Isometric Operators on Hilbert Spaces and the Classical Wold Decomposition for Stationary Time Series

2014 ◽  
Author(s):  
Federico Severino
Keyword(s):  
Time Series ◽  
Hilbert Spaces ◽  
Stationary Time Series ◽  
Wold Decomposition ◽  
Stationary Time

scholarly journals A persistence‐based Wold‐type decomposition for stationary time series

10.3982/qe994 ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 203-230 ◽  
Author(s):  
Fulvio Ortu ◽  
Federico Severino ◽  
Andrea Tamoni ◽  
Claudio Tebaldi
Keyword(s):  
Time Series ◽  
Stationary Process ◽  
Financial Time Series ◽  
Variance Decomposition ◽  
Stationary Time Series ◽  
Wold Decomposition ◽  
Process Innovations ◽  
Financial Time ◽  
Stationary Time ◽  
Type Decomposition

This paper shows how to decompose weakly stationary time series into the sum, across time scales, of uncorrelated components associated with different degrees of persistence. In particular, we provide an Extended Wold Decomposition based on an isometric scaling operator that makes averages of process innovations. Thanks to the uncorrelatedness of components, our representation of a time series naturally induces a persistence‐based variance decomposition of any weakly stationary process. We provide two applications to show how the tools developed in this paper can shed new light on the determinants of the variability of economic and financial time series.

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