Long Memory and Volatility Clustering: is the empirical evidence consistent across stock markets?

TL;DR

This paper compares long memory and volatility clustering in US and European stock markets using both traditional models and entropy measures, confirming nonlinear dynamics across markets.

Contribution

It provides a comparative analysis of volatility phenomena using conditionally heteroscedastic models and entropy measures across different stock markets.

Findings

Nonlinear dynamics are confirmed in all markets studied.

Entropy measures align with traditional models in detecting volatility features.

Differences between US and European markets are highlighted.

Abstract

Long memory and volatility clustering are two stylized facts frequently related to financial markets. Traditionally, these phenomena have been studied based on conditionally heteroscedastic models like ARCH, GARCH, IGARCH and FIGARCH, inter alia. One advantage of these models is their ability to capture nonlinear dynamics. Another interesting manner to study the volatility phenomena is by using measures based on the concept of entropy. In this paper we investigate the long memory and volatility clustering for the SP 500, NASDAQ 100 and Stoxx 50 indexes in order to compare the US and European Markets. Additionally, we compare the results from conditionally heteroscedastic models with those from the entropy measures. In the latter, we examine Shannon entropy, Renyi entropy and Tsallis entropy. The results corroborate the previous evidence of nonlinear dynamics in the time series considered.