LINEAR-QUADRATIC CONTROL FOR A CLASS OF STOCHASTIC VOLTERRA EQUATIONS: SOLVABILITY AND APPROXIMATION
成果类型：
Article
署名作者：
Jaber, Eduardo Abi; Miller, Enzo; Pham, Huyen
署名单位：
Universite Paris Cite
刊物名称：
ANNALS OF APPLIED PROBABILITY
ISSN/ISSBN：
1050-5164
DOI：
10.1214/20-AAP1645
发表日期：
2021
页码：
2244-2274
关键词：
摘要：
We provide an exhaustive treatment of linear-quadratic control problems for a class of stochastic Volterra equations of convolution type, whose kernels are Laplace transforms of certain signed matrix measures which are not necessarily finite. These equations are in general neither Markovian nor semimartingales, and include the fractional Brownian motion with Hurst index smaller than 1/2 as a special case. We establish the correspondence of the initial problem with a possibly infinite dimensional Markovian one in a Banach space, which allows us to identify the Markovian controlled state variables. Using a refined martingale verification argument combined with a squares completion technique, we prove that the value function is of linear quadratic form in these state variables with a linear optimal feedback control, depending on nonstandard Banach space valued Riccati equations. Furthermore, we show that the value function of the stochastic Volterra optimization problem can be approximated by that of conventional finite dimensional Markovian linear-quadratic problems, which is of crucial importance for numerical implementation.