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)

An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.

Behavioral finance is the study of how psychology affects financial decision making and financial markets. It is increasingly becoming the common way of understanding investor behavior and stock market activity. Incorporating the latest research and theory, Shefrin offers both a strong theory and efficient empirical tools that address derivatives, fixed income securities, mean-variance efficient portfolios, and the market portfolio. The book provides a series of examples to illustrate the theory. The second edition continues the tradition of the first edition by being the one and only book to focus completely on how behavioral finance principles affect asset pricing, now with its theory deepened and enriched by a plethora of research since the first edition

Economic analysis of choice under uncertainty has been dominated by the expected utility (EU) model, yet the EU model has never been without critics. Psychologists accumulated evidence that individual choices under uncertainty were inconsistent with the predictions of the EU model. Applied work in areas such as finance was dominated by the simpler mean-variance analysis. In the 1980s this skepticism was dispelled as a number of generalizations of EU were proposed, most of which were capable of explaining evidence inconsistent with EU, while preserving transitivity and dominance. Generalized expected utility is now a flourishing subfield of economics, with dozens of competing models and considerable literature exploring their theoretical properties and comparing their empirical performance. But the EU model remains the principal tool for the analysis of choice under uncertainty. There is a view that generalized models are too difficult to handle or incapable of generating sharp results. This creates a need to show that the new models can be used in the kinds of economic analysis for which EU has been used, and that they can yield new and interesting results. This book meets this need by describing one of the most popular generalized models -- the rank-dependent expected utility model (RDEU), also known as anticipated utility, EU with rank-dependent preferences, the dual theory of choice under uncertainty, and simply as rank-dependent utility. As the many names indicate, the model has been approached in many ways by many scientists and for this reason, consideration of a single model sheds light on many of the concerns that have motivated the development of generalized utility models. The popularity of the RDEU model rests on its simplicity and tractability. The standard tools of analysis developed for EU theory may be applied to the RDEU model, but since RDEU admits behavior inconsistent with EU, the field of potential applications is widened. As such, the RDEU model is not as much a competitor to EU as an extension based on less restrictive assumptions.

From the perspective of partial differential equations (PDE), this book introduces the Black-Scholes-Merton's option pricing theory. A unified approach is used to model various types of option pricing as PDE problems, to derive pricing formulas as their solutions, and to design efficient algorithms from the numerical calculation of PDEs.

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.