О сумме максимумов случайных сумм случайных величин при наличии тяжелых хвостов распределений

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").