Estimating the memory parameter for possibly non-linear and non-Gaussian time series with wavelets

2021 ◽  
Author(s):  
Chen Xu ◽  
Ye Zhang
Keyword(s):  
Time Series ◽  
Non Linear ◽  
Non Gaussian ◽  
Gaussian Time ◽  
Gaussian Time Series ◽  
Memory Parameter

Estimating the memory parameter for potentially non-linear and non-Gaussian time series with wavelets

2022 ◽  
Author(s):  
Chen Xu ◽  
Ye Zhang
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Asymptotic Theory ◽  
Financial Time Series ◽  
Non Linear ◽  
Long Memory Processes ◽  
Non Gaussian ◽  
Gaussian Time ◽  
Gaussian Time Series ◽  
Memory Parameter

Abstract The asymptotic theory for the memory-parameter estimator constructed from the log-regression with wavelets is incomplete for 1/$f$ processes that are not necessarily Gaussian or linear. Having a complete version of this theory is necessary because of the importance of non-Gaussian and non-linear long-memory models in describing financial time series. To bridge this gap, we prove that, under some mild assumptions, a newly designed memory estimator, named LRMW in this paper, is asymptotically consistent. The performances of LRMW in three simulated long-memory processes indicate the efficiency of this new estimator.

Detecting Conditional Independence for Modeling Non-Gaussian Time Series

2020 ◽  
Vol 49 (2) ◽  
pp. 578-595
Author(s):  
Sudheesh K. Kattumannil ◽  
Deemat C. Mathew ◽  
G. Hareesh
Keyword(s):  
Time Series ◽  
Conditional Independence ◽  
Non Gaussian ◽  
Gaussian Time ◽  
Gaussian Time Series
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