Approximation par des intégrales de Stieltjes-Lebesgue d’intégrales stochastiques relatives au mouvement brownien indexé par
Approximation par un processus de diffusion de files GI/GI/1 avec dépendance de l'état
Approximation scheme for solutions of backward stochastic differential equations via the representation theorem
We are interested in the approximation and simulation of solutions for the backward stochastic differential equations. We suggest two approximation schemes, and we study the induced error.
Approximation theorems of Wong-Zakai type for stochastic differential equations in infinite dimensions [Book]
Approximations et majorations optimales des statistiques d'ordre d'un échantillon
Approximations for the maximum of stochastic processes with drift
If a stochastic process can be approximated with a Wiener process with positive drift, then its maximum also can be approximated with a Wiener process with positive drift.
Approximations of believability functions under incomplete identification of sets of compatible states
Approximations of Positive Contractions on Loo. II.
Approximations of the brownian rough path with applications to stochastic analysis
Approximations to mild solutions of stochastic semilinear equations.
Approximations to mild solutions of stochastic semilinear equations with non-Lipschitz coefficients
In the present paper, using a Picard type method of approximation, we investigate the global existence of mild solutions for a class of Ito type stochastic differential equations whose coefficients satisfy conditions more general than the Lipschitz and linear growth ones.
Approximative solutions of stochastic optimization problems
The aim of this paper is to present some ideas how to relax the notion of the optimal solution of the stochastic optimization problem. In the deterministic case, -minimal solutions and level-minimal solutions are considered as desired relaxations. We call them approximative solutions and we introduce some possibilities how to combine them with randomness. Relations among random versions of approximative solutions and their consistency are presented in this paper. No measurability is assumed, therefore,...
Aproximación aleatoria de cuerpos convexos.
Problems related to the random approximation of convex bodies fall into the field of integral geometry and geometric probabilities. The aim of this paper is to give a survey of known results about the stochastic model that has received special attention in the literature and that can be described as follows:Let K be a d-dimensional convex body in Eucliden space Rd, d ≥ 2. Denote by Hn the convex hull of n independent random points X1, ..., Xn distributed identically and uniformly in the interior...
Arbitrage et lois de martingale
Arbitrage for simple strategies
Various aspects of arbitrage on finite horizon continuous time markets using simple strategies consisting of a finite number of transactions are studied. Special attention is devoted to transactions without shortselling, in which we are not allowed to borrow assets. The markets without or with proportional transaction costs are considered. Necessary and sufficient conditions for absence of arbitrage are shown.
Arbitrage in markets without shortselling with proportional transaction costs
We consider markets with proportional transaction costs and shortsale restrictions. We give necessary and sufficient conditions for the absence of arbitrage and also estimate the super-replication price.
Arbres et processus de Bellman-Harris
Arbres et processus de Galton-Watson
Are continuous mappings preserving normality necessarily linear?
An example of a normal nonlinear continuous function of a normal random variable is given. Also the Cauchy case is considered.
Are law-invariant risk functions concave on distributions?
While it is reasonable to assume that convex combinations on the level of random variables lead to a reduction of risk (diversification effect), this is no more true on the level of distributions. In the latter case, taking convex combinations corresponds to adding a risk factor. Hence, whereas asking for convexity of risk functions defined on random variables makes sense, convexity is not a good property to require on risk functions defined on distributions. In this paper we study the interplay...