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© 2011

Markov Decision Processes with Applications to Finance

Benefits

  • 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

Textbook

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

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

  1. 1., Institute for StochasticsKarlsruhe Institute of TechnologyKarlsruheGermany
  2. 2., Institute of Optimization and OperationsUniversity of UlmUlmGermany

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

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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)