Work

Wavelet-based estimation of long-memory parameter in stochastic volatility models using a robust log-periodogram

Year: 2025

Type: article

Abstract: In this paper, we propose a novel method for estimating the long-memory parameter in time series. By combining the multi-resolution framework of wavelets with the robustness of the least absolute deviations criterion, we introduce a periodogram providing a robust alternative to classical methods in the presence of non-Gaussian noise. Incorporating this periodogram into a log-periodogram regression, we develop a new estimator. Simulation studies demonstrate that our estimator outperforms the Geweke and PorterHudak (GPH) and wavelet-based log-periodogram (WBLP) estimators, particularly in terms of mean squared error, across various sample sizes and parameter configurations.

Source: Baku Mathematical Journal

DOI: 10.32010/j.bmj.2025.14

Authors: Manganaw NDaam, Tchilabalo Abozou Kpanzou, Edoh Katchekpele

Institution: University of Kara

Language: English

Topic: Financial Risk and Volatility Modeling

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: bronze

OpenAlex ID: W4414537909

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