A model-free no-arbitrage price bound for variance options

In the framework of Galichon, Henry-Labordère and Touzi, we consider the model-free no-arbitrage bound of variance option given the marginal distributions of the underlying asset. We first make some approximations which restrict the computation on a bounded domain. Then we propose a gradient projection algorithm together with a finite difference scheme to approximate the bound. The general convergence result is obtained. We also provide a numerical example on the variance swap option.Variance option ; model-free price bound ; gradient projection algorithm.

Monotonicity condition for the θ\theta-scheme for diffusion equations

We derive the necessary and sufficient condition for the $L^{\infty}-$monotonicity of finite difference $\theta$-scheme for a diffusion equation. We confirm that the discretization ratio $\Delta t = O(\Delta x^2)$ is necessary for the monotonicity except for the implicit scheme. In case of the heat equation, we get an explicit formula, which is weaker than the classical CFL condition.

First and second order optimality conditions for optimal control problems of state constrained integral equations

This paper deals with optimal control problems of integral equations, with initial-final and running state constraints. The order of a running state constraint is defined in the setting of integral dynamics, and we work here with constraints of arbitrary high orders. First and second-order necessary conditions of optimality are obtained, as well as second-order sufficient conditions

Optimal control of ordinary differential equations

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Commande optimale

Article pour l'encyclopédie des sciences de l'ingénieurThe optimal control theory analyzes how to optimize dynamical systems with various criteria : reach a target in minimal time or minimal energy, maximize the efficiency of an industrial process for instance. This involves the optimization of both time independent parameters, and the control variables that are function of time. The article analyzes the first and second order optimality conditions, and the ways to solve them, by time discretization, the shooting algorithm, or dynamic programming.L'objet de la commande optimale est l'optimisation de systèmes dynamiques suivant différents objectifs : atteinte d'une cible en temps ou énergie minimale, maximisation du rendement d'un processus industriel par exemple. Pour cela on joue à la fois sur des paramètres indépendants du temps et sur les commandes qui, elles, dépendent du temps. L'article analyse les conditions d'optimalité du premier et second ordre, et leur résolution par discrétisation temporelle, algorithme de tir, ou programmation dynamique

Second order Pontryagin's principle for stochastic control problems

We discuss stochastic optimal control problems whose volatility does not depend on the control, and which have finitely many equality and inequality constraints on the expected value of functions of the final state, as well as control constraints. The main result is a proof of necessity of some second order optimality conditions involving Pontryagin multipliers

Singular arcs in the optimal control of a parabolic equation

International audienceWe present a theory of singular arc, and the corresponding second order necessary and sufficient conditions, for the optimal control of a semilinear parabolic equation with scalar control applied on the r.h.s. We obtain in particular an extension of Kelley's condition, and the characterization of a quadratic growth property for a weak norm.Nous présentons une théorie des arcs singuliers, et les conditions associées nécessaires ou suffisantes du second ordre, pour la commande optimale d'une équation parabolique avec commande scalaire appliquée au membre de droite.On obtient en particulier une extension de la condition de Kelley, et la caractérisation de la croissance quadratique dans une norme faible

First and second order necessary conditions for stochastic optimal control problems

International audienceIn this work we consider a stochastic optimal control problem with either convex control constraints or finitely many equality and inequality constraints over the final state. Using the variational approach, we are able to obtain first and second order expansions for the state and cost function, around a local minimum. This fact allows us to prove general first order necessary condition and, under a geometrical assumption over the constraint set, second order necessary conditions are also established. We end by giving second order optimality conditions for problems with constraints on expectations of the final state