Measuring Volatility Clustering in Stock Markets

TL;DR

This paper introduces a new method to quantify volatility clustering in financial markets, demonstrating its effectiveness across various datasets and highlighting the impact of GARCH filtering on clustering measurement.

Contribution

A novel approach for measuring volatility clustering in financial time series, validated on high-frequency market data and compared with GARCH filtering effects.

Findings

All datasets exhibit volatility clustering.

GARCH filtering significantly reduces observed clustering.

Method effectively measures clustering in financial data.

Abstract

We propose a novel method to quantify the clustering behavior in a complex time series and apply it to a high-frequency data of the financial markets. We find that regardless of used data sets, all data exhibits the volatility clustering properties, whereas those which filtered the volatility clustering effect by using the GARCH model reduce volatility clustering significantly. The result confirms that our method can measure the volatility clustering effect in financial market.