An Ordinal Pattern Approach to Detect and to Model Leverage Effects and Dependence Structures Between Financial Time Series

TL;DR

This paper introduces ordinal pattern dependence as a non-linear alternative to correlation for analyzing dependence structures in financial time series, demonstrating its presence in real data.

Contribution

It proposes two types of ordinal pattern dependence and explores their application to financial data, offering a non-parametric, non-linear dependence measure.

Findings

Positive and negative ordinal pattern dependence are observed in financial data.

Ordinal pattern dependence captures non-linear relationships beyond correlation.

The approach provides new insights into dependence structures in finance.

Abstract

We introduce two types of ordinal pattern dependence between time series. Positive (resp. negative) ordinal pattern dependence can be seen as a non-paramatric and in particular non-linear counterpart to positive (resp. negative) correlation. We show in an explorative study that both types of this dependence show up in real world financial data.