Study of the Periodicity in Euro-US Dollar Exchange Rates Using Local Alignment and Random Matrices
First published: 2017
Brief summary
Adapts bioinformatics sequence-alignment techniques (originally developed for detecting periodicity in DNA/protein sequences) to detect latent periodicity in EUR/USD exchange rate data despite gaps or irregularities in the time series.
Article
Study of the Periodicity in Euro-US Dollar Exchange Rates Using Local Alignment and Random Matrices is a peer-reviewed conference paper published by Procedia Computer Science (ScienceDirect) in 2017. It focuses on adapts bioinformatics sequence-alignment techniques (originally developed for detecting periodicity in DNA/protein sequences) to detect latent periodicity in EUR/USD exchange rate data despite gaps or irregularities in the time series.
Novel cross-disciplinary method (local sequence alignment + random matrix theory) applied to detect latent periodicity in EUR/USD exchange rate time series, including in the presence of data gaps. This gives the cycle claim a specific numerical and evidential setting rather than presenting periodicity only as a visual impression.
The article reports the following result: The purpose of this study was to detect latent periodicity in the presence of deletions or insertions in the analysed EUR/USD exchange rate data, adapting methods originally developed for finding hidden periodicities in biological sequences. The interpretation is strongest when volatility, non-stationarity, market-regime changes and multiple-frequency testing are treated explicitly.
For cycles researchers, the article brings together eur-usd periodicity, local sequence alignment, random matrix theory, exchange rates. It is relevant to exchange-rate cycle research because periodic components must be separated from volatility, trend, market microstructure and changing monetary regimes.
Because it is a peer-reviewed conference paper, the article is a strong starting point for discussion in the Exchange Rates forum, although its conclusions should still be compared with later replications and updated datasets.
Source details and credits
- Source / publisher: Procedia Computer Science (ScienceDirect)
- Source type: Peer-reviewed conference paper
- URL type: WWW
- Credits: Procedia Computer Science (ScienceDirect)
- URL: https://www.sciencedirect.com/science/article/pii/S1877050917306804