Nonnegativity preserving convolution kernels. Application to Stochastic Volterra Equations in closed convex domains and their approximation

This work defines and studies one-dimensional convolution kernels that preserve nonnegativity. When the past dynamics of a process is integrated with a convolution kernel like in Stochastic Volterra Equations or in the jump intensity of Hawkes processes, this property allows to get the nonnegativity...

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Veröffentlicht in:	Stochastic processes and their applications 2025-03, Vol.181, p.104535, Article 104535
1. Verfasser:	Alfonsi, Aurélien
Format:	Artikel
Sprache:	eng
Schlagworte:	Completely monotone kernels Hawkes processes with inhibition Mathematics Rough Heston model Stochastic Volterra Equations Volterra Equations
Online-Zugang:	Volltext
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