Quantile Correlations: Uncovering temporal dependencies in financial time series

TL;DR

This paper introduces the quantile-based correlation function as a novel tool to analyze and compare temporal dependencies in financial time series, demonstrated through empirical analysis of S&P 500 data and comparison with GARCH models.

Contribution

The paper proposes the quantile-based correlation function for analyzing financial data and demonstrates its effectiveness in revealing differences from traditional stochastic models.

Findings

Quantile correlations reveal distinct temporal dependencies in financial data.

Significant differences between empirical data and GARCH models are identified.

Quantile correlation function effectively assesses model-data agreement.

Abstract

We conduct an empirical study using the quantile-based correlation function to uncover the temporal dependencies in financial time series. The study uses intraday data for the S\&P 500 stocks from the New York Stock Exchange. After establishing an empirical overview we compare the quantile-based correlation function to stochastic processes from the GARCH family and find striking differences. This motivates us to propose the quantile-based correlation function as a powerful tool to assess the agreements between stochastic processes and empirical data.