Markov Decision Processes with Applications to Finance
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 students and researchers both in applied probability and
finance and provides exercises (without solutions).
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)
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)
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.
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Inhaltsangabe
Preface.- 1.Introduction and First Examples.- Part I Finite Horizon Optimization Problems and Financial Markets.- 2.Theory of Finite Horizon Markov Decision Processes.- 3.The Financial Markets.- 4.Financial Optimization Problems.- Part II Partially Observable Markov Decision Problems.- 5.Partially Observable Markov Decision Processes.- 6.Partially Observable Markov Decision Problems in Finance.- Part III Infinite Horizon Optimization Problems.- 7.Theory of Infinite Horizon Markov Decision Processes.- 8.Piecewise Deterministic Markov Decision Processes.- 9.Optimization Problems in Finance and Insurance.- Part IV Stopping Problems.- 10.Theory of Optimal Stopping Problems.- 11.Stopping Problems in Finance.- Part V Appendix.- A.Tools from Analysis.- B.Tools from Probability.- C.Tools from Mathematical Finance.- References.- Index.
