In today's global economy, supply chains are an essential ingredient to corporate survival and growth. Operations strategy in supply chains must assume an ever-expanding and strategic role of risks that modern enterprises face when they operate in an interdependent supply chain environment. These operational and strategic facets entail a brand new set of operational problems and risks that have not always been understood or managed very well. It falls to supply chain managers to identify and to educate corporate managers on what these critical operational problems and risks involve. This book provides business students and practitioners with the means to understand, to model and to analyze these outstanding issues and problems that are the essential elements in managing supply chains today. This book will consider these problems in depth and draw essential conclusions regarding their management in supply chains. As a textbook treatment, it will examine traditional operational problems, expressing them in a strategic context, understanding their complexity, and recognizing their interdependency with other firms within a supply-chain environment. Used throughout the book will be application examples that illustrate all the aspects of dealing with and solving these kinds of problems. The content of SUPPLY CHAIN GAMES: Operations Management and Risk Valuation is presented in three sections, each of which will emphasize important facets of supply chain management operations. (1) Supply chains and operations modeling and management section will provide static and time models and their gradual extension to a supply chain environment. The section will give special attention tothe new concerns and issues at this level of analysis. (2) Inter-temporal supply chain management section will address this aspect as differential games. The differential games will be presented as natural continuous-time extensions of static models so that the effect of various types of dynamics on supply chains can be assessed and insights can be developed. (3) Risk and supply chain management section will deal with risk and supply chains as well as providing numerous applications regarding the management of interdependent operations and quality in a supply chain environment.

Risk models are models of uncertainty, engineered for some purposes. They are "educated guesses and hypotheses" assessed and valued in terms of well-defined future states and their consequences. They are engineered to predict, to manage countable and accountable futures and to provide a frame of reference within which we may believe that "uncertainty is tamed". Quantitative-statistical tools are used to reconcile our information, experience and other knowledge with hypotheses that both serve as the foundation of risk models and also value and price risk. Risk models are therefore common to most professions, each with its own methods and techniques based on their needs, experience and a wisdom accrued over long periods of time. This book provides a broad and interdisciplinary foundation to engineering risks and to their financial valuation and pricing. Risk models applied in industry and business, heath care, safety, the environment and regulation are used to highlight their variety while financial valuation techniques are used to assess their financial consequences. This book is technically accessible to all readers and students with a basic background in probability and statistics (with 3 chapters devoted to introduce their elements). Principles of risk measurement, valuation and financial pricing as well as the economics of uncertainty are outlined in 5 chapters with numerous examples and applications. New results, extending classical models such as the CCAPM are presented providing insights to assess the risks and their price in an interconnected, dependent and strategic economic environment. In an environment departing from the fundamental assumptions we make regarding financial markets, the book provides a strategic/game-like approach to assess the risk and the opportunities that such an environment implies. To control these risks, a strategic-control approach is developed that recognizes that many risks resulting by "what we do" as well as "what others do". In particular we address the strategic and statistical control of compliance in large financial institutions confronted increasingly with a complex and far more extensive regulation.

Applied Stochastic Models and Control for Finance and Insurance presents at an introductory level some essential stochastic models applied in economics, finance and insurance. Markov chains, random walks, stochastic differential equations and other stochastic processes are used throughout the book and systematically applied to economic and financial applications. In addition, a dynamic programming framework is used to deal with some basic optimization problems. The book begins by introducing problems of economics, finance and insurance which involve time, uncertainty and risk. A number of cases are treated in detail, spanning risk management, volatility, memory, the time structure of preferences, interest rates and yields, etc. The second and third chapters provide an introduction to stochastic models and their application. Stochastic differential equations and stochastic calculus are presented in an intuitive manner, and numerous applications and exercises are used to facilitate their understanding and their use in Chapter 3. A number of other processes which are increasingly used in finance and insurance are introduced in Chapter 4. In the fifth chapter, ARCH and GARCH models are presented and their application to modeling volatility is emphasized. An outline of decision-making procedures is presented in Chapter 6. Furthermore, we also introduce the essentials of stochastic dynamic programming and control, and provide first steps for the student who seeks to apply these techniques. Finally, in Chapter 7, numerical techniques and approximations to stochastic processes are examined. This book can be used in business, economics, financial engineering and decision sciences schools for second year Master's students, as well as in a number of courses widely given in departments of statistics, systems and decision sciences.

This book provides a perspective on a number of financial modelling analytics and risk management. The book begins with extensive outline of GLM estimation techniques combined with the proof of its fundamental results. Applications of static and dynamic models provide a unified approach to the estimation of nonlinear risk models. The book then examines the definition of risks and their management, with particular emphasis on the importance of bi-modal distributions for financial regulation. Chapters also cover the implications of stress testing and the noncyclical CAR (Capital Adequacy Rule). The next section highlights financial modelling analytic approaches and techniques including an overview of memory based financial models, spanning non-memory models, long run and short memory. Applications of these models are used to highlight their variety and their importance to Financial Analytics. Subsequent chapters offer an extensive overview of multi-fractional models and their important applications to Asset price modeling (from Fractional to Multi-fractional Processes), and a look at the binomial pricing model by discussing the effects of memory on the pricing of asset prices. The book concludes with an examination of an algorithmic future perspective to real finance.

The chapters in "Future Perspectives in Risk Models and Finance" are concerned with both theoretical and practical issues. Theoretically, financial risks models are models of certainty, based on information and rules that are both available and agree to by their user. Empirical and data finance however, has provided a bridge between theoretical constructs risks models and the empirical evidence that these models entail. Numerous approaches are then used to model financial risk models, emphasizing mathematical and stochastic models based on the fundamental theoretical tenets of finance and others departing from the fundamental assumptions of finance. The underlying mathematical foundations of these risks models provide a future guideline for risk modeling. Both static and dynamic risk models are then considered. The chapters in this book provide selective insights and developments, that can contribute to a greater understanding the complexity of financial modelling and its ability to bridge financial theories and their practice. Risk models are models of uncertainty, and therefore all risk models are an expression of perceptions, priorities, needs and the information we have. In this sense, all risks models are complex hypotheses we have constructed and based on what we have or believe . Risk models are then challenged by their definition, are risk definition defining in fact prospective risks? By their estimation, what data can we apply to estimate risk processes and how can we do so? How should we use the data and the models at hand for useful and constructive end. "

Originally published in 1977. Management is a dynamic process reflected in three essential functions: management of time, change and people. The book provides a bridging gap between quantitative theories imbedded in the systems approach and managerial decision-making over time and under risk. The conventional wisdom that management is a dynamic process is rendered operational. This title will be of interest to students of business studies and management.

Originally published in 1977. Management is a dynamic process reflected in three essential functions: management of time, change and people. The book provides a bridging gap between quantitative theories imbedded in the systems approach and managerial decision-making over time and under risk. The conventional wisdom that management is a dynamic process is rendered operational. This title will be of interest to students of business studies and management.

This book provides a perspective on a number of approaches to financial modelling and risk management. It examines both theoretical and practical issues. Theoretically, financial risks models are models of a real and a financial "uncertainty", based on both common and private information and economic theories defining the rules that financial markets comply to. Financial models are thus challenged by their definitions and by a changing financial system fueled by globalization, technology growth, complexity, regulation and the many factors that contribute to rendering financial processes to be continuously questioned and re-assessed. The underlying mathematical foundations of financial risks models provide future guidelines for risk modeling. The book's chapters provide selective insights and developments that can contribute to better understand the complexity of financial modelling and its ability to bridge financial theories and their practice. Future Perspectives in Risk Models and Finance begins with an extensive outline by Alain Bensoussan et al. of GLM estimation techniques combined with proofs of fundamental results. Applications to static and dynamic models provide a unified approach to the estimation of nonlinear risk models. A second section is concerned with the definition of risks and their management. In particular, Guegan and Hassani review a number of risk models definition emphasizing the importance of bi-modal distributions for financial regulation. An additional chapter provides a review of stress testing and their implications. Nassim Taleb and Sandis provide an anti-fragility approach based on "skin in the game". To conclude, Raphael Douady discusses the noncyclical CAR (Capital Adequacy Rule) and their effects of aversion of systemic risks. A third section emphasizes analytic financial modelling approaches and techniques. Tapiero and Vallois provide an overview of mathematical systems and their use in financial modeling. These systems span the fundamental Arrow-Debreu framework underlying financial models of complete markets and subsequently, mathematical systems departing from this framework but yet generalizing their approach to dynamic financial models. Explicitly, models based on fractional calculus, on persistence (short memory) and on entropy-based non-extensiveness. Applications of these models are used to define a modeling approach to incomplete financial models and their potential use as a "measure of incompleteness". Subsequently Bianchi and Pianese provide an extensive overview of multi-fractional models and their important applications to Asset price modeling. Finally, Tapiero and Jinquyi consider the binomial pricing model by discussing the effects of memory on the pricing of asset prices.

Risk models are models of uncertainty, engineered for some purposes. They are "educated guesses and hypotheses" assessed and valued in terms of well-defined future states and their consequences. They are engineered to predict, to manage countable and accountable futures and to provide a frame of reference within which we may believe that "uncertainty is tamed". Quantitative-statistical tools are used to reconcile our information, experience and other knowledge with hypotheses that both serve as the foundation of risk models and also value and price risk. Risk models are therefore common to most professions, each with its own methods and techniques based on their needs, experience and a wisdom accrued over long periods of time. This book provides a broad and interdisciplinary foundation to engineering risks and to their financial valuation and pricing. Risk models applied in industry and business, heath care, safety, the environment and regulation are used to highlight their variety while financial valuation techniques are used to assess their financial consequences. This book is technically accessible to all readers and students with a basic background in probability and statistics (with 3 chapters devoted to introduce their elements). Principles of risk measurement, valuation and financial pricing as well as the economics of uncertainty are outlined in 5 chapters with numerous examples and applications. New results, extending classical models such as the CCAPM are presented providing insights to assess the risks and their price in an interconnected, dependent and strategic economic environment. In an environment departing from the fundamental assumptions we make regarding financial markets, the book provides a strategic/game-like approach to assess the risk and the opportunities that such an environment implies. To control these risks, a strategic-control approach is developed that recognizes that many risks resulting by "what we do" as well as "what others do". In particular we address the strategic and statistical control of compliance in large financial institutions confronted increasingly with a complex and far more extensive regulation.

Applied Stochastic Models and Control for Finance and Insurance presents at an introductory level some essential stochastic models applied in economics, finance and insurance. Markov chains, random walks, stochastic differential equations and other stochastic processes are used throughout the book and systematically applied to economic and financial applications. In addition, a dynamic programming framework is used to deal with some basic optimization problems. The book begins by introducing problems of economics, finance and insurance which involve time, uncertainty and risk. A number of cases are treated in detail, spanning risk management, volatility, memory, the time structure of preferences, interest rates and yields, etc. The second and third chapters provide an introduction to stochastic models and their application. Stochastic differential equations and stochastic calculus are presented in an intuitive manner, and numerous applications and exercises are used to facilitate their understanding and their use in Chapter 3. A number of other processes which are increasingly used in finance and insurance are introduced in Chapter 4. In the fifth chapter, ARCH and GARCH models are presented and their application to modeling volatility is emphasized. An outline of decision-making procedures is presented in Chapter 6. Furthermore, we also introduce the essentials of stochastic dynamic programming and control, and provide first steps for the student who seeks to apply these techniques. Finally, in Chapter 7, numerical techniques and approximations to stochastic processes are examined. This book can be used in business, economics, financial engineering and decision sciences schools for second year Master's students, as well as in a number of courses widely given in departments of statistics, systems and decision sciences.

In today's global economy, operations strategy in supply chains must assume an ever-expanding and strategic role of risks. These operational and strategic facets entail a brand new set of operational problems and risks that have not always been understood or managed very well. This book provides the means to understand, to model and to analyze these outstanding issues and problems that are the essential elements in managing supply chains today.

Originally published in 1977. Management is a dynamic process reflected in three essential functions: management of time, change and people. The book provides a bridging gap between quantitative theories imbedded in the systems approach and managerial decision-making over time and under risk. The conventional wisdom that management is a dynamic process is rendered operational. This title will be of interest to students of business studies and management.

Originally published in 1977. Management is a dynamic process reflected in three essential functions: management of time, change and people. The book provides a bridging gap between quantitative theories imbedded in the systems approach and managerial decision-making over time and under risk. The conventional wisdom that management is a dynamic process is rendered operational. This title will be of interest to students of business studies and management.