Stochastic Dominance
Stochastic Dominance

Author: Haim Levy

Publisher: Springer

Published: 2015-10-31

Total Pages: 517

ISBN-13: 3319217089

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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)

Advances in monitoring the economy
Advances in monitoring the economy

Author: Rene Segers

Publisher: Rozenberg Publishers

Published: 2009

Total Pages: 160

ISBN-13: 9036101042

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Monitoring involves the collection, analysis and evaluation of information over time. For many professionals, monitoring is a central aspect of their work. For example, policy- makers closely watch the e®ects of their current policies to set the right course for reform. Likewise, physicians monitor the well-being of their patients to adjust their treatments when necessary. In business, n̄ancial investors monitor stock prices and interest rates to optimally time their investments, while marketing managers watch their customers' needs and wants to frame their marketing e®orts. The above examples illustrate that monitoring is crucial in many disciplines to make the right decisions at the right moment. For this reason, there has always been a need for improved monitoring methods. With the advent of increasingly powerful computers and advanced analytical techniques, monitoring systems can nowadays process large amounts of information and have become fully automated where desired. A large body of moni- toring methods originate from academics. Especially during the past four decades, many insights from various ēlds such as economics, statistics, psychometrics and econometrics found their way into everyday monitoring practice. With the overwhelming availability of information in some cases, but also the intrinsic lack of information in other cases, the area is continuously faced with new and highly relevant research challenges. The aim of this thesis is to contribute to the development of new monitoring methods by o®ering potential solutions to some of these challenges. The challenges studied in this thesis arise from all three aspects of monitoring, that is from the collection, the analysis as well as from the evaluation of information.

Forecasting Financial Time Series Using Model Averaging
Forecasting Financial Time Series Using Model Averaging

Author: Francesco Ravazzolo

Publisher: Rozenberg Publishers

Published: 2007

Total Pages: 198

ISBN-13: 9051709145

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Believing in a single model may be dangerous, and addressing model uncertainty by averaging different models in making forecasts may be very beneficial. In this thesis we focus on forecasting financial time series using model averaging schemes as a way to produce optimal forecasts. We derive and discuss in simulation exercises and empirical applications model averaging techniques that can reproduce stylized facts of financial time series, such as low predictability and time-varying patterns. We emphasize that model averaging is not a "magic" methodology which solves a priori problems of poorly forecasting. Averaging techniques have an essential requirement: individual models have to fit data. In the first section we provide a general outline of the thesis and its contributions to previ ous research. In Chapter 2 we focus on the use of time varying model weight combinations. In Chapter 3, we extend the analysis in the previous chapter to a new Bayesian averaging scheme that models structural instability carefully. In Chapter 4 we focus on forecasting the term structure of U.S. interest rates. In Chapter 5 we attempt to shed more light on forecasting performance of stochastic day-ahead price models. We examine six stochastic price models to forecast day-ahead prices of the two most active power exchanges in the world: the Nordic Power Exchange and the Amsterdam Power Exchange. Three of these forecasting models include weather forecasts. To sum up, the research finds an increase of forecasting power of financial time series when parameter uncertainty, model uncertainty and optimal decision making are included.