Impulse Control in Finance: Numerical Methods and Viscosity Solutions
Parsiad Azimzadeh

TL;DR
This thesis develops efficient, convergent numerical methods for solving impulse control PDEs in finance, using viscosity solutions and policy iteration, with applications to various financial optimization problems.
Contribution
It introduces a policy iteration algorithm for nonlinear Bellman problems derived from impulse control, extending convergence theory to nonlocal PDEs in finance.
Findings
Proved convergence of the proposed numerical schemes.
Applied methods to classical financial control problems.
Established new links between w.c.d.d. matrices and M-matrices.
Abstract
The goal of this thesis is to provide efficient and provably convergent numerical methods for solving partial differential equations (PDEs) coming from impulse control problems motivated by finance. Impulses, which are controlled jumps in a stochastic process, are used to model realistic features in financial problems which cannot be captured by ordinary stochastic controls. The dynamic programming equations associated with impulse control problems are Hamilton-Jacobi-Bellman quasi-variational inequalities (HJBQVIs) Other than in certain special cases, the numerical schemes that come from the discretization of HJBQVIs take the form of complicated nonlinear matrix equations also known as Bellman problems. We prove that a policy iteration algorithm can be used to compute their solutions. In order to do so, we employ the theory of weakly chained diagonally dominant (w.c.d.d.) matrices.…
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Taxonomy
TopicsStochastic processes and financial applications · Economic theories and models · Risk and Portfolio Optimization
