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Markov Decision Processes with Applications to Finance

  • Nicole Bäuerle
  • Ulrich Rieder

Part of the Universitext book series (UTX)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Nicole Bäuerle, Ulrich Rieder
    Pages 1-9
  3. Finite Horizon Optimization Problems and Financial Markets

    1. Front Matter
      Pages 11-11
    2. Nicole Bäuerle, Ulrich Rieder
      Pages 13-57
    3. Nicole Bäuerle, Ulrich Rieder
      Pages 59-74
    4. Nicole Bäuerle, Ulrich Rieder
      Pages 75-144
  4. Partially Observable Markov Decision Problems

    1. Front Matter
      Pages 145-145
    2. Nicole Bäuerle, Ulrich Rieder
      Pages 147-174
    3. Nicole Bäuerle, Ulrich Rieder
      Pages 175-189
  5. Infinite Horizon Optimization Problems

    1. Front Matter
      Pages 191-191
    2. Nicole Bäuerle, Ulrich Rieder
      Pages 193-242
    3. Nicole Bäuerle, Ulrich Rieder
      Pages 243-265
    4. Nicole Bäuerle, Ulrich Rieder
      Pages 267-299
  6. Stopping Problems

    1. Front Matter
      Pages 301-301
    2. Nicole Bäuerle, Ulrich Rieder
      Pages 303-330
    3. Nicole Bäuerle, Ulrich Rieder
      Pages 331-343
  7. Appendix

    1. Front Matter
      Pages 345-345
    2. Nicole Bäuerle, Ulrich Rieder
      Pages 347-354
    3. Nicole Bäuerle, Ulrich Rieder
      Pages 355-363
    4. Nicole Bäuerle, Ulrich Rieder
      Pages 365-368
  8. Back Matter
    Pages 369-388

About this book

Introduction

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).

 

Keywords

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

Authors and affiliations

  • Nicole Bäuerle
    • 1
  • Ulrich Rieder
    • 2
  1. 1., Institute for StochasticsKarlsruhe Institute of TechnologyKarlsruheGermany
  2. 2., Institute of Optimization and OperationsUniversity of UlmUlmGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-18324-9
  • Copyright Information Springer-Verlag Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-642-18323-2
  • Online ISBN 978-3-642-18324-9
  • Series Print ISSN 0172-5939
  • Series Online ISSN 2191-6675
  • Buy this book on publisher's site