scholarly journals Large‐sample approximations and change testing for high‐dimensional covariance matrices of multivariate linear time series and factor models

2020 ◽  
Author(s):  
Monika Bours ◽  
Ansgar Steland
Keyword(s):  
Time Series ◽  
Linear Time ◽  
Factor Models ◽  
Covariance Matrices ◽  
High Dimensional ◽  
Large Sample

scholarly journals Large-sample approximations for variance-covariance matrices of high-dimensional time series

Bernoulli ◽  
2017 ◽  
Vol 23 (4A) ◽  
pp. 2299-2329 ◽  
Author(s):  
Ansgar Steland ◽  
Rainer von Sachs
Keyword(s):  
Time Series ◽  
Covariance Matrices ◽  
High Dimensional ◽  
Large Sample

scholarly journals Multivariate Fractional Components Analysis

2021 ◽  
Author(s):  
Tobias Hartl ◽  
Roland Jucknewitz
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Covariance Matrices ◽  
High Dimensional ◽  
Nonstationary Processes ◽  
Realized Covariance ◽  
Out Of Sample ◽  
Short Memory ◽  
Components Analysis ◽  
Fractional Orders

Abstract We propose a setup for fractionally cointegrated time series which is formulated in terms of latent integrated and short-memory components. It accommodates nonstationary processes with different fractional orders and cointegration of different strengths and is applicable in high-dimensional settings. In an application to realized covariance matrices, we find that orthogonal short- and long-memory components provide a reasonable fit and competitive out-of-sample performance compared with several competing methods.

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