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<p>This paper proposes Fourier-based and wavelet-based techniques for analyzing periodic financial time series. Conventional models such as the periodic generalized 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.</p>

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Parsimonious modeling of periodic time series using Fourier and wavelet techniques

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Rhea Davis,
  • N. Balakrishna

摘要

This paper proposes Fourier-based and wavelet-based techniques for analyzing periodic financial time series. Conventional models such as the periodic generalized 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.