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arXiv · 2502.20101

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

Manganaw N'Daam·Tchilabalo Abozou Kpanzou·Edoh Katchekpele
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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 (LAD) 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 Porter-Hudak (GPH) and Wavelet-Based Log-Periodogram (WBLP) estimators, particularly in terms of mean squared error, across various sample sizes and parameter configurations.

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Manganaw N'Daam, Tchilabalo Abozou Kpanzou, Edoh Katchekpele. 2025-02-27. Wavelet-based estimation of long-memory parameter in stochastic volatility models using a robust log-periodogram. https://arxiv.org/abs/2502.20101

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