This thesis is a compilation of two papers. In the first paper we investigate a class of two dimensional stochastic differential equations related to susceptible-infected-susceptible epidemic models with demographic stochasticity. While preserving the key features of the model considered in \cite{Mao}, where an \emph{ad hoc} approach has been utilized to prove existence, uniqueness and non explosivity of the solution, we consider an encompassing family of models described by a stochastic differential equation with random and H\"older continuous coefficients. We prove the existence of a unique strong solution by means of a Cauchy-Euler-Peano approximation scheme which is shown to converge in the proper topologies to the unique solution. In the second paper we link a general method for modeling random phenomena using systems of stochastic differential equations to the class of affine stochastic differential equations. This general construction emphasizes the central role of the Duffie-Kan system \cite{DK} as a model for first order approximations of a wide class of nonlinear systems perturbed by noise. We also specialize to a two dimensional framework and propose a direct proof of the Duffie-Kan theorem which does not pass through the comparison with an auxiliary process. Our proof produces a scheme to obtain an explicit representation of the solution once the one dimensional square root process is assigned.

On SDE systems with non-Lipschitz diffusion coefficients

2020

Abstract

This thesis is a compilation of two papers. In the first paper we investigate a class of two dimensional stochastic differential equations related to susceptible-infected-susceptible epidemic models with demographic stochasticity. While preserving the key features of the model considered in \cite{Mao}, where an \emph{ad hoc} approach has been utilized to prove existence, uniqueness and non explosivity of the solution, we consider an encompassing family of models described by a stochastic differential equation with random and H\"older continuous coefficients. We prove the existence of a unique strong solution by means of a Cauchy-Euler-Peano approximation scheme which is shown to converge in the proper topologies to the unique solution. In the second paper we link a general method for modeling random phenomena using systems of stochastic differential equations to the class of affine stochastic differential equations. This general construction emphasizes the central role of the Duffie-Kan system \cite{DK} as a model for first order approximations of a wide class of nonlinear systems perturbed by noise. We also specialize to a two dimensional framework and propose a direct proof of the Duffie-Kan theorem which does not pass through the comparison with an auxiliary process. Our proof produces a scheme to obtain an explicit representation of the solution once the one dimensional square root process is assigned.
2-apr-2020
Inglese
Bernardi, Enrico
Università degli Studi di Bologna
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/151246
Il codice NBN di questa tesi è urn:nbn:it:unibo-26240