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

2025/02/27 by Manganaw N'Daam, N'Daam, Manganaw, Tchilabalo Abozou Kpanzou +3
Economics, Econometrics and Finance · #62J05 #62M10 #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Methodology (stat.ME) #Stochastic processes and financial applications

paper · pdf · doi:10.48550/arxiv.2502.20101

openalex publication_date 2025/02/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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