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Mathematics - Probability Theory and Stochastic Processes | Markov Decision Processes with Applications to Finance

Markov Decision Processes with Applications to Finance

Series: Universitext

Bäuerle, Nicole, Rieder, Ulrich

2011, XVI, 388p. 24 illus..

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  • Contains various applications with a particular view towards finance/insurance
  • Avoids many technical (e.g. measure theoretic) problems
  • The collection of topics is unique
  • Approach is problem-oriented and illustrated by many examples

The theory of Markov decision processes focuses on controlled Markov chains in discrete time. The authors establish the theory for general state and action spaces and at the same time show its application by means of numerous examples, mostly taken from the fields of finance and operations research. By using a structural approach many technicalities (concerning measure theory) are avoided. They cover problems with finite and infinite horizons, as well as partially observable Markov decision processes, piecewise deterministic Markov decision processes and stopping problems.

The book presents Markov decision processes in action and includes various state-of-the-art applications with a particular view towards finance. It is useful for upper-level undergraduates, Master's students and researchers  in both applied probability and finance, and provides exercises (without solutions).

 

Content Level » Graduate

Keywords » 90C40, 93E20, 60J05, 91G10, 93E35, 60G40 - Markov Decision Processes - Partially Observable Markov Decision Processes - Portfolio optimization - Stochastic dynamic programming

Related subjects » Applications - Probability Theory and Stochastic Processes - Quantitative Finance

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