Properties of low variability periods in financial time series

TL;DR

This paper investigates the properties of low-variability periods in financial time series, demonstrating that a simple scaling analysis can reveal detailed multi-affine structures more effectively than traditional methods.

Contribution

It introduces a scaling analysis method for low-variability periods that uncovers multi-affinity in financial data more efficiently than traditional multi-affine analysis.

Findings

Good scaling behavior observed in currency and stock index data

High-frequency USD-EUR exchange rate data confirmed theoretical predictions

Method reveals more details about time-series than traditional approaches

Abstract

Properties of low-variability periods in the time series are analysed. The theoretical approach is used to show the relationship between the multi-scaling of low-variability periods and multi-affinity of the time series. It is shown that this technically simple method is capable of reveling more details about time-series than the traditional multi-affine analysis. We have applied this scaling analysis to financial time series: a number of daily currency and stock index time series. The results show a good scaling behaviour for different model parameters. The analysis of high-frequency USD-EUR exchange rate data confirmed the theoretical expectations.