GARTFIMA Process and its Empirical Spectral Density Based Estimation

2022/08/30 by Niharika Bhootna, Bhootna, Niharika, Kumar, Arun
Economics, Econometrics and Finance · Mathematics · #FOS: Mathematics #Financial Risk and Volatility Modeling #Fuzzy Systems and Optimization #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.2208.14006

openalex publication_date 2022/08/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

In this article, we introduce a Gegenbauer autoregressive tempered fractionally integrated moving average (GARTFIMA) process. We work on the spectral density and autocovariance function for the introduced process. The parameter estimation is done using the empirical spectral density with the help of the nonlinear least square technique and the Whittle likelihood estimation technique. The performance of the proposed estimation techniques is assessed on simulated data. Further, the introduced process is shown to better model the real-world data in comparison to other time series models.

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