Application of spectral methods for high-frequency financial data to quantifying states of market participants

TL;DR

This paper applies spectral methods to high-frequency forex data to quantify market participant states, revealing fluctuations linked to daily cycles and behavioral diversity, supported by empirical and numerical analysis.

Contribution

It introduces spectral distance measures to analyze high-frequency financial data, connecting market behavior with spectral divergence metrics and participant diversity.

Findings

Spectral similarities fluctuate with Earth's rotation.

Spectral divergence correlates with quotation frequencies.

Distances relate to behavioral parameter distributions.

Abstract

Empirical analysis of the foreign exchange market is conducted based on methods to quantify similarities among multi-dimensional time series with spectral distances introduced in [A.-H. Sato, Physica A, 382 (2007) 258--270]. As a result it is found that the similarities among currency pairs fluctuate with the rotation of the earth, and that the similarities among best quotation rates are associated with those among quotation frequencies. Furthermore it is shown that the Jensen-Shannon spectral divergence is proportional to a mean of the Kullback-Leibler spectral distance both empirically and numerically. It is confirmed that these spectral distances are connected with distributions for behavioral parameters of the market participants from numerical simulation. This concludes that spectral distances of representative quantities of financial markets are related into diversification of behavioral parameters of the market participants.