Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

We explore in this chapter questions of existence and uniqueness for solutions to stochastic differential equations and offer a study of their properties. This endeavor is really a study of diffusion processes. Loosely speaking, the term diffusion is attributed to a Markov process which has continuous sample paths and can be characterized in terms of its infinitesimal generator.

Stochastic Differential Equations

This paper poses and solves a new problem of stochastic (nonlinear) disturbance attenuation where the task is to make the system solution bounded by a monotone function of the supremum of the covariance of the noise. This is a natural stochastic counterpart of the problem of input-to-state stabilization in the sense of Sontag (1989). Our development starts with a set of new global stochastic Lyapunov theorems. For an exemplary class of stochastic strict-feedback systems with vanishing nonlinearities, where the equilibrium is preserved in the presence of noise, we develop an adaptive stabilization scheme (based on tuning functions) that requires no a priori knowledge of a bound on the covariance. Next, we introduce a control Lyapunov function formula for stochastic disturbance attenuation. Finally, we address optimality and solve a differential game problem with the control and the noise covariance as opposing players; for strict-feedback systems the resulting Isaacs equation has a closed-form solution.

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Stabilization of stochastic nonlinear systems driven by noise of unknown covariance

Stochastic nonlinear stabilization—I: a backstepping design

Deterministic and Stochastic Optimal Control

Risk-sensitive control problems are considered. Existence of a nonnegative solution to the Bellman equation of risk-sensitive control is shown. The result is applied to prove that no breaking down occurs. Asymptotic behaviour of the nonnegative solution is studied in relation to ergodic control problems and the relationship between the asymptotics and the large deviation principle is noted.

Bellman Equations of Risk-Sensitive Control

We consider an optimal investment problem for a factor model treated by Bielecki and Pliska (Appl. Math. Optim. 39 337–360) as a risk-sensitive stochastic control problem, where the mean returns of individual securities are explicitly affected by economic factors defined as Gaussian processes. We relax the measurability condition assumed as Bielecki and Pliska for the investment strategies to select. Our investment strategies are supposed to be chosen without using information of factor processes but by using only past information of security prices. Then our problem is formulated as a kind of stochastic control problem with partial information. The case on a finite time horizon is discussed by Nagai (Stochastics in Finite and Infinite Dimension 321–340. Birkhauser, Boston). Here we discuss the problem on infinite time horizon.

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Risk-Sinsitive Dynamic Portfolio Optimization with Partial Information on Infinite Time Horizon

We consider a continuous time portfolio optimization problems on an infinite time horizon for a factor model, recently treated by Bielecki and Pliska ["Risk-sensitive dynamic asset management", Appl. Math. Optim. , 39 (1990) 337-360], where the mean returns of individual securities or asset categories are explicitly affected by economic factors. The factors are assumed to be Gaussian processes. We see new features in constructing optimal strategies for risk-sensitive criteria of the portfolio optimization on an infinite time horizon, which are obtained from the solutions of matrix Riccati equations.

Risk-sensitive portfolio optimization on infinite time horizon

We consider constructing optimal strategies for risk-sensitive portfolio optimization problems on an infinite time horizon for general factor models, where the mean returns and the volatilities of individual securities or asset categories are explicitly affected by economic factors. The factors are assumed to be general diffusion processes. In studying the ergodic type Bellman equations of the risk-sensitive portfolio optimization problems, we introduce some auxiliary classical stochastic control problems with the same Bellman equations as the original ones. We show that the optimal diffusion processes of the problem are ergodic and that under some condition related to integrability by the invariant measures of the diffusion processes we can construct optimal strategies for the original problems by using the solution of the Bellman equations.

Optimal Strategies for Risk-Sensitive Portfolio Optimization Problems for General Factor Models

Risk sensitive control problems are considered. Existence of a nonnegative solution to the Bellman equation of risk sensitive control is shown. The result is applied to prove that no breaking down occurs. Asymptotic behaviour of the nonnegative solution is studied in relation to ergodic control problems and the relationship between the asymptotics and the large deviation principle is noted.

Hideo Nagai

Papers

Bellman Equations of Risk-Sensitive Control

Risk-Sinsitive Dynamic Portfolio Optimization with Partial Information on Infinite Time Horizon

Risk-sensitive portfolio optimization on infinite time horizon

Optimal Strategies for Risk-Sensitive Portfolio Optimization Problems for General Factor Models

Bellman equations of risk sensitive control