A Functional Limit Theorem for Dependent Sequences with Infinite Variance Stable Limits

Johan Segers (U.C.Louvain, Belgique)
vendredi 18 juin 2010

Résumé : Under an appropriate regular variation condition, the affinely normalized partial sums of a sequence of independent and identically distributed random variables converges weakly to a non-Gaussian stable random variable. A functional version of this is known to be true as well, the limit process being a stable Lévy process. The main result in the paper is that for a stationary, regularly varying sequence for which clusters of high-threshold excesses can be broken down into asymptotically independent blocks, the properly centered partial sum process still converges to a stable Lévy process. Due to clustering, the Lévy triple of the limit process can be different from the one in the independent case. The convergence takes place in the space of càdlàg functions endowed with Skorohod’s M_1 topology, the more usual J_1 topology being inappropriate as the partial sum processes may exhibit rapid successions of jumps within temporal clusters of large values, collapsing in the limit to a single jump. The result rests on a new limit theorem for point processes which is of independent interest. The theory is applied to moving average processes, squared GARCH(1,1) processes, and stochastic volatility models.

Travail en collaboration avec Bojan BASRAK (University of Zagreb) et Danijel KRIZMANIC (University of Rijeka).

Cet exposé se tiendra à 11h00 en salle C20-13, 20ème étage, Université Paris 1, Centre Pierre Mendes-France, 90 rue de Tolbiac, 75013 Paris (métro : Olympiades).











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