Stochastic differential games for optimal investment and risk control problems in an incomplete market

State Transition Tensors for Continuous-Thrust Control of Three-Body Relative Motion

In this paper, we apply the martingale approach to investigate the optimal investment and risk control problem for an insurer in an incomplete market. The claim risk of per policy is characterized by a compound Poisson process with drift, and the insurer can be invested in multiple risky assets whose price processes are described by the geometric Brownian motions model. By ‘complete’ the incomplete market, closed-form solutions to the problems of mean–variance criterion and expected exponential utility maximization are obtained. Moreover, numerical simulations are presented to illustrate the results with the basic parameters.

This work is concerned with the maximum principles for optimal control problems governed by 3-dimensional Navier--Stokes equations. Some types of state constraints (time variables) are considered.

This paper considers optimal investment and risk control problem under the Hull and White Stochastic Volatility (SV) model for an Insurer who aims to optimize the investment and risk control strategies. The surplus process of the insurer is assumed to follow the Brownian motion with drift. An Insurer can invest in the financial market consisting of risk-free and risky assets whose price process satisfies Hull-White SV model. By applying the stochastic dynamic programming approach, we derive closed-form expressions for the optimal strategies and the value function. We find that under the Hull and White model, the interest rate and risk aversion parameters both influence optimal strategies. Moreover, we provide a numerical example to illustrate the model’s economic implications.

This paper considers an optimal investment and risk control problem under the criterion of logarithm utility maximization. The risky asset process and the insurance risk process are described by stochastic differential equations with jumps and anticipating coefficients. The insurer invests in the financial assets and controls the number of policies based on some partial information about the financial market and the insurance claims. The forward integral and Malliavin calculus for Lévy processes are used to obtain a characterization of the optimal strategy. Some special cases are discussed and the closed-form expressions for the optimal strategies are derived.

This paper addresses a investment and risk control problem with a delay for an insurer in the defaultable market. Suppose that an insurer can invest in a risk-free bank account, a risky stock and a defaultable bond. Taking into account the history of the insurer's wealth performance, the controlled wealth process is governed by a stochastic delay differential equation. The insurer's goal is to maximize the expected exponential utility of the combination of terminal wealth and average performance wealth. We decompose the original optimization problem into two subproblems: a pre-default case and a post-default case. The explicit solutions in a finite dimensional space are derived for a illustrative situation, and numerical illustrations and sensitivity analysis for our results are provided.

Optimal risk control and dividend distribution policies for a diffusion model with terminal value

Practical Methods for Optimal Control using Nonlinear Programming

A numerical method for an optimal control problem with minimum sensitivity on coefficient variation

This paper studies an optimal investment and risk control problem for an insurer with default contagion and regime-switching. The insurer in our model allocates his/her wealth across multi-name defaultable stocks and a riskless bond under regime-switching risk. Default events have an impact on the distress state of the surviving stocks in the portfolio. The aim of the insurer is to maximize the expected utility of the terminal wealth by selecting optimal investment and risk control strategies. We characterize the optimal trading strategy of defaultable stocks and risk control for the insurer. By developing a truncation technique, we analyze the existence and uniqueness of global (classical) solutions to the recursive HJB system. We prove the verification theorem based on the (classical) solutions of the recursive HJB system.

We consider the problem of optimal investment in an incomplete market with borrowing, random and possibly unbounded coefficients, and the power utility from terminal wealth. We use the Heston model for stochastic volatility, and the quadratic-affine model for interest rates. The resulting problem is an example of optimal stochastic control problem with a nonlinear system dynamics which is due to borrowing, the square-root non-linearity of Heston model, and the quadratic non-linearity of the interest rates. Explicit closed-form solution is obtained by a certain piece-wise completion of squares method. The resulting optimal control law is of linear state-feedback form the gain of which can be in up to three different regimes.

In this chapter, optimal state feedback control problems of nonlinear systems with time delays are studied. In general, the optimal control for time-delay systems is an infinite-dimensional control problem, which is very difficult to solve and there is presently no good method for dealing with this problem. In this chapter, the optimal state feedback control problems of nonlinear systems with time delays both in states and controls are investigated. By introducing a delay matrix function, the explicit expression of the optimal control function can be obtained. Next, for nonlinear time-delay systems with saturating actuators, we further study the optimal control problem using a nonquadratic functional, where two optimization processes are developed for searching the optimal solutions. The above two results are for the infinite-horizon optimal control problem. To the best of our knowledge, there are no results on the finite-horizon optimal control of nonlinear time-delay systems. Hence, in the last part of this chapter, a novel optimal control strategy is developed to solve the finite-horizon optimal control problem for a class of time-delay systems.KeywordsOptimal ControllerDiscrete Nonlinear Time-delay SystemFinite Horizon Optimal Control ProblemActuator SaturationInfinite Dimensional Control ProblemsThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper mainly talks about some equivalences of the optimal time control problems dominated by the impulsive ordinary differential equation. As the pulse has equipped with the quality of instantaneous effect, so, there is no fundamental difference between integral optimal control problems dominated by impulsive differential system and corresponding differential equation, which is also dominated by integral optimal control problems. And this is well proved by current studies, thus the instantaneous effect has been averaged. However, in terms of the optimal time control problems, the pulse has definite impacts on the optimal time because the optimal time is instantaneous value. Thus, one of the purposes, to study optimal time control problems dominated by the impulsive differential system, is to discover the influences of the pulse puts on the default properties of differential system from the aspect of optimal time control. In this paper, we offered a method to define optimal time and made a further study on the equivalence relation among optimal time control problems, optimal norm control problems and optimal control problems. During this process, we had finished some relevant targets which mentioned in this paper.

1 A Survey on Computational Optimal Control.- Issues in the Direct Transcription of Optimal Control Problems to Sparse Nonlinear Programs.- Optimization in Control of Robots.- Large-scale SQP Methods and their Application in Trajectory Optimization.- Solving Optimal Control and Pursuit-Evasion Game Problems of High Complexity.- 2 Theoretical Aspects of Optimal Control and Nonlinear Programming.- Continuation Methods In Boundary Value Problems.- Second Order Optimality Conditions for Singular Extremals.- Synthesis of Adaptive Optimal Controls for Linear Dynamic Systems.- Control Applications of Reduced SQP Methods.- Time Optimal Control of Mechanical Systems.- 3 Algorithms for Optimal Control Calculations.- Second Order Algorithm for Time Optimal Control of a Linear System.- An SQP-type Solution Method for Constrained Discrete-Time Optimal Control Problems.- Numerical Methods for Solving Differential Games, Prospective Applications to Technical Problems.- Construction of the Optimal Feedback Controller for Constrained Optimal Control Problems with Unknown Disturbances.- Repetitive Optimization for Predictive Control of Dynamic Systems under Uncertainty.- Optimal Control of Multistage Systems Described by High-Index Differential-Algebraic Equations.- A New Class of a High Order Interior Point Method for the Solution of Convex Semiinfinite Optimization Problems.- A Structured Interior Point SQP Method for Nonlinear Optimal Control Problems.- 4 Software for Optimal Control Calculations.- Automated Approach for Optimizing Dynamic Systems.- ANDECS: A Computation Environment for Control Applications of Optimization.- Application of Automatic Differentiation to Optimal Control Problems.- OCCAL: A mixed symbolic-numeric Optimal Control CALculator.- 5 Applications of Optimal Control.- A Robotic Satellite with Simplified Design.- Nonlinear Control under Constraints of a Biological System.- An Object-Oriented Approach to Optimally Describe and Specify a SCADA System Applied to a Power Network.- Near-Optimal Flight Trajectories Generated by Neural Networks.- Performance of a Feedback Method with Respect to Changes in the Air-Density during the Ascent of a Two-Stage-To-Orbit Vehicle.- Linear Optimal Control for Reentry Flight.- Steady-State Modelling of Turbine Engine with Controllers.- Shortest Paths for Satellite Mounted Robot Manipulators.- Optimal Control of the Industrial Robot Manutec r3.

Accurate solution of differential-algebraic optimization problems