTACH;FMTTYPE=image/png:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/07/R_Liu.png
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