  Financial markets provide an ideal frame for the study of crossing or
first-passage time events of non-Gaussian correlated dynamics mainly because
large data sets are available. Tick-by-tick data of six futures markets are
herein considered resulting in fat tailed first-passage time probabilities. The
scaling of the return with the standard deviation collapses the probabilities
of all markets examined, and also for different time horizons, into single
curves, suggesting that first-passage statistics is market independent (at
least for high-frequency data). On the other hand, a very closely related
quantity, the survival probability, shows, away from the center and tails of
the distribution, a hyperbolic $t^{-1/2}$ decay typical of a Markovian dynamics
albeit the existence of memory in markets. Modifications of the Weibull and
Student distributions are good candidates for the phenomenological description
of first-passage time properties under certain regimes. The scaling strategies
shown may be useful for risk control and algorithmic trading.
