EconPapers    
Economics at your fingertips  
 

Detecting correlations and triangular arbitrage opportunities in the Forex by means of multifractal detrended cross-correlations analysis

Robert G\k{e}barowski, Pawe{\l} O\'swi\k{e}cimka, Marcin W\k{a}torek and Stanis{\l}aw Dro\.zd\.z

Papers from arXiv.org

Abstract: Multifractal detrended cross-correlation methodology is described and applied to Foreign exchange (Forex) market time series. Fluctuations of high frequency exchange rates of eight major world currencies over 2010-2018 period are used to study cross-correlations. The study is motivated by fundamental questions in complex systems' response to significant environmental changes and by potential applications in investment strategies, including detecting triangular arbitrage opportunities. Dominant multiscale cross-correlations between the exchange rates are found to typically occur at smaller fluctuation levels. However hierarchical organization of ties expressed in terms of dendrograms, with a novel application of the multiscale cross-correlation coefficient, are more pronounced at large fluctuations. The cross-correlations are quantified to be stronger on average between those exchange rate pairs that are bound within triangular relations. Some pairs from outside triangular relations are however identified to be exceptionally strongly correlated as compared to the average strength of triangular correlations.This in particular applies to those exchange rates that involve Australian and New Zealand dollars and reflects their economic relations. Significant events with impact on the Forex are shown to induce triangular arbitrage opportunities which at the same time reduce cross--correlations on the smallest time scales and act destructively on the multiscale organization of correlations. In 2010--2018 such instances took place in connection with the Swiss National Bank intervention and the weakening of British pound sterling accompanying the initiation of Brexit procedure. The methodology could be applicable to temporal and multiscale pattern detection in any time series.

Date: 2019-06, Revised 2019-10
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Published in Nonlinear Dynamics 98, 2349-2364 (2019)

Downloads: (external link)
http://arxiv.org/pdf/1906.07491 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1906.07491

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().

 
Page updated 2019-12-17
Handle: RePEc:arx:papers:1906.07491