Infinite variance stable limits for sums of dependent random variables

Katarzyna Bartkiewicz (1) , Adam Jakubowski (1) , Thomas Mikosch (2) , Olivier Wintenberger (3)

1 Faculty of Mathematics and Computer Science [Toruń]
2 Laboratory of Actuarial Mathematics
3 CEREMADE - CEntre de REcherches en MAthématiques de la DEcision

The aim of this paper is to provide conditions which ensure that the affinely transformed partial sums of a strictly stationary process converge in distribution to an in?nite variance stable distribution. Conditions for this convergence to hold are known in the literature. However, most of these results are qualitative in the sense that the parameters of the limit distribution are expressed in terms of some limiting point process. In this paper we will be able to determine the parameters of the limiting stable distribution in terms of some tail characteristics of the underlying stationary sequence. We will apply our results to some standard time series models, including the GARCH(1, 1) process and its squares, the stochastic volatility models and solutions to stochastic recurrence equations.

Mots clés: ARMA process; stationary sequence; stable limit distribution; weak conver- gence; mixing; weak dependence; characteristic function; regular variation; GARCH; stochastic volatility model; ARMA process.

Domaines: Probabilités [math.PR]; Statistiques [math.ST]; Théorie [stat.TH]

Katarzyna Bartkiewicz, Adam Jakubowski, Thomas Mikosch, Olivier Wintenberger. Infinite variance stable limits for sums of dependent random variables. 2009. ⟨hal-00395525v1⟩