Overview
- 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
- Includes supplementary material: sn.pub/extras
Part of the book series: Universitext (UTX)
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Table of contents (14 chapters)
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Front Matter
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Finite Horizon Optimization Problems and Financial Markets
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Front Matter
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Finite Horizon Optimization Problems and FinancialMarkets
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Partially Observable Markov Decision Problems
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Front Matter
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Infinite Horizon Optimization Problems
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Front Matter
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Stopping Problems
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Front Matter
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Appendix
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Front Matter
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Keywords
About this book
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).
Reviews
From the reviews:
“This book presents Markov decision processes with general state and action spaces and includes various state-of-the-art applications that stem from finance and operations research. … very helpful, not only for graduate students, but also for researchers working in the field of MDPs and finance. The authors do not focus only on discrete-time MDPs, but provide the description of different classes of Markov models … . Each chapter ends with remarks, where the potential reader may find further hints concerning references.” (Anna Jaskiewicz, Zentralblatt MATH, Vol. 1236, 2012)
Authors and Affiliations
About the authors
Nicole Bäuerle is full professor for Stochastics at the Karlsruhe Institute of Technology. Currently she is in the board of the Fachgruppe Stochastik and the DGVFM (Deutsche Gesellschaft für Versicherungs- und Finanzmathematik). She is editor of the journals "Stochastic Models" and "Mathematical Methods of Operations Research".
Ulrich Rieder is full professor for Optimization and Operations Research at the University of Ulm since 1980. He helped to establish a new program in applied mathematics at Ulm, called Wirtschaftsmathematik. From 1990-2008 he was editor-in-chief of "Mathematical Methods of Operations Research". He is editor of several journals in the areas of operations research and finance.
Bibliographic Information
Book Title: Markov Decision Processes with Applications to Finance
Authors: Nicole Bäuerle, Ulrich Rieder
Series Title: Universitext
DOI: https://doi.org/10.1007/978-3-642-18324-9
Publisher: Springer Berlin, Heidelberg
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer-Verlag GmbH Berlin Heidelberg 2011
Softcover ISBN: 978-3-642-18323-2Published: 08 June 2011
eBook ISBN: 978-3-642-18324-9Published: 06 June 2011
Series ISSN: 0172-5939
Series E-ISSN: 2191-6675
Edition Number: 1
Number of Pages: XVI, 388
Number of Illustrations: 24 b/w illustrations
Topics: Probability Theory and Stochastic Processes, Quantitative Finance, Applications of Mathematics