Parsimonious Modeling of Periodic Time Series Using Fourier and Wavelet Techniques
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916728677597184 |
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| author | Davis, Rhea Balakrishna, N. |
| author_facet | Davis, Rhea Balakrishna, N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05778 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parsimonious Modeling of Periodic Time Series Using Fourier and Wavelet Techniques Davis, Rhea Balakrishna, N. Methodology 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. |
| title | Parsimonious Modeling of Periodic Time Series Using Fourier and Wavelet Techniques |
| topic | Methodology |
| url | https://arxiv.org/abs/2505.05778 |