\documentclass[11pt,a4]{amsart}
\begin{document}
\section*{{\bf \Large Self-normalized sums of heavy-tailed random vectors}\\[2mm]
Thomas Mikosch (Copenhagen)\\[1mm]
Joint work with Muneya Matsui (Nagoya) and Olivier Wintenberger (Paris, Vienna)}
Self-normalized sum processes have attracted attention through the last decades.
Most work has been devoted to sums of iid variables, and as normalization one often
chooses the sample maximum or standard deviation.
\par
We consider the case of a regularly varying stationary process with infinite second moment. First we derive the joint convergence of sums and self-normalizing 
quantities, then we consider the convergence of the ratios of the sums and 
self-normalizating sequences. We also study aspects of the distributions
of the limiting ratios such as their moments.


   
\end{document}
