Stochastic Dominance and Applications to Finance, Risk and Economics

Stochastic Dominance and Applications to Finance, Risk and Economics
Author: Songsak Sriboonchita
Publisher: CRC Press
Total Pages: 456
Release: 2009-10-19
Genre: Business & Economics
ISBN: 1420082671

Drawing from many sources in the literature, Stochastic Dominance and Applications to Finance, Risk and Economics illustrates how stochastic dominance (SD) can be used as a method for risk assessment in decision making. It provides basic background on SD for various areas of applications. Useful Concepts and Techniques for Economics ApplicationsThe

Stochastic Dominance

Stochastic Dominance
Author: Haim Levy
Publisher: Springer
Total Pages: 517
Release: 2015-10-31
Genre: Business & Economics
ISBN: 3319217089

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)

Stochastic Dominance

Stochastic Dominance
Author: Haim Levy
Publisher: Springer Science & Business Media
Total Pages: 439
Release: 2006-08-25
Genre: Business & Economics
ISBN: 0387293116

This book is devoted to investment decision-making under uncertainty. The book covers three basic approaches to this process: the stochastic dominance approach; the mean-variance approach; and the non-expected utility approach, focusing on prospect theory and its modified version, cumulative prospect theory. Each approach is discussed and compared. In addition, this volume examines cases in which stochastic dominance rules coincide with the mean-variance rule and considers how contradictions between these two approaches may occur.

Stochastic Dominance

Stochastic Dominance
Author: G. A. Whitmore
Publisher:
Total Pages: 424
Release: 1978
Genre: Business & Economics
ISBN:

Theoretical foundations of stochastic dominance; Portfolio applications: empirical studies; Portfolio applications: computational aspects; Applications to financial management and capital markets; Applications in economic theory and analysis.

Stochastic Dominance

Stochastic Dominance
Author: Haim Levy
Publisher: Springer
Total Pages: 0
Release: 2010-11-25
Genre: Business & Economics
ISBN: 9781441939838

This book is devoted to investment decision-making under uncertainty. The book covers three basic approaches to this process: the stochastic dominance approach; the mean-variance approach; and the non-expected utility approach, focusing on prospect theory and its modified version, cumulative prospect theory. Each approach is discussed and compared. In addition, this volume examines cases in which stochastic dominance rules coincide with the mean-variance rule and considers how contradictions between these two approaches may occur.

Stochastic Optimization Models in Finance

Stochastic Optimization Models in Finance
Author: William T. Ziemba
Publisher: World Scientific
Total Pages: 756
Release: 2006
Genre: Business & Economics
ISBN: 981256800X

A reprint of one of the classic volumes on portfolio theory and investment, this book has been used by the leading professors at universities such as Stanford, Berkeley, and Carnegie-Mellon. It contains five parts, each with a review of the literature and about 150 pages of computational and review exercises and further in-depth, challenging problems.Frequently referenced and highly usable, the material remains as fresh and relevant for a portfolio theory course as ever.

Stochastic Modeling in Economics and Finance

Stochastic Modeling in Economics and Finance
Author: Jitka Dupacova
Publisher: Springer Science & Business Media
Total Pages: 394
Release: 2005-12-30
Genre: Mathematics
ISBN: 0306481677

In Part I, the fundamentals of financial thinking and elementary mathematical methods of finance are presented. The method of presentation is simple enough to bridge the elements of financial arithmetic and complex models of financial math developed in the later parts. It covers characteristics of cash flows, yield curves, and valuation of securities. Part II is devoted to the allocation of funds and risk management: classics (Markowitz theory of portfolio), capital asset pricing model, arbitrage pricing theory, asset & liability management, value at risk. The method explanation takes into account the computational aspects. Part III explains modeling aspects of multistage stochastic programming on a relatively accessible level. It includes a survey of existing software, links to parametric, multiobjective and dynamic programming, and to probability and statistics. It focuses on scenario-based problems with the problems of scenario generation and output analysis discussed in detail and illustrated within a case study.