Abstract:
We deal with two independent random walks
with subexponential distributions of their increments. We
study the tail distributional asymptotics for the sum of their
partial maxima within random time intervals. Assuming the
distributions of the lengths of these intervals to be relatively
small, with respect to that of the increments of the random walks,
we show that the sum of the maxima takes a large value mostly due a
large value of a single summand (this is the so-called "principle of
a single big jump").
Keywords:random sums of random variables, convolution tail, convolution equivalence, heavy-tailed distributions, subexponential istributions, the principle of a single big jump.