Weak convergence of partial maxima processes in the M₁ topology

2014/04/05 by Krizmanić, Danijel
#FOS: Mathematics #Probability (math.PR)

paper · doi:10.48550/arxiv.1404.1480

Abstract

It is known that for a sequence of independent and identically distributed random variables (X_n) the regular variation condition is equivalent to weak convergence of partial maxima M_n= max\X₁, …, X_n\, appropriately scaled. A functional version of this is known to be true as well, the limit process being an extremal process, and the convergence takes place in the space of càdlàg functions endowed with the Skorohod J₁ topology. We first show that weak convergence of partial maxima M_n holds also for a class of weakly dependent sequences under the joint regular variation condition. Then using this result we obtain a corresponding functional version for the processes of partial maxima M_n(t) = \bigvee_i=1^{\lfloor nt \rfloor}X_i, t ∈ [0,1], but with respect to the Skorohod M₁ topology, which is weaker than the more usual J₁ topology. We also show that the M₁ convergence generally can not be replaced by the J₁ convergence. Applications of our main results to moving maxima, squared GARCH and ARMAX processes are also given.

Related

• Weak convergence of multivariate partial maxima processes
• On joint weak convergence of partial sum and maxima processes
• Functional weak convergence of partial maxima processes
• Skorokhod M₁ convergence of maxima of multivariate linear processes with heavy-tailed innovations and random coefficients
• A note on joint functional convergence of partial sum and maxima for\n linear processes