We consider discrete time dynamical systems and show the linking between Extreme Value Laws and Hitting Time Statistics. The stochastic processes arise from dynamical systems by evaluating an observable function (which achieves a global maximum at a single point of the phase space) along the orbits of the system. We exploit the connection between these two approaches both in the absence and presence of clustering. Clustering means that the occurrence of rare events has a tendency to appear concentrated in time. The strength of the clustering is quantified by the Extremal Index, which takes values between 0 and 1. We associate the existence of an Extremal Index less than 1 to the occurrence of periodic phenomena. We show that, under certain conditions, in the absence of clustering, the point processes of exceedances converge to a standard Poisson process. In the presence of clustering, the point processes converge to a compound Poisson process, so instead of single exceedances, we have entire clusters of exceedances occurring at Poisson times with a geometric distribution ruling its multiplicity.

We will also consider observables achieving a global maximum at multiple points which are correlated by belonging to the same orbit of a certain chosen point. We will see that this is a mechanism capable of creating clustering without an underlying periodic phenomenon and with different types of patterns.