Tail of a linear diffusion with Markov switching

By Benoîte de Saporta, Jianfeng Yao. 2004

59 citations recorded by the source corpus.

Let Y be a Ornstein–Uhlenbeck diffusion governed by a stationary and ergodic Markov jump process X, i.e. dYt=a(Xt)Ytdt+σ(Xt)dWt, Y0=y0. Ergodicity conditions for Y have been obtained. Here we investigate the tail property of the stationary distribution of this model. A characterization of the only two possible cases is established: light tail or polynomial tail. Our method is based on discretizations and renewal theory. To cite this article: B. de Saporta, J.-F. Yao, C. R. Acad. Sci. Paris, Ser. I 339 (2004).

Subfields: Finance, Management Information Systems, Mathematical Physics

View Paper: https://www.semanticscholar.org/paper/9238a9eee8f0949f7b88c713ca5a8579f47ceacd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

1. M. Benaim
2. Florent Malrieu
3. Pierre-André Zitt
4. St'ephane Le Borgne
5. Claude Lobry

In Mathematical Physics

1. Pierre-André Zitt
2. Florent Malrieu
3. M. Benaim

Citations in the corpus

• Quantitative ergodicity for some switched dynamical systems (2012, background): “Many results are available both in the discrete time setting (see [26, 33, 21, 25, 1]) and in the continuous time setting (see [24, 16, 4]).”
• Long time behavior of diffusions with Markov switching (2009, unknown): “In particular we make more precise the results of [7, 4].”