Uncovering Hidden Insights with Long-Memory Process Detection: An In-Depth Overview

Uncovering Hidden Insights with Long-Memory Process Detection: An In-Depth Overview

Long-memory models are frequently used in finance and other fields to capture long-range dependence in time series data. However, correctly identifying whether a process has long memory is crucial. This paper highlights a significant limitation in using the sample autocorrelation function (ACF) to i...

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Bibliographic Details
Main Authors: Hossein Hassani, Masoud Yarmohammadi, Leila Marvian Mashhad
Format: Article
Language:English
Published: MDPI AG 2023-06-01
Series:Risks
Subjects:
long-memory process
sum of sample autocorrelation function
Hassani’s <named-content content-type="inline-formula"><inline-formula> <mml:math id="mm300"> <mml:semantics> <mml:mfrac bevelled="true"> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> <mml:mn>2</mml:mn> </mml:mfrac> </mml:semantics> </mml:math> </inline-formula></named-content> theorem
spectral density
time series
autocorrelation
Online Access:https://www.mdpi.com/2227-9091/11/6/113

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https://www.mdpi.com/2227-9091/11/6/113

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