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This volume offers the reader practical methods to compute the option prices in the incomplete asset markets. The [GLP & MEMM] pricing models are clearly introduced, and the properties of these models are discussed in great detail. It is shown that the geometric L(r)vy process (GLP) is a typical example of the incomplete market, and that the MEMM (minimal entropy martingale measure) is an extremely powerful pricing measure. This volume also presents the calibration procedure of the [GLP \& MEMM] model that has been widely used in the application of practical problem
This book is devoted to establishing a completely new concept within economics referred to as "trading economics" which is a reconstructed economic system in theory that seeks perfect harmony between micro and macro elements in a structured way, hence making the economic theory a rigorous system supported by internal logical continuity. Representing a revolution of the existing theoretical framework, trading economics has changed the logic of mainstream economics. Specifically, it deduces the "macro whole" from the "micro individuals", and it introduces a systematic and comprehensive analysis approach. It stresses that within an interconnected world, the interaction between trading agents is the fundamental driving force behind the operation, development and evolution of the economic system.
How can we use market knowledge effectively? What needs to be done to move from market knowledge to market insight? These and other questions of significance to marketers, researchers, and scholars alike are addressed in this timely volume. Drawing on a collection of outstanding papers from the prestigious Marketing Science Institute, Editor Rohit Desphande, has assembled, in a single source, the key research on market knowledge management and the best information available for new ideas on what's next. The contributing authors are scholars from leading business schools including Harvard, MIT, and Wharton. Using Market Knowledge is appropriate for students in advanced marketing courses, scholars and faculty interested in improving their understanding of knowledge management, and professionals in market research firms.
Text introducing a new empirically-based model of financial market dynamics.
Stochastic portfolio theory is a mathematical methodology for constructing stock portfolios and for analyzing the effects induced on the behavior of these portfolios by changes in the distribution of capital in the market. Stochastic portfolio theory has both theoretical and practical applications: as a theoretical tool it can be used to construct examples of theoretical portfolios with specified characteristics and to determine the distributional component of portfolio return. This book is an introduction to stochastic portfolio theory for investment professionals and for students of mathematical finance. Each chapter includes a number of problems of varying levels of difficulty and a brief summary of the principal results of the chapter, without proofs.
Mathematics in Science and Engineering, Volume 20, Adaptive Processes in Economic Systems demonstrates the usefulness of communications theory, self-adaptive control theory, and thermodynamic theory to certain economic processes. This book discusses the common properties of adaptive processes, role of the decision maker, and mixed adaptive processes of the first and second kind. The economic environmental processes, concept of entropy time, and stochastic dynamic economic process are also elaborated. This text likewise covers the investment model with full liquidity, adaptive capital allocation process, and concept of an economic state space. Other topics include the stochastic equilibrium in the market and individual adaptive behavior. This volume is suitable for engineers, economists, and specialists of disciplines related to economic systems.
Written by one of the leading authorities in market microstructure research, this book provides a comprehensive guide to the theoretical work in this important area of finance.
Build, test, and tune financial, insurance or other market trading systems using C++ algorithms and statistics. You’ve had an idea and have done some preliminary experiments, and it looks promising. Where do you go from here? Well, this book discusses and dissects this case study approach. Seemingly good backtest performance isn't enough to justify trading real money. You need to perform rigorous statistical tests of the system's validity. Then, if basic tests confirm the quality of your idea, you need to tune your system, not just for best performance, but also for robust behavior in the face of inevitable market changes. Next, you need to quantify its expected future behavior, assessing how bad its real-life performance might actually be, and whether you can live with that. Finally, you need to find its theoretical performance limits so you know if its actual trades conform to this theoretical expectation, enabling you to dump the system if it does not live up to expectations. This book does not contain any sure-fire, guaranteed-riches trading systems. Those are a dime a dozen... But if you have a trading system, this book will provide you with a set of tools that will help you evaluate the potential value of your system, tweak it to improve its profitability, and monitor its on-going performance to detect deterioration before it fails catastrophically. Any serious market trader would do well to employ the methods described in this book. What You Will Learn See how the 'spaghetti-on-the-wall' approach to trading system development can be done legitimatelyDetect overfitting early in developmentEstimate the probability that your system's backtest results could have been due to just good luckRegularize a predictive model so it automatically selects an optimal subset of indicator candidatesRapidly find the global optimum for any type of parameterized trading systemAssess the ruggedness of your trading system against market changesEnhance the stationarity and information content of your proprietary indicatorsNest one layer of walkforward analysis inside another layer to account for selection bias in complex trading systemsCompute a lower bound on your system's mean future performanceBound expected periodic returns to detect on-going system deterioration before it becomes severeEstimate the probability of catastrophic drawdown Who This Book Is For Experienced C++ programmers, developers, and software engineers. Prior experience with rigorous statistical procedures to evaluate and maximize the quality of systems is recommended as well.
International management and cultural diversity -- Sustainable development and business sustainability -- University governance and management -- Knowledge-based organization, intellectual capital, information and management documents -- Entrepreneurship, social enterprise and smes -- Leadership and human resources management -- Management of change, innovation and quality.
In economics agents are assumed to choose on the basis of rational calculations aimed at the maximization of their pleasure or profit. Formally, agents are said to manifest transitive and consistent preferences in attempting to maximize their utility in the presence of several constraints. They operate according to the choice imperative: given a set of alternatives, choose the best. This imperative works well in a static and simplistic framework, but it may fail or vary when 'the best' is changing continuously. This approach has been questioned by a descriptive approach that springing from the complexity theory tries to give a scientific basis to the way in which individuals really choose, showing that those models of human nature is routinely falsified by experiments since people are neither selfish nor rational. Thus inductive rules of thumb are usually implemented in order to make decisions in the presence of incomplete and heterogeneous information sets.