Home
Resource
Risk-averse optimal control of diffusion processes
PDF
PDF format is widely accepted and good for printing.
Plug-in required
PDF-1(616.30 kb)
Citation & Export
View Usage Statistics
Staff View

Citation & Export
Hide

Simple citation

Yao, Jianing. Risk-averse optimal control of diffusion processes. Retrieved from https://doi.org/doi:10.7282/T3W66PQ6

Export

Click here for information about Citation Management Tools at Rutgers.

Statistics
Hide

Description

TitleRisk-averse optimal control of diffusion processes
NameYao, Jianing (author); Ruszczynski, Andrzej (chair); Ocone, Daniel (internal member); Yang, Jian (internal member); Lin, Xiaodong (internal member); Dentcheva, Darinka (outside member); Rutgers University; Graduate School - Newark
Date Created2017
Other Date2017-05 (degree)
SubjectManagement
Extent1 online resource (viii, 86 p. : ill.)
DescriptionThis work analyzes an optimal control problem for which the performance is measured by a dynamic risk measure. While dynamic risk measures in discrete-time and the control problems associated are well understood, the continuous-time framework brings great challenges both in theory and practice. This study addresses modeling, numerical schemes and applications. In the first part, we focus on the formulation of a risk-averse control problem. Specifically, we make use of a decoupled forward-backward system of stochastic differential equations to evaluate a fixed policy: the forward stochastic differential equation (SDE) characterizes the evolution of states, and the backward stochastic differential equation (BSDE) does the risk evaluation at any instant of time. Relying on the Markovian structure of the system, we obtain the corresponding dynamic programming equation via weak formulation and strong formulation; in the meanwhile, the risk-averse Hamilton-Jacobi-Bellman equation and its verification are derived under suitable assumptions. In the second part, the main thrust is to find a convergent numerical method to solve the system in discrete-time setting. Specifically, we construct a piecewise-constant Markovian control to show its arbitrarily closeness to the optimal control. The results heavily relies on the regularity of the solution to generalized Hamilton-Jacobi-Bellman PDE. In the third part, we propose a numerical method for risk evaluation defined by BSDE. Using dual representation of the risk measure, we converted risk valuation to a stochastic control problem, where the control is the Radon-Nikodym derivative process. The optimality conditions of such control problem enables us to use a piecewise-constant density (control) to arrive at a close approximation on a short interval. Then, the Bellman principle extends the approximation to any finite time horizon problem. Lastly, we give a financial application in risk management in conjunction with nested simulation.
NotePh.D.
NoteIncludes bibliographical references
Noteby Jianing Yao
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3W66PQ6
Languageeng
CollectionGraduate School - Newark Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
Version 8.4.8
Rutgers University Libraries - Copyright ©2022