Asset returns and volatility clustering in financial time series

TL;DR

This paper investigates the clustering of large fluctuations in financial returns, introduces an index to measure this behavior, and demonstrates its effectiveness in capturing market dynamics beyond traditional methods.

Contribution

It introduces a new index for quantifying volatility clustering in financial time series, providing deeper insights into market fluctuations beyond conventional analysis.

Findings

Clustering of large fluctuations correlates with market losses.

The new index captures volatility clustering more effectively than traditional methods.

Big losses tend to cluster more severely than big gains.

Abstract

An analysis of the stylized facts in financial time series is carried out. We find that, instead of the heavy tails in asset return distributions, the slow decay behaviour in autocorrelation functions of absolute returns is actually directly related to the degree of clustering of large fluctuations within the financial time series. We also introduce an index to quantitatively measure the clustering behaviour of fluctuations in these time series and show that big losses in financial markets usually lump more severely than big gains. We further give examples to demonstrate that comparing to conventional methods, our index enables one to extract more information from the financial time series.