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We put forward a complete theory on moment explosion for fairly general state-spaces. This includes a characterization of the validity of the affine transform formula in terms of minimal solutions of a system of generalized Riccati…

Probability · Mathematics 2016-01-07 Eberhard Mayerhofer

We introduce a flexible and tractable infinite-dimensional stochastic volatility model. More specifically, we consider a Hilbert space valued Ornstein-Uhlenbeck-type process, whose instantaneous covariance is given by a pure-jump stochastic…

Probability · Mathematics 2021-08-06 Sonja Cox , Sven Karbach , Asma Khedher

The theory of affine processes on the space of positive semidefinite d x d matrices has been established in a joint work with Cuchiero, Filipovi\'c and Teichmann (2011). We confirm the conjecture stated therein that in dimension d greater…

Probability · Mathematics 2013-01-15 Eberhard Mayerhofer

In this paper we study time-inhomogeneous affine processes beyond the common assumption of stochastic continuity. In this setting times of jumps can be both inaccessible and predictable. To this end we develop a general theory of finite…

Probability · Mathematics 2018-12-21 Martin Keller-Ressel , Thorsten Schmidt , Robert Wardenga

We study the long-time behavior of affine processes on positive self-adjoiont Hilbert-Schmidt operators which are of pure-jump type, conservative and have finite second moment. For subcritical processes we prove the existence of a unique…

Probability · Mathematics 2022-03-29 Martin Friesen , Sven Karbach

We consider stochastic partial differential equations appearing as Markovian lifts of matrix valued (affine) Volterra type processes from the point of view of the generalized Feller property (see e.g., \cite{doetei:10}). We introduce in…

Probability · Mathematics 2019-09-05 Christa Cuchiero , Josef Teichmann

We introduce closed-form transition density expansions for multivariate affine jump-diffusion processes. The expansions rely on a general approximation theory which we develop in weighted Hilbert spaces for random variables which possess…

Statistics Theory · Mathematics 2016-01-07 Damir Filipović , Eberhard Mayerhofer , Paul Schneider

The behavior of affine processes, which are ubiquitous in a wide range of applications, depends crucially on the choice of state space. We study the case where the state space is compact, and prove in particular that (i) no diffusion is…

Probability · Mathematics 2018-03-13 Paul Krühner , Martin Larsson

We introduce and analyse infinite dimensional Wishart processes taking values in the cone $S^+_1(H)$ of positive self-adjoint trace class operators on a separable real Hilbert space $H$. Our main result gives necessary and sufficient…

Probability · Mathematics 2023-04-10 Sonja Cox , Christa Cuchiero , Asma Khedher

We revisit affine diffusion processes on general and on the canonical state space in particular. A detailed study of theoretic and applied aspects of this class of Markov processes is given. In particular, we derive admissibility conditions…

Probability · Mathematics 2009-10-10 Damir Filipovic , Eberhard Mayerhofer

We theoretically and computationally investigate long-memory processes based on the Markovian lifts of affine jump-diffusion processes. A nominal superposition process consisting of an infinite number of interacting affine processes is…

Probability · Mathematics 2026-01-15 Hidekazu Yoshioka

By using lower bound conditions of the L\'evy measure w.r.t. a nice reference measure, the coupling and strong Feller properties are investigated for the Markov semigroup associated with a class of linear SDEs driven by (non-cylindrical)…

Probability · Mathematics 2013-01-09 Feng-Yu Wang , Jian Wang

We consider stochastic (partial) differential equations appearing as Markovian lifts of affine Volterra processes with jumps from the point of view of the generalized Feller property which was introduced in e.g.~\cite{doetei:10}. In…

Probability · Mathematics 2019-08-05 Christa Cuchiero , Josef Teichmann

We present a probabilistic construction of $\mathbb{R}^d$-valued non-linear affine processes with jumps. Given a set $\Theta$ of affine parameters, we define a family of sublinear expectations on the Skorokhod space under which the…

Probability · Mathematics 2022-07-19 Francesca Biagini , Georg Bollweg , Katharina Oberpriller

Affine jump-diffusions constitute a large class of continuous-time stochastic models that are particularly popular in finance and economics due to their analytical tractability. Methods for parameter estimation for such processes require…

Mathematical Finance · Quantitative Finance 2018-11-02 Xiaowei Zhang , Peter W. Glynn

In this paper we consider a broad class of infinite horizon discrete-time optimal control models that involve a nonnegative cost function and an affine mapping in their dynamic programming equation. They include as special cases classical…

Optimization and Control · Mathematics 2017-11-29 Dimitri Bertsekas

Affine point processes are a class of simple point processes with self- and mutually-exciting properties, and they have found useful applications in several areas. In this paper, we obtain large-time asymptotic expansions in large…

Probability · Mathematics 2019-07-26 Xuefeng Gao , Lingjiong Zhu

We develop a recursive approach for deriving closed-form solutions to both conditional and unconditional moments of affine jump diffusions with state-independent jump intensities. Using these moment solutions, we construct closed-form…

Mathematical Finance · Quantitative Finance 2025-04-10 Yan-Feng Wu , Jian-Qiang Hu

This article provides the mathematical foundation for stochastically continuous affine processes on the cone of positive semidefinite symmetric matrices. This analysis has been motivated by a large and growing use of matrix-valued affine…

Probability · Mathematics 2011-04-12 Christa Cuchiero , Damir Filipović , Eberhard Mayerhofer , Josef Teichmann

Markov jump processes are continuous-time stochastic processes which describe dynamical systems evolving in discrete state spaces. These processes find wide application in the natural sciences and machine learning, but their inference is…

Machine Learning · Computer Science 2025-03-05 David Berghaus , Kostadin Cvejoski , Patrick Seifner , Cesar Ojeda , Ramses J. Sanchez
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