  The dynamics of prices in financial markets has been studied intensively both
experimentally (data analysis) and theoretically (models). Nevertheless, a
complete stochastic characterization of volatility is still lacking. What it is
well known is that absolute returns have memory on a long time range, this
phenomenon is known as clustering of volatility. In this paper we show that
volatility correlations are power-laws with a non-unique scaling exponent. This
kind of multiscale phenomenology, which is well known to physicists since it is
relevant in fully developed turbulence and in disordered systems, is recently
pointed out for financial series. Starting from historical returns series, we
have also derived the volatility distribution, and the results are in agreement
with a log-normal shape. In our study we consider the New York Stock Exchange
(NYSE) daily composite index closes (January 1966 to June 1998) and the US
Dollar/Deutsch Mark (USD-DM) noon buying rates certified by the Federal Reserve
Bank of New York (October 1989 to September 1998).
