Stochastic Dominance

Investment Decision Making under Uncertainty

Business & Finance, Economics, Microeconomics, Finance & Investing, Finance
Cover of the book Stochastic Dominance by Haim Levy, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Haim Levy ISBN: 9783319217086
Publisher: Springer International Publishing Publication: October 31, 2015
Imprint: Springer Language: English
Author: Haim Levy
ISBN: 9783319217086
Publisher: Springer International Publishing
Publication: October 31, 2015
Imprint: Springer
Language: English

This fully updated third edition is devoted to the analysis of various Stochastic Dominance (SD) decision rules. It discusses the pros and cons of each of the alternate SD rules, the application of these rules to various research areas like statistics, agriculture, medicine, measuring income inequality and the poverty level in various countries, and of course, to investment decision-making under uncertainty. The book features changes and additions to the various chapters, and also includes two completely new chapters. One deals with asymptotic SD and the relation between FSD and the maximum geometric mean (MGM) rule (or the maximum growth portfolio). The other new chapter discusses bivariate SD rules where the individual’s utility is determined not only by his own wealth, but also by his standing relative to his peer group.

Stochastic Dominance: Investment Decision Making under Uncertainty, 3rd Ed. covers the following basic issues: the SD approach, asymptotic SD rules, the mean-variance (MV) approach, as well as the non-expected utility approach. The non-expected utility approach focuses on Regret Theory (RT) and mainly on prospect theory (PT) and its modified version, cumulative prospect theory (CPT) which assumes S-shape preferences. In addition to these issues the book suggests a new stochastic dominance rule called the Markowitz stochastic dominance (MSD) rule corresponding to all reverse-S-shape preferences. It also discusses the concept of the multivariate expected utility and analyzed in more detail the bivariate expected utility case.

From the reviews of the second edition:

"This book is an economics book about stochastic dominance. … is certainly a valuable reference for graduate students interested in decision making under uncertainty. It investigates and compares different approaches and presents many examples. Moreover, empirical studies and experimental results play an important role in this book, which

makes it interesting to read." (Nicole Bäuerle, Mathematical Reviews, Issue 2007 d)

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This fully updated third edition is devoted to the analysis of various Stochastic Dominance (SD) decision rules. It discusses the pros and cons of each of the alternate SD rules, the application of these rules to various research areas like statistics, agriculture, medicine, measuring income inequality and the poverty level in various countries, and of course, to investment decision-making under uncertainty. The book features changes and additions to the various chapters, and also includes two completely new chapters. One deals with asymptotic SD and the relation between FSD and the maximum geometric mean (MGM) rule (or the maximum growth portfolio). The other new chapter discusses bivariate SD rules where the individual’s utility is determined not only by his own wealth, but also by his standing relative to his peer group.

Stochastic Dominance: Investment Decision Making under Uncertainty, 3rd Ed. covers the following basic issues: the SD approach, asymptotic SD rules, the mean-variance (MV) approach, as well as the non-expected utility approach. The non-expected utility approach focuses on Regret Theory (RT) and mainly on prospect theory (PT) and its modified version, cumulative prospect theory (CPT) which assumes S-shape preferences. In addition to these issues the book suggests a new stochastic dominance rule called the Markowitz stochastic dominance (MSD) rule corresponding to all reverse-S-shape preferences. It also discusses the concept of the multivariate expected utility and analyzed in more detail the bivariate expected utility case.

From the reviews of the second edition:

"This book is an economics book about stochastic dominance. … is certainly a valuable reference for graduate students interested in decision making under uncertainty. It investigates and compares different approaches and presents many examples. Moreover, empirical studies and experimental results play an important role in this book, which

makes it interesting to read." (Nicole Bäuerle, Mathematical Reviews, Issue 2007 d)

More books from Springer International Publishing

Cover of the book Logic-Based Program Synthesis and Transformation by Haim Levy
Cover of the book Distributions in the Physical and Engineering Sciences, Volume 3 by Haim Levy
Cover of the book A Project-Based Introduction to Computational Statics by Haim Levy
Cover of the book Vaccine Science and Immunization Guideline by Haim Levy
Cover of the book Private Law, Public Law, Metalaw and Public Policy in Space by Haim Levy
Cover of the book REBT in the Treatment of Subclinical and Clinical Depression by Haim Levy
Cover of the book Knowledge Preservation Through Community of Practice by Haim Levy
Cover of the book Critical Information Infrastructures Security by Haim Levy
Cover of the book Health Information Science by Haim Levy
Cover of the book From Animals to Animats 15 by Haim Levy
Cover of the book Determinism and Free Will by Haim Levy
Cover of the book Creationism and Anti-Creationism in the United States by Haim Levy
Cover of the book Thermo-Hydro-Mechanical-Chemical Processes in Fractured Porous Media: Modelling and Benchmarking by Haim Levy
Cover of the book Knowledge, Learning and Innovation by Haim Levy
Cover of the book Al-Si Alloys Casts by Die Casting by Haim Levy
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy