Using detrended deconvolution foreign exchange network to identify currency status

TL;DR

This paper introduces a hybrid method called DDFEN that combines detrended cross-correlation analysis and network deconvolution to accurately reveal true currency correlations and better reflect long-term currency status changes.

Contribution

The paper presents a novel hybrid DDFEN method that improves the accuracy of foreign exchange network analysis by filtering indirect effects, outperforming traditional methods.

Findings

DDFEN effectively captures long-term currency status changes.

DDFEN provides more stable results than traditional methods.

The method reveals true currency correlations by filtering indirect effects.

Abstract

This article proposed a hybrid detrended deconvolution foreign exchange network construction method (DDFEN), which combined the detrended cross-correlation analysis coefficient (DCCC) and the network deconvolution method together. DDFEN is designed to reveal the `true' correlation of currencies by filtering indirect effects in the foreign exchange networks (FXNs). The empirical results show that DDFEN can reflect the change of currency status in the long term and also perform more stable than traditional network construction methods.