Stochastic optimal control with random coefficients and associated stochastic Hamilton–Jacobi–Bellman equations

We consider the optimal control problem for stochastic differential equations (SDEs) with random coefficients under the recursive-type objective functional captured by the backward SDE (BSDE). Due to the random coefficients, the associated Hamilton–Jacobi–Bellman (HJB) equation is a class of second-order stochastic PDEs (SPDEs) driven by Brownian motion, which we call the stochastic HJB (SHJB) equation. In addition, as we adopt the recursive-type objective functional, the drift term of the SHJB equation depends on the second component of its solution. These two generalizations cause several technical intricacies, which do not appear in the existing literature. We prove the dynamic programming principle (DPP) for the value function, for which unlike the existing literature we have to use the backward semigroup associated with the recursive-type objective functional. By the DPP, we are able to show the continuity of the value function. Using the Itô–Kunita’s formula, we prove the verification theorem, which constitutes a sufficient condition for optimality and characterizes the value function, provided that the smooth (classical) solution of the SHJB equation exists. In general, the smooth solution of the SHJB equation may not exist. Hence, we study the existence and uniqueness of the solution to the SHJB equation under two different weak solution concepts. First, we show, under appropriate assumptions, the existence and uniqueness of the weak solution via the Sobolev space technique, which requires converting the SHJB equation to a class of backward stochastic evolution equations. The second result is obtained under the notion of viscosity solutions, which is an extension of the classical one to the case for SPDEs. Using the DPP and the estimates of BSDEs, we prove that the value function is the viscosity solution to the SHJB equation. For applications, we consider the linear-quadratic problem, the utility maximization problem, and the European option pricing problem. Specifically, different from the existing literature, each problem is formulated by the generalized recursive-type objective functional and is subject to random coefficients. By applying the theoretical results of this paper, we obtain the explicit optimal solution for each problem in terms of the solution of the corresponding SHJB equation.

Constrained stochastic LQ control on infinite time horizon with regime switching

This paper is concerned with a stochastic linear-quadratic (LQ) optimal control problem on infinite time horizon, with regime switching, random coefficients, and cone control constraint. To tackle the problem, two new extended stochastic Riccati equations (ESREs) on infinite time horizon are introduced. The existence of the nonnegative solutions, in both standard and singular cases, is proved through a sequence of ESREs on finite time horizon. Based on this result and some approximation techniques, we obtain the optimal state feedback control and optimal value for the stochastic LQ problem explicitly. Finally, we apply these results to solve a lifetime portfolio selection problem of tracking a given wealth level with regime switching and portfolio constraint.

An Exponential Endogenous Switching Regression with Correlated Random Coefficients

This paper presents a method for estimating the average treatment effects (ATE) of an exponential endogenous switching model where the coefficients of covariates in the structural equation are random and correlated with the binary treatment variable. The estimating equations are derived under some mild identifying assumptions. We find that the ATE is identified, although each coefficient in the structural model may not be. Tests assessing the endogeneity of treatment and for model selection are provided. Monte Carlo simulations show that, in large samples, the proposed estimator has a smaller bias and a larger variance than the methods that do not take the random coefficients into account. This is applied to health insurance data of Oregon.

Longitudinal Changes in Swiss Adolescent’s Mental Health Outcomes from before and during the COVID-19 Pandemic

This study aimed to explore changes in mental health outcomes (depression, anxiety, home, and school stress) from before the first COVID-19 wave (autumn 2019) to the later stages of the same wave (autumn 2020) in a sample of N = 377 Swiss adolescents (Mage = 12.67; 47% female). It also examined whether students’ background characteristics (gender, immigrant status, and socio-economic status) and reported COVID-19 burden predicted students’ outcomes and their intra-individual changes. Student’s mental health, background characteristics, and reported COVID-19 burden were assessed by a self-report questionnaire. The intra-individual changes in students’ scores were estimated using random coefficients regression analyses, with time points nested in individuals. To examine the effects of predictors (students’ background characteristics and the reported COVID-19 burden) on outcome scores and changes, multilevel intercepts-and-slopes-as-outcomes models were used. The results showed that the expected impact of the pandemic on mental health was not noticeable in the later stages of the first COVID-19 wave. Only two effects were demonstrated in terms of intra-individual changes, namely, an effect of gender on depression and anxiety symptoms and an effect of reported COVID-19 burden on school stress symptoms. Moreover, few associations were found for selected predictors and students’ mean level scores, averaged across both time points.

Indefinite Linear-Quadratic Stochastic Control Problem for Jump-Diffusion Models with Random Coefficients: A Completion of Squares Approach

In this paper, we study the indefinite linear-quadratic (LQ) stochastic optimal control problem for stochastic differential equations (SDEs) with jump diffusions and random coefficients driven by both the Brownian motion and the (compensated) Poisson process. In our problem setup, the coefficients in the SDE and the objective functional are allowed to be random, and the jump-diffusion part of the SDE depends on the state and control variables. Moreover, the cost parameters in the objective functional need not be (positive) definite matrices. Although the solution to this problem can also be obtained through the stochastic maximum principle or the dynamic programming principle, our approach is simple and direct. In particular, by using the Itô-Wentzell’s formula, together with the integro-type stochastic Riccati differential equation (ISRDE) and the backward SDE (BSDE) with jump diffusions, we obtain the equivalent objective functional that is quadratic in control u under the positive definiteness condition, where the approach is known as the completion of squares method. Then the explicit optimal solution, which is linear in state characterized by the ISRDE and the BSDE jump diffusions, and the associated optimal cost are derived by eliminating the quadratic term of u in the equivalent objective functional. We also verify the optimality of the proposed solution via the verification theorem, which requires solving the stochastic HJB equation, a class of stochastic partial differential equations with jump diffusions.

Numerical modeling of boundary value problems for differential equations with random coefficients

This paper deals with the numerical modeling of differential equations with coefficients in the form of random fields. Using the Karhunen-Lo´eve expansion, we approximate these coefficients as a sum of independent random variables and real functions. This allows us to use the computational probabilistic analysis. In particular, we apply the technique of probabilistic extensions to construct the probability density functions of the processes under study. As a result, we present a comparison of our approach with Monte Carlo method in terms of the number of operations and demonstrate the results of numerical experiments for boundary value problems for differential equations of the elliptic type.

Endogenous Vertical Differentiation, Variety, and the Unequal Gains from Trade

How unequal are the gains from trade? This paper develops a structural framework to quantify the consequences of international trade on welfare of consumers across the income distribution, allowing for non-homothetic demand and endogenous quality choices by firms. Using random coefficients demand estimation techniques, I infer demand and supply parameters, as well as household-specific price indexes for more than 3,000 distinct industries and find the gains from trade to be moderately unequal except in wealthier and small economies. Further, not accounting for endogenous vertical differentiation would overstate the impact of trade on cost-of-living inequality by close to 50%.