Titlebook: Optimal Stochastic Control, Stochastic Target Problems, and Backward SDE; Nizar Touzi Book 2013 Springer Science+Business Media New York 2

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Solving Control Problems by Verification,o the unknown value function. Namely, given a smooth solution . of the dynamic programming equation, we give sufficient conditions which allow to conclude that . coincides with the value function .. This is the so-called .. The statement of this result is heavy, but its proof is simple and relies es

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Backward SDEs and Stochastic Control,rol to stochastic target problems. More importantly, the general theory in this chapter will be developed in the non-Markov framework. The Markovian framework of the previous chapters and the corresponding PDEs will be obtained under a specific construction. From this viewpoint, BSDEs can be viewed

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Quadratic Backward SDEs,wth. In the Markovian case, this corresponds to a problem of second-order semilinear PDE with quadratic growth in the gradient term. The first existence and uniqueness result in this context was established by M. Kobylanski in her Ph.D. thesis by adapting some previously established PDE techniques t

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Introduction to Finite Differences Methods,g in quantitative finance. The approach is based on the very powerful and simple framework developed by Barles– Souganidis [4], see the review of the previous chapter. The key property here is the monotonicity which guarantees that the scheme satisfies the same ellipticity condition as the HJB opera

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https://doi.org/10.1007/978-1-4614-4286-8backwards stochastic differential equations; dynamic programming; financial mathematics; stochastic con