Paris Dauphine University

About: Paris Dauphine University is a education organization based out in Paris, France. It is known for research contribution in the topics: Population & Approximation algorithm. The organization has 1766 authors who have published 6909 publications receiving 162747 citations. The organization is also known as: Paris Dauphine & Dauphine.

New Public Management et professions dans l’État : au-delà des oppositions, quelles recompositions ?

Abstract: La multiplication de reformes, au sein des administrations, mobilisant des principes et des instruments inspires de la doctrine du New Public Management (NPM), a provoque protestations et mobilisations collectives de la part de nombreux groupes professionnels dans de nombreux secteurs (sante, education, justice, travail social, recherche…). Ces phenomenes font surgir des interrogations sur le devenir des groupes professionnels inscrits dans les services publics en particulier sur la remise en cause de leur autonomie par les reformes NPM. L’opposition entre NPM et professions, pour heuristique qu’elle soit, n’epuise pas l’analyse. Faut-il penser ces changements en termes de declin des professions, de retrecissement des autonomies professionnelles, de mutations des modeles professionnels, de recomposition du professionnalisme, etc. ? Ces questions se situent au carrefour d’une actualite vive — en France et en Europe — et de reflexions sociologiques continues. Elles sont developpees et traitees ici a partir de terrains empiriques, d’echelles d’analyse et d’experiences de recherche variees. Les contributions a ce dossier explorent ainsi les declinaisons des tensions entre NPM et professions.

Stochastic Target Problems with Controlled Loss

Abstract: We consider the problem of finding the minimal initial data of a controlled process which guarantees to reach a controlled target with a given probability of success or, more generally, with a given level of expected loss. By suitably increasing the state space and the controls, we show that this problem can be converted into a stochastic target problem, i.e., finding the minimal initial data of a controlled process which guarantees to reach a controlled target with probability one. Unlike in the existing literature on stochastic target problems, our increased controls are valued in an unbounded set. In this paper, we provide a new derivation of the dynamic programming equation for general stochastic target problems with unbounded controls, together with the appropriate boundary conditions. These results are applied to the problem of quantile hedging in financial mathematics and are shown to recover the explicit solution of Follmer and Leukert [Finance Stoch., 3 (1999), pp. 251-273].

Time Dependent Heston Model

Abstract: The use of the Heston model is still challenging because it has a closed formula only when the parameters are constant [Hes93] or piecewise constant [MN03]. Hence, using a small volatility of volatility expansion and Malliavin calculus techniques, we derive an accurate analytical formula for the price of vanilla options for any time dependent Heston model (the accuracy is less than a few bps for various strikes and maturities). In addition, we establish tight error estimates. The advantage of this approach over Fourier based methods is its rapidity (gain by a factor 100 or more), while maintaining a competitive accuracy. From the approximative formula, we also derive some corollaries related first to equivalent Heston models (extending some work of Piterbarg on stochastic volatility models [Pit05]) and second, to the calibration procedure in terms of ill-posed problems.

Lipschitz Regularity for Elliptic Equations with Random Coefficients

Abstract: We develop a higher regularity theory for general quasilinear elliptic equations and systems in divergence form with random coefficients. The main result is a large-scale L∞-type estimate for the gradient of a solution. The estimate is proved with optimal stochastic integrability under a one-parameter family of mixing assumptions, allowing for very weak mixing with non-integrable correlations to very strong mixing (for example finite range of dependence). We also prove a quenched L2 estimate for the error in homogenization of Dirichlet problems. The approach is based on subadditive arguments which rely on a variational formulation of general quasilinear divergence-form equations.