Cargando…

Stochastic differential equations, backward SDEs, partial differential equations

This research monograph presents results to researchers in stochastic calculus, forward and backward stochastic differential equations, connections between diffusion processes and second order partial differential equations (PDEs), and financial mathematics. It pays special attention to the relation...

Descripción completa

Detalles Bibliográficos
Autores principales: Pardoux, Etienne, Rӑşcanu, Aurel
Lenguaje:eng
Publicado: Springer 2014
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-05714-9
http://cds.cern.ch/record/1742601
_version_ 1780942737907908608
author Pardoux, Etienne
Rӑşcanu, Aurel
author_facet Pardoux, Etienne
Rӑşcanu, Aurel
author_sort Pardoux, Etienne
collection CERN
description This research monograph presents results to researchers in stochastic calculus, forward and backward stochastic differential equations, connections between diffusion processes and second order partial differential equations (PDEs), and financial mathematics. It pays special attention to the relations between SDEs/BSDEs and second order PDEs under minimal regularity assumptions, and also extends those results to equations with multivalued coefficients. The authors present in particular the theory of reflected SDEs in the above mentioned framework and include exercises at the end of each chapter. Stochastic calculus and stochastic differential equations (SDEs) were first introduced by K. Itô in the 1940s, in order to construct the path of diffusion processes (which are continuous time Markov processes with continuous trajectories taking their values in a finite dimensional vector space or manifold), which had been studied from a more analytic point of view by Kolmogorov in the 1930s. Since then, this topic has become an important subject of Mathematics and Applied Mathematics, because of its mathematical richness and its importance for applications in many areas of Physics, Biology, Economics and Finance, where random processes play an increasingly important role. One important aspect is the connection between diffusion processes and linear partial differential equations of second order, which is in particular the basis for Monte Carlo numerical methods for linear PDEs. Since the pioneering work of Peng and Pardoux in the early 1990s, a new type of SDEs called backward stochastic differential equations (BSDEs) has emerged. The two main reasons why this new class of equations is important are the connection between BSDEs and semilinear PDEs, and the fact that BSDEs constitute a natural generalization of the famous Black and Scholes model from Mathematical Finance, and thus offer a natural mathematical framework for the formulation of many new models in Finance.
id cern-1742601
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2014
publisher Springer
record_format invenio
spelling cern-17426012021-04-21T20:56:36Zdoi:10.1007/978-3-319-05714-9http://cds.cern.ch/record/1742601engPardoux, EtienneRӑşcanu, AurelStochastic differential equations, backward SDEs, partial differential equationsMathematical Physics and MathematicsThis research monograph presents results to researchers in stochastic calculus, forward and backward stochastic differential equations, connections between diffusion processes and second order partial differential equations (PDEs), and financial mathematics. It pays special attention to the relations between SDEs/BSDEs and second order PDEs under minimal regularity assumptions, and also extends those results to equations with multivalued coefficients. The authors present in particular the theory of reflected SDEs in the above mentioned framework and include exercises at the end of each chapter. Stochastic calculus and stochastic differential equations (SDEs) were first introduced by K. Itô in the 1940s, in order to construct the path of diffusion processes (which are continuous time Markov processes with continuous trajectories taking their values in a finite dimensional vector space or manifold), which had been studied from a more analytic point of view by Kolmogorov in the 1930s. Since then, this topic has become an important subject of Mathematics and Applied Mathematics, because of its mathematical richness and its importance for applications in many areas of Physics, Biology, Economics and Finance, where random processes play an increasingly important role. One important aspect is the connection between diffusion processes and linear partial differential equations of second order, which is in particular the basis for Monte Carlo numerical methods for linear PDEs. Since the pioneering work of Peng and Pardoux in the early 1990s, a new type of SDEs called backward stochastic differential equations (BSDEs) has emerged. The two main reasons why this new class of equations is important are the connection between BSDEs and semilinear PDEs, and the fact that BSDEs constitute a natural generalization of the famous Black and Scholes model from Mathematical Finance, and thus offer a natural mathematical framework for the formulation of many new models in Finance.Springeroai:cds.cern.ch:17426012014
spellingShingle Mathematical Physics and Mathematics
Pardoux, Etienne
Rӑşcanu, Aurel
Stochastic differential equations, backward SDEs, partial differential equations
title Stochastic differential equations, backward SDEs, partial differential equations
title_full Stochastic differential equations, backward SDEs, partial differential equations
title_fullStr Stochastic differential equations, backward SDEs, partial differential equations
title_full_unstemmed Stochastic differential equations, backward SDEs, partial differential equations
title_short Stochastic differential equations, backward SDEs, partial differential equations
title_sort stochastic differential equations, backward sdes, partial differential equations
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-319-05714-9
http://cds.cern.ch/record/1742601
work_keys_str_mv AT pardouxetienne stochasticdifferentialequationsbackwardsdespartialdifferentialequations
AT răscanuaurel stochasticdifferentialequationsbackwardsdespartialdifferentialequations