Activity autocorrelation in financial marketsA comparative study between several models

We study the activity of financial markets, i.e., the number of transactions per unit of time. Using the diffusion entropy technique we show that the autocorrelation of the activity is caused by the presence of peaks whose time distances are distributed following an asymptotic power-law which ultimately recovers an exponential behavior. We discuss these results in comparison with ARCH models, stochastic volatility models and multi-agent models showing that ARCH and stochastic volatility models better describe the observed experimental evidences.