On Extreme Value Asymptotics of Projected Sample Covariances in High Dimensions with Applications in Finance and Convolutional Networks

Ansgar Steland ()
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Ansgar Steland: RWTH Aachen University, Institute of Statistics

A chapter in Advanced Statistical Methods in Process Monitoring, Finance, and Environmental Science, 2024, pp 367-388 from Springer

Abstract: Abstract Maximum-type statistics of certain functions of the sample covariance matrix of high-dimensional vector time series are studied to statistically confirm or reject the null hypothesis that a dataset has been collected under normal conditions. Within a linear time series framework, it is shown that Gumbel-type extreme value asymptotics hold true. As applications, we discuss long-only mimimal-variance portfolio optimization, ETF index tracking, convolutional deep learners for image analysis, and the analysis of array-of-sensors data.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-69111-9_17

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DOI: 10.1007/978-3-031-69111-9_17

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