  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.
