Stationarity and Ergodicity for an Affine Two-Factor Model

We study the existence of a unique stationary distribution and ergodicity for a two-dimensional affine process. Its first coordinate process is supposed to be a so-called α-root process with α ∈ (1, 2]. We prove the existence of a unique stationary distribution for the affine process in the α ∈ (1,...

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Published in:Advances in applied probability 2014-09, Vol.46 (3), p.878-898
Main Authors: Barczy, Mátyás, Döring, Leif, Li, Zenghu, Pap, Gyula
Format: Article
Language:English
Subjects:
37A25
60J25
Affine process
Differential equations
Ergodic processes
Ergodic theory
ergodicity
Foster-Lyapunov criteria
General Applied Probability
Geometry
Lebesgue measures
Markov processes
Martingales
Mathematical analysis
Mathematical models
Mathematical moments
Mathematics
Probability
Probability distribution
Semigroups
stationary distribution
Studies
Symbols
Two dimensional
Two dimensional modeling
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description We study the existence of a unique stationary distribution and ergodicity for a two-dimensional affine process. Its first coordinate process is supposed to be a so-called α-root process with α ∈ (1, 2]. We prove the existence of a unique stationary distribution for the affine process in the α ∈ (1, 2] case; furthermore, we show ergodicity in the α = 2 case.
doi_str_mv 10.1239/aap/1409319564
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source JSTOR Archival Journals and Primary Sources Collection
subjects 37A25
60J25
Affine process
Differential equations
Ergodic processes
Ergodic theory
ergodicity
Foster-Lyapunov criteria
General Applied Probability
Geometry
Lebesgue measures
Markov processes
Martingales
Mathematical analysis
Mathematical models
Mathematical moments
Mathematics
Probability
Probability distribution
Semigroups
stationary distribution
Studies
Symbols
Two dimensional
Two dimensional modeling
title Stationarity and Ergodicity for an Affine Two-Factor Model
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