Stochastic Riccati Equation Research Articles

Solvability Conditions for Indefinite Linear Quadratic Optimal Stochastic Control Problems and Associated Stochastic Riccati Equations

A linear quadratic optimal stochastic control problem with random coefficients and indefinite state/control weight costs is usually linked to an indefinite stochastic Riccati equation (SRE), which is a matrix-valued quadratic backward stochastic differential equation along with an algebraic constraint involving the unknown. Either the optimal control problem or the SRE is solvable only if the given data satisfy a certain structure condition that has yet to be precisely defined. In this paper, by introducing a notion of subsolution for the SRE, we derive several novel sufficient conditions for the existence and uniqueness of the solution to the SRE and for the solvability of the associated optimal stochastic control problem.

Control of Linear Stochastic Systems with State Dependent Noise

A stochastic control problem is formulated and a solution is described for a linear stochastic system that has a quadratic cost and a linear state dependent noise process appearing in the stochastic system. A stochastic Riccati equation is given that provides an optimal feedback control.

Backward stochastic differential equations with Markov switching driven by Brownian motion and Poisson random measure

This paper is concerned with backward stochastic differential equations with Markov switching driven by Brownian motion and Poisson random measure. The motivation is a constrained stochastic Riccati equation derived from a stochastic linear quadratic optimal control problem with both Poisson and Markovian jumps. The existence and uniqueness of an adapted solution under global Lipschitz condition on the generator is obtained. The continuous dependence of the solution on parameters is proved. Two comparison theorems are also derived by a generalized Girsanov transformation theorem.

Mean–variance portfolio selection under a constant elasticity of variance model

This paper discusses a mean–variance portfolio selection problem under a constant elasticity of variance model. A backward stochastic Riccati equation is first considered. Then we relate the solution of the associated stochastic control problem to that of the backward stochastic Riccati equation. Finally, explicit expressions of the optimal portfolio strategy, the value function and the efficient frontier of the mean–variance problem are expressed in terms of the solution of the backward stochastic Riccati equation.

Solvability of indefinite stochastic Riccati equations and linear quadratic optimal control problems

A new approach to study the indefinite stochastic linear quadratic (LQ) optimal control problems, which we called the “equivalent cost functional method”, is introduced by Yu (2013) in the setup of Hamiltonian system. On the other hand, another important issue along this research direction, is the possible state feedback representation of optimal control and the solvability of associated indefinite stochastic Riccati equations. As the response, this paper continues to develop the equivalent cost functional method by extending it to the Riccati equation setup. Our analysis is featured by its introduction of some equivalent cost functionals which enable us to have the bridge between the indefinite and positive-definite stochastic LQ problems. With such bridge, some solvability relation between the indefinite and positive-definite Riccati equations is further characterized. It is remarkable the solvability of the former is rather complicated than the latter, hence our relation provides some alternative but useful viewpoint. Consequently, the corresponding indefinite linear quadratic problem is discussed for which the unique optimal control is derived in terms of state feedback via the solution of the Riccati equation. In addition, some example is studied using our theoretical results.

A Mixed Linear Quadratic Optimal Control Problem with a Controlled Time Horizon

A mixed linear quadratic (MLQ) optimal control problem is considered. The controlled stochastic system consists of two diffusion processes which are in different time horizons. There are two control actions: a standard control action \(u(\cdot )\) enters the drift and diffusion coefficients of both state equations, and a stopping time \(\tau \), a possible later time after the first part of the state starts, at which the second part of the state is initialized with initial condition depending on the first state. A motivation of MLQ problem from a two-stage project management is presented. It turns out that solving an MLQ problem is equivalent to sequentially solve a random-duration linear quadratic (RLQ) problem and an optimal time (OT) problem associated with Riccati equations. In particular, the optimal cost functional can be represented via two coupled stochastic Riccati equations. Some optimality conditions for MLQ problem is therefore obtained using the equivalence among MLQ, RLQ and OT problems. In case of seeking the optimal time in the family of deterministic times (even through somewhat restrictive, such seeking is still reasonable from practical standpoint), we give a more explicit characterization of optimal actions.

Well Posedness of Operator Valued Backward Stochastic Riccati Equations in Infinite Dimensional Spaces

We prove existence and uniqueness of the mild solution of an infinite dimensional, operator valued, backward stochastic Riccati equation. We exploit the regularizing properties of the semigroup generated by the unbounded operator involved in the equation. Then the results will be applied to characterize the value function and optimal feedback law for an infinite dimensional, linear quadratic control problem with stochastic coefficients. Moreover we shall show that it covers the second variation equation arising in the optimal control of the stochastic heat equation in an interval (see [M. Fuhrman, Y. Hu, and G. Tessitore, C. R. Acad. Sci. Paris Ser. 1 Math., 350 (2012), pp. 683--688] and [M. Fuhrman, Y. Hu, and G. Tessitore, Appl. Math. Optim., 68 (2013), pp. 181--217]).

LQ control for Itô-type stochastic systems with input delays

This paper considers the linear quadratic (LQ) control problem for the Itô-type stochastic system with input delays. Due to simultaneous appearances of diffusion terms (dependent on the state and the control) as well as delays in the dynamic system, the problem is very involved and remains to be solved. We not only provide the solvable condition of the problem but also the explicit expression of the causal and adapted controller for a kind of LQ problems. All of these are based on a stochastic Riccati equation. The key technique is to pursue the explicit cost value of the LQ problem by FBSDE and derive the analytical controller via the interplay between the original problem and its equivalent abstract description.

Universal conductance statistics in a backscattering model: Solving the Dorokhov-Mello-Pereyra-Kumar equation withβ=1, 2, and 4

It is shown that universal transport statistics in the ensembles of scattering matrices with the symmetry index $\ensuremath{\beta}\ensuremath{\in}{1,2,4}$ can be found from a simple microscopic model. A stochastic Riccati equation for the reflection matrix in this model is mapped exactly to the Dorokhov-Mello-Pereyra-Kumar equation for all $\ensuremath{\beta}$s. The map is used to obtain transport statistics in a quasi-one-dimensional disordered conductor in a wide range of its parameters from ballistic to localizations regime. The conductance statistics, shot noise suppression, and transmission channel density are calculated and compared with available analytical results. Deviation from the universal statistics is also discussed.

Continuous-Time Mean-Variance Portfolio Selection with Random Horizon

This paper examines the continuous-time mean-variance optimal portfolio selection problem with random market parameters and random time horizon. Treating this problem as a linearly constrained stochastic linear-quadratic optimal control problem, I explicitly derive the efficient portfolios and efficient frontier in closed forms based on the solutions of two backward stochastic differential equations. Some related issues such as a minimum variance portfolio and a mutual fund theorem are also addressed. All the results are markedly different from those in the problem with deterministic exit time. A key part of my analysis involves proving the global solvability of a stochastic Riccati equation, which is interesting in its own right.

Stochastic H 2/H ∞ control with random coefficients

This paper is concerned with the mixed H2/H∞ control for stochastic systems with random coefficients, which is actually a control combining the H2 optimization with the H∞ robust performance as the name of H2/H∞ reveals. Based on the classical theory of linear-quadratic (LQ, for short) optimal control, the sufficient and necessary conditions for the existence and uniqueness of the solution to the indefinite backward stochastic Riccati equation (BSRE, for short) associated with H∞ robustness are derived. Then the sufficient and necessary conditions for the existence of the H2/H∞ control are given utilizing a pair of coupled stochastic Riccati equations.

Existence of Solutions to a Class of Indefinite Stochastic Riccati Equations

An indefinite stochastic Riccati equation is a matrix-valued, highly nonlinear backward stochastic differential equation together with an algebraic, matrix positive definiteness constraint. We introduce a new approach to solve a class of such equations (including the existence of solutions) driven by one-dimensional Brownian motion. The idea is to replace the original equation by a system of backward stochastic differential equations (without involving any algebraic constraint) whose existence of solutions automatically enforces the original algebraic constraint to be satisfied.

Stochastic differential equations and stochastic linear quadratic optimal control problem with Lévy processes

In this paper, the authors first study two kinds of stochastic differential equations (SDEs) with Levy processes as noise source. Based on the existence and uniqueness of the solutions of these SDEs and multi-dimensional backward stochastic differential equations (BSDEs) driven by Levy processes, the authors proceed to study a stochastic linear quadratic (LQ) optimal control problem with a Levy process, where the cost weighting matrices of the state and control are allowed to be indefinite. One kind of new stochastic Riccati equation that involves equality and inequality constraints is derived from the idea of square completion and its solvability is proved to be sufficient for the well-posedness and the existence of optimal control which can be of either state feedback or open-loop form of the LQ problems. Moreover, the authors obtain the existence and uniqueness of the solution to the Riccati equation for some special cases. Finally, two examples are presented to illustrate these theoretical results.

Infinite Horizon and Ergodic Optimal Quadratic Control for an Affine Equation with Stochastic Coefficients

We study stochastic optimal control problems with a quadratic cost and linear state equation that involves stochastic coefficients and control dependent noise and, moreover, is perturbed by an affine term. Both the infinite horizon case and the ergodic case are treated. To this purpose we introduce a backward stochastic Riccati equation and a dual backward stochastic equation, both considered in the whole time line. Besides some stabilizability conditions we prove the existence of a solution for the two previous equations defined as limit of suitable finite horizon approximating problems. This allows us to perform the synthesis of the optimal control.

Change of filtrations and mean–variance hedging

We consider the mean–variance hedging (MVH) problem (under measure P) of two kinds of investors for two different levels of information, described by two filtrations and such that . Under the assumption that there exists a measure such that all -martingales are -martingales, we give the variance-optimal martingale measure (VOMM) with respect to and through a couple of stochastic Riccati equation (SRE)s, which can be viewed as the same SRE with differential terminal value under . Then we derive an explicit form of the optimal mean–variance strategy and the optimal costs with respect to and . We describe the concept of -no-value-to-investment in the means of mean–variance, and for a given contingent claim , we compare their optimal costs with respect to and .

Backward Stochastic Riccati Equations and Infinite Horizon L-Q Optimal Control with Infinite Dimensional State Space and Random Coefficients

We study the Riccati equation arising in a class of quadratic optimal control problems with infinite dimensional stochastic differential state equation and infinite horizon cost functional. We allow the coefficients, both in the state equation and in the cost, to be random.

The Mean-Variance Hedging of a Defaultable Option with Partial Information

We consider the mean-variance hedging of a defaultable claim in a general stochastic volatility model. By introducing a new measure Q 0, we derive the martingale representation theorem with respect to the investors' filtration 𝔾. We present an explicit form of the optimal-variance martingale measure by means of a stochastic Riccati equation (SRE). For a general contingent claim, we represent the optimal strategy and the optimal cost of the mean-variance hedging by means of another backward stochastic differential equation (BSDE). For the defaultable option, especially when there exists a random recovery rate we give an explicit form of the solution of the BSDE.

Investment with Sequence Losses in an Uncertain Environment and Mean-Variance Hedging

In a market with a discontinuous filtration, whose price is influenced by a random factor, we study an optimization problem of an investor who is facing a sequence of losses driven by a Cox process. We give a form of variance-optimal martingale measure by changing the filtration. By using the solutions of the stochastic Riccati equation and another associated backward stochastic equation, we obtain a solution of the optimization problem of the investor.

Constrained Stochastic LQ Control with Random Coefficients, and Application to Portfolio Selection

This paper is devoted to the study of a stochastic linear-quadratic (LQ) optimal control problem where the control variable is constrained in a cone, and all the coefficients of the problem are random processes. Employing Tanaka's formula, optimal control and optimal cost are explicitly obtained via solutions to two extended stochastic Riccati equations (ESREs). The ESREs, introduced for the first time in this paper, are highly nonlinear backward stochastic differential equations (BSDEs), whose solvability is proved based on a truncation function technique and Kobylanski's results. The general results obtained are then applied to a mean-variance portfolio selection problem for a financial market with random appreciation and volatility rates, and with short-selling prohibited. Feasibility of the problem is characterized, and efficient portfolios and efficient frontier are presented in closed forms.

On the Backward Stochastic Riccati Equation in Infinite Dimensions

We study backward stochastic Riccati equations (BSREs) arising in quadratic optimal control problems with infinite dimensional stochastic differential state equations. We allow the coefficients, both in the state equation and in the cost, to be random. In such a context BSREs are backward stochastic differential equations existing in a non-Hilbert space and involving quadratic nonlinearities. We propose two different notions of solutions to BSREs and prove, for both of them, existence and uniqueness results. We also show that such solutions allow us to perform the synthesis of the optimal control. Finally we apply our results to the optimal control of a delay equation and of a wave equation with random damping.