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Sequential Binary Investment Decisions

A Bayesian Approach

  • Book
  • © 1988

Overview

Authors:
  1. Werner Jammernegg

    1. Institut für Statistik, Ökonometrie und Operations Research, Karl-Franzens-Universität Graz, Graz, Austria

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Part of the book series: Lecture Notes in Economics and Mathematical Systems (LNE, volume 313)

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Table of contents (5 chapters)

  1. Front Matter

    Pages I-VI
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  2. Introduction

    • Werner Jammernegg
    Pages 1-9

  3. The Monotonicity of Transition Probabilities

    • Werner Jammernegg
    Pages 10-23

  4. Dynamic Portfolio Models under Uncertainty

    • Werner Jammernegg
    Pages 24-83

  5. The Optimal Timing of Investment

    • Werner Jammernegg
    Pages 84-146

  6. Concluding Remarks

    • Werner Jammernegg
    Pages 147-147

  7. Back Matter

    Pages 148-159
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Keywords

About this book

This book describes some models from the theory of investment which are mainly characterized by three features. Firstly, the decision-maker acts in a dynamic environment. Secondly, the distributions of the random variables are only incompletely known at the beginning of the planning process. This is termed as decision-making under conditions of uncer­ tainty. Thirdly, in large parts of the work we restrict the analysis to binary decision models. In a binary model, the decision-maker must choose one of two actions. For example, one decision means to undertake the invest­ ·ment project in a planning period, whereas the other decision prescribes to postpone the project for at least one more period. The analysis of dynamic decision models under conditions of uncertainty is not a very common approach in economics. In this framework the op­ timal decisions are only obtained by the extensive use of methods from operations research and from statistics. It is the intention to narrow some of the existing gaps in the fields of investment and portfolio analysis in this respect. This is done by combining techniques that have been devel­ oped in investment theory and portfolio selection, in stochastic dynamic programming, and in Bayesian statistics. The latter field indicates the use of Bayes' theorem for the revision of the probability distributions of the random variables over time.

Authors and Affiliations

  • Institut für Statistik, Ökonometrie und Operations Research, Karl-Franzens-Universität Graz, Graz, Austria

    Werner Jammernegg

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