Long memory and volatility clustering: is the empirical evidence consistent across stock markets?

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 cap...

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Bibliographic Details
Main Author: Bentes, S. R. (author)
Other Authors: Menezes, R. (author), Mendes, D. A. (author)
Format: article
Language:eng
Published: 2017
Subjects:
Long memory
Volatility clustering
ARCH type models
Nonlinear dynamics
Entropy
Online Access:https://ciencia.iscte-iul.pt/id/ci-pub-14585
Country:Portugal
Oai:oai:repositorio.iscte-iul.pt:10071/13988
Description
Summary: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 phenomenon 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.

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