On Inhomogeneous Affine Volterra Processes: Stationarity and Applications to the Volterra Heston Model

TL;DR

This paper studies inhomogeneous affine Volterra processes, introducing a 'fake stationarity' concept to analyze their finite-time behavior and long-term limits, with applications to the Volterra Heston model.

Contribution

It develops a framework for understanding finite-time and asymptotic behavior of inhomogeneous affine Volterra processes, including a new 'fake stationary' regime and explicit characteristic functions.

Findings

Introduces the Fake stationary Volterra Heston model with closed-form characteristic function.

Establishes existence of limiting distributions depending on initial states, except for fractional kernels.

Highlights the importance of finite-time analysis due to intractability of long-term dynamics.

Abstract

True Volterra equations are inherently non stationary and therefore do not admit $\textit{genuine stationary regimes}$ over finite horizons. This motivates the study of the finite-time behavior of the solutions to scaled inhomogeneous affine Stochastic Volterra equations through the lens of a weaker notion of stationarity referred to as $\textit{fake stationary regime}$ in the sense that all marginal distributions share the same expectation and variance. As a first application, we introduce the $\textit{Fake stationary Volterra Heston model}$ and derive a closed-form expression for its characteristic function. Having established this finite-time proxy for stationarity, we then investigate the asymptotic (long-time) behavior to assess whether genuine stationary regimes emerge in the limit. Using an extension of the exponential-affine transformation formula for those processes, we establish in the long run the existence of limiting distributions, which (unlike in the case of classical affine diffusion processes) may depend on the initial state of the process, unless the Volterra kernel coincides with the $\alpha-$ fractional integration kernel, for which the dependence on the initial state vanishes. We then proceed to the construction of stationary processes associated with these limiting distributions. However, the dynamics in this long-term regime are analytically intractable, and the process itself is not guaranteed to be stationary in the classical sense over finite horizons. This highlights the relevance of finite-time analysis through the lens of the aforementioned $\textit{fake stationarity}$, which offers a tractable approximation to stationary behavior in genuinely non-stationary Volterra systems.