Parsimonious Modeling of Periodic Time Series Using Fourier and Wavelet Techniques

2025/05/09 by Rhea Davis, Davis, Rhea, N. Balakrishna +1
Decision Sciences · Economics, Econometrics and Finance · #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Forecasting Techniques and Applications #Methodology (stat.ME) #Stock Market Forecasting Methods

paper · pdf · doi:10.48550/arxiv.2505.05778

openalex publication_date 2025/05/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper proposes Fourier-based and wavelet-based techniques for analyzing periodic financial time series. Conventional models such as the periodic autoregressive conditional heteroscedastic (PGARCH) and periodic autoregressive conditional duration (PACD) often involve many parameters. The methods put forward here resulted in more parsimonious models with increased forecast efficiency. The effectiveness of these approaches is demonstrated through simulation and data analysis studies.

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