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Ergodicity of affine processes on the cone of symmetric positive semidefinite matrices

Part of: Markov processes Stochastic processes Ergodic theory

Published online by Cambridge University Press:  24 September 2020

Martin Friesen ,
Peng Jin ,
Jonas Kremer  and
Barbara Rüdiger
Martin Friesen*
Affiliation:
University of Wuppertal
Peng Jin*
Affiliation:
Shantou University
Jonas Kremer*
Affiliation:
University of Wuppertal
Barbara Rüdiger*
Affiliation:
University of Wuppertal
*
*Postal address: School of Mathematics and Natural Sciences, University of Wuppertal, 42119 Wuppertal, Germany. E-mail address: [email protected]
**Postal address: Department of Mathematics, Shantou University, Shantou, Guangdong 515063, China. E-mail address: [email protected]
***Postal address: School of Mathematics and Natural Sciences, University of Wuppertal, 42119 Wuppertal, Germany. E-mail address: [email protected]
****Postal address: School of Mathematics and Natural Sciences, University of Wuppertal, 42119 Wuppertal, Germany. E-mail address: [email protected]
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Abstract

This article investigates the long-time behavior of conservative affine processes on the cone of symmetric positive semidefinite $d\times d$ matrices. In particular, for conservative and subcritical affine processes we show that a finite $\log$ -moment of the state-independent jump measure is sufficient for the existence of a unique limit distribution. Moreover, we study the convergence rate of the underlying transition kernel to the limit distribution: first, in a specific metric induced by the Laplace transform, and second, in the Wasserstein distance under a first moment assumption imposed on the state-independent jump measure and an additional condition on the diffusion parameter.

Keywords

Affine processinvariant distributionlimit distributionergodicity

MSC classification

Primary: 60J25: Continuous-time Markov processes on general state spaces 37A25: Ergodicity, mixing, rates of mixing
Secondary: 60G10: Stationary processes 60J75: Jump processes
Type
Original Article
Information
Advances in Applied Probability , Volume 52 , Issue 3 , September 2020 , pp. 825 - 854
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Copyright
© Applied Probability Trust 2020

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