Fast Times, Slow Times: Timescale Separation in Financial Timeseries Data
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arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866918292126433280 |
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| author | Rosenzweig, Jan |
| author_facet | Rosenzweig, Jan |
| contents | Financial time series exhibit multiscale behavior, with interaction between multiple processes operating on different timescales. This paper introduces a method for separating these processes using variance and tail stationarity criteria, framed as generalized eigenvalue problems. The approach allows for the identification of slow and fast components in asset returns and prices, with applications to parameter drift, mean reversion, and tail risk management. Empirical examples using currencies, equity ETFs and treasury yields illustrate the practical utility of the method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11201 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fast Times, Slow Times: Timescale Separation in Financial Timeseries Data Rosenzweig, Jan Portfolio Management Computational Finance Risk Management Statistical Finance Trading and Market Microstructure Financial time series exhibit multiscale behavior, with interaction between multiple processes operating on different timescales. This paper introduces a method for separating these processes using variance and tail stationarity criteria, framed as generalized eigenvalue problems. The approach allows for the identification of slow and fast components in asset returns and prices, with applications to parameter drift, mean reversion, and tail risk management. Empirical examples using currencies, equity ETFs and treasury yields illustrate the practical utility of the method. |
| title | Fast Times, Slow Times: Timescale Separation in Financial Timeseries Data |
| topic | Portfolio Management Computational Finance Risk Management Statistical Finance Trading and Market Microstructure |
| url | https://arxiv.org/abs/2601.11201 |